WO2018006454A1 - 一种无人机的飞行路径规划、控制方法及系统 - Google Patents

一种无人机的飞行路径规划、控制方法及系统 Download PDF

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Publication number
WO2018006454A1
WO2018006454A1 PCT/CN2016/092398 CN2016092398W WO2018006454A1 WO 2018006454 A1 WO2018006454 A1 WO 2018006454A1 CN 2016092398 W CN2016092398 W CN 2016092398W WO 2018006454 A1 WO2018006454 A1 WO 2018006454A1
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Prior art keywords
point
drone
flight
area
maintenance
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PCT/CN2016/092398
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English (en)
French (fr)
Inventor
李泽飞
徐节文
王铭熙
陈汉平
周琦
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深圳市大疆创新科技有限公司
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Priority to CN201680002372.5A priority Critical patent/CN107924188A/zh
Publication of WO2018006454A1 publication Critical patent/WO2018006454A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/0202—Control of position or course in two dimensions specially adapted to aircraft
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C1/00—Fuselages; Constructional features common to fuselages, wings, stabilising surfaces or the like
    • B64C1/16—Fuselages; Constructional features common to fuselages, wings, stabilising surfaces or the like specially adapted for mounting power plant
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C39/00—Aircraft not otherwise provided for
    • B64C39/02—Aircraft not otherwise provided for characterised by special use
    • B64C39/024—Aircraft not otherwise provided for characterised by special use of the remote controlled vehicle type, i.e. RPV
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
    • B64D1/00—Dropping, ejecting, releasing, or receiving articles, liquids, or the like, in flight
    • B64D1/16—Dropping or releasing powdered, liquid, or gaseous matter, e.g. for fire-fighting
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C23/00—Combined instruments indicating more than one navigational value, e.g. for aircraft; Combined measuring devices for measuring two or more variables of movement, e.g. distance, speed or acceleration
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/003—Flight plan management
    • G08G5/0034—Assembly of a flight plan
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/003—Flight plan management
    • G08G5/0039—Modification of a flight plan
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/0056—Navigation or guidance aids for a single aircraft in an emergency situation, e.g. hijacking
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/006—Navigation or guidance aids for a single aircraft in accordance with predefined flight zones, e.g. to avoid prohibited zones
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0047—Navigation or guidance aids for a single aircraft
    • G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0073—Surveillance aids
    • G08G5/0086—Surveillance aids for monitoring terrain
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/02—Automatic approach or landing aids, i.e. systems in which flight data of incoming planes are processed to provide landing data
    • G08G5/025—Navigation or guidance aids
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00—Type of UAV
    • B64U10/10—Rotorcrafts
    • B64U10/13—Flying platforms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/60—UAVs specially adapted for particular uses or applications for transporting passengers; for transporting goods other than weapons
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B64U2201/10—UAVs characterised by their flight controls autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U50/00—Propulsion; Power supply
    • B64U50/30—Supply or distribution of electrical power
    • B64U50/37—Charging when not in flight

Definitions

  • the present application relates to the field of drones, and in particular, to a flight path planning method and system, control method and system for a drone.
  • the drone referred to as the "unmanned aerial vehicle” is a non-manned aircraft operated by radio remote control equipment and its own program control device.
  • the drone can be used for the protection of agricultural and forestry plants, and has the advantages of safety, high efficiency and resource saving.
  • the drone is used for the protection of agricultural and forestry plants, and can be remotely controlled by the operator, but the effect is not satisfactory, and an automatic operation is proposed for this purpose.
  • the automatic operation technology of the drone is not perfect. Generally, it can only be applied to simple terrain, and the working area of complex terrain, such as irregular terrain mixed with bumps, complicated obstacles in the work area, etc., cannot be effectively processed. In addition, the drone's battery life is shorter, which greatly limits the application of the drone.
  • the present invention provides a flight path planning method for a drone, the method comprising: acquiring geographic information of a preset flight area of the drone; and dividing the preset flight area according to the geographic information. Work areas; determine the flight path of the drone within the preset flight area based on the plurality of work areas.
  • the geographic information includes coordinate information for indicating a boundary of the preset flight area, wherein the boundary includes at least a first boundary for indicating an outer contour of the preset flight area.
  • the boundary further includes a second boundary for indicating an outer contour of the obstacle region within the preset flight region.
  • the step of acquiring geographic information of a preset flight area of the drone includes: obtaining for representation The latitude and longitude coordinates of the boundary; convert the latitude and longitude coordinates into two-dimensional coordinates.
  • the step of converting the latitude and longitude coordinates into two-dimensional coordinates includes: converting the latitude and longitude coordinates into three-dimensional coordinates in the geocentric coordinate system; and converting the three-dimensional coordinates into two-dimensional coordinates in a plane coordinate system tangent to the surface of the earth.
  • the step of dividing the preset flight area into the plurality of work areas according to the geographic information includes: obtaining, according to the position information of the boundary and the working width of the drone, the flight direction parallel to the drone in the preset flight area.
  • the route segment; according to the intersection of the route segment and the boundary, the route segment is divided into multiple work regions.
  • the spacing between two adjacent route segments is equal to the working width of the drone, and the endpoints of each route segment are located on the boundary.
  • the flight direction of the drone is determined according to the wind direction of the preset flight area.
  • the step of dividing the route segment into the plurality of work regions comprises: obtaining a tangent point of a line parallel to the flight direction of the drone and the boundary; dividing the boundary into multiple according to the tangent point Edge segments; route segments with endpoints on both sides that are on the same edge segment are divided into the same work area.
  • the step of determining a flight path of the drone in the preset flight area according to the plurality of work areas includes: determining a port point that can be used as an entry point or an exit point of each work area; determining, according to the port point of the work area, The flight path of the man-machine.
  • the step of determining a port point that can be used as an entry point or an exit point of each work area includes: using two end points of the most outer route segment of the same work area as port points.
  • the step of determining a flight path of the drone according to the port point of the work area includes: calculating an unconventional consumption of the plurality of candidate connection paths traversing all the work areas according to the start point and the port point of the drone, wherein the unconventional consumption is at least It includes the distance consumption between different work areas and the travel cost from the starting point to the work area; and selects one candidate connection path with the least conventional consumption as the flight path of the drone.
  • the step of unconventional consumption of the path includes: determining a candidate work area connection mode between the work areas; determining a port point between the start point of the drone and the work area and the port point of the work area according to the candidate work area connection manner; The candidate port connection mode; the unconventional consumption is calculated according to the candidate port connection mode.
  • the determining the candidate work area connection manner between the work areas comprises: determining the candidate work area connection manner by the arrangement combination or according to the adjacency relationship between the work areas, wherein the candidate work area is determined according to the adjacency relationship between the work areas
  • the connection method includes determining a plurality of candidate job area connection manners for traversing all job areas and connecting the non-contiguous job areas to a minimum.
  • the step of determining the candidate port connection manner between the start point of the drone and the port point of the work area and the port point of the segment according to the candidate work area connection manner includes: for each candidate work area connection manner, The candidate entry point and the candidate exit point of the work area are determined according to the port point of each work area and the number of route segments included in the work area; the candidate port connection manner is determined according to the candidate entry point and the candidate exit point of the work area.
  • the step of determining the candidate entry point and the candidate exit point of the work area according to the port point of each work area and the number of route segments included in the work area includes: for the first work area Selecting the port point closest to the starting point as the candidate entry point of the first work area, and determining the first work area according to the number of the route segments included in the first work area and the candidate entry points of the first work area Candidate exit point; for the remaining work area, select the port point closest to the candidate exit point of the previous work area of the current work area as the candidate entry point of the current work area, and according to the number of route segments included in the current work area and the current work area
  • the candidate entry point determines the candidate exit point for the current work area.
  • the step of calculating the unconventional consumption according to the candidate port connection manner includes: connecting the candidate entry point of each work area with the candidate exit point of the previous work area or the drone The line segment of the starting point intersects the edge of the obstacle area; if the intersection is empty, the distance consumed along the line segment is consumed as the distance between the two working areas or between the working area and the starting point of the drone.
  • the travel consumption generated by the obstacle avoidance area includes: the travel consumption caused by bypassing the obstacle area by the bypass method or the travel consumption generated by crossing or crossing the obstacle area by raising or lowering.
  • the unconventional consumption further includes the travel consumption of the return flight maintenance; calculating the unconventional consumption of the plurality of candidate connection paths traversing all the work areas according to the start point and the port point of the drone further includes: according to the power source of the drone and/or Or the state calculation of the job source should be performed for the return point of the return flight maintenance; the travel cost of the return maintenance is calculated according to the coordinates of the return point and the coordinates of the maintenance point.
  • the step of calculating the return point that should be returned to the maintenance according to the working state of the drone includes: sequentially determining whether the current end point of the line segment satisfies the endurance condition, and if the endurance condition is not satisfied, using the current end point as the return point.
  • the step of calculating the return point that should be returned to the maintenance according to the working state of the drone includes: sequentially determining whether the current end point of the line segment satisfies the endurance condition; if the endurance condition is not satisfied, determining whether the current end point and the next end point are For the route segment to be operated; if yes, find the return point on the route segment between the current endpoint and the next endpoint, so that the drone can safely return to the maintenance point after flying to the return point, and if not, the current The endpoint acts as a return point.
  • the endurance condition refers to the remaining amount of the power source and/or the working source of the drone flying to the current end point minus the preset safety amount being greater than or equal to flying from the current end point to the next end point and flying back and forth from the next end point.
  • Point power source and / or source consumption are examples of Point power source and / or source consumption.
  • the step of calculating the travel cost of the return maintenance according to the coordinates of the return point and the coordinates of the maintenance point includes: calculating the maintenance device or the person according to the remaining flight time of the drone, the movement path of the maintenance device or the person, the current position, and the movement speed.
  • the maximum range of motion of the drone during the remaining flight time; the maintenance point is specified within the maximum range of motion.
  • the steps of specifying the maintenance point within the maximum range of motion include: returning the maximum range of motion
  • the point at which the power source and/or the work source between the waypoints or the next endpoint consumes the least or the closest flight distance is used as a maintenance point.
  • the method further includes: acquiring coordinates of a special edge point for indicating an edge of the special work area in the preset flight area; calculating an intersection of the route segment and the edge of the special work area; and intersecting the edge of the route segment with the edge of the special work area
  • the flight path is inserted so that the operation performed by the drone in the special work area is different from the operation performed by the drone outside the special work area.
  • the special work area includes at least one of a flightable non-work area, a high-altitude flight area, and a low-altitude flight area.
  • the method further includes: forming a coordinate sequence of the plurality of task points for the flight path of the drone, so that the drone performs the work between the plurality of task points according to the coordinate sequence, wherein the task point includes at least the end point of the route segment The intersection of the route segment and the edge of the special work area.
  • the present invention provides a control method for a drone, the method comprising: performing a flight operation in a preset flight area according to a preset flight path; adjusting a flight path of the drone and/or performing The operation is to adapt the drone to the current operating environment or working state.
  • adjusting the flight path of the drone includes at least one of: changing the flight direction, changing the flight height, and stopping the flight; and adjusting the operation performed by the drone includes any one of the following: stopping the operation and starting the operation.
  • the working environment includes at least one of the following: an obstacle in front, a non-working area in front, and a working area in front; and the working state includes at least one of the following: insufficient power source, insufficient working source, lost navigation signal, and received control. command.
  • adjusting the flight path of the drone includes changing the flight direction to fly to the maintenance point; adjusting the flight path of the drone and/or performing the operation to make the unmanned
  • the step of adapting the machine to the current working environment or working state includes: obtaining the state of the power source and/or the working source of the drone; calculating the returning point that should be returned for maintenance according to the power source and/or the working source state of the drone; Change the direction of flight when the drone flies to the return point to fly to the maintenance point for maintenance.
  • the steps of obtaining the state of the power source and/or the working source of the drone include:
  • the step of calculating the return point to be returned for maintenance according to the state of the power source and/or the source of the drone includes: determining whether the state of the power source and/or the source of the drone meets the endurance condition; For the endurance condition, the current position of the drone is used as the return point.
  • the step of calculating the return point to be returned for maintenance according to the state of the power source and/or the source of the drone includes: determining whether the state of the power source and/or the source of the drone meets the endurance condition; The endurance condition determines whether the current position of the drone and the next task point of the task point corresponding to the current position of the drone are the route segments to be operated; if yes, the current position of the drone and the next The return point is found on the route segment between the mission points so that the drone can safely return to the maintenance point after flying to the return point. If not, the current position of the drone is used as the return point.
  • the endurance condition refers to the remaining amount of the power source and/or the working source of the drone minus the preset safety amount greater than or equal to the task point corresponding to the current position of the drone from the current position of the drone to the drone The power source and/or source of operation of the next mission point.
  • the method further includes: estimating the remaining flight time of the drone according to the state of the power source and/or the working source of the drone; calculating the maintenance device or the personnel according to the current position, the moving path, and the moving speed of the maintenance device or the person Estimate the maximum range of motion during the remaining flight time; specify the maintenance point within the maximum range of motion.
  • the step of designating the maintenance point within the maximum range of motion includes: estimating the position of the return point according to the state of the power source and/or the source of the drone; and the power source between the maximum range of motion and the estimated return point The point that consumes the least or the closest flight distance is used as a maintenance point.
  • the present invention provides a control method for a drone, the method comprising: acquiring a location of a current maintenance point of the drone; determining a location of the next maintenance point according to a location of the current maintenance point; Send the location of the next maintenance point to the maintenance device or personnel.
  • the current maintenance point is the maintenance point of the last maintenance of the drone; according to the current maintenance point
  • the step of determining the location of the next maintenance point includes: calculating the maximum flight of the maintenance device or personnel based on the current maintenance point location, the estimated maximum flight time of the drone, the motion path of the maintenance equipment or personnel, and the motion speed. The maximum range of motion over time; the position of the next maintenance point is determined within the maximum range of motion.
  • determining the position of the next maintenance point within the maximum motion range comprises: estimating the drone from the current maintenance point according to the current maintenance point position and the flight path of the drone and the estimated maximum flight time The next return point for return maintenance should be performed; the point with the lowest power source consumption or the closest flight distance between the maximum range of motion and the next return point is used as the next maintenance point.
  • the current maintenance point is located at the current location of the maintenance device or the person; and the step of determining the location of the next maintenance point according to the location of the current maintenance point includes: estimating the state of the power source and/or the operation source of the drone The remaining flight time of the drone; calculate the maximum range of motion of the maintenance equipment or personnel within the estimated maximum flight time based on the current position, motion path, and speed of the maintenance equipment or personnel; determine the next maintenance point within the maximum motion range position.
  • the step of determining the position of the next maintenance point within the maximum motion range comprises: estimating the position of the return point according to the state of the power source and/or the source of the drone; and the maximum range of motion and the estimated return point The point where the power source consumes the least or the closest flight distance is used as the next maintenance point.
  • the present invention provides a flight path planning method for a drone, the method comprising: displaying a landform image of a preset flight area; and acquiring a boundary of a land image for indicating a preset flight area.
  • the coordinates of the feature points; the edge lines of the preset flight area are determined according to the coordinates of the plurality of feature points.
  • the feature point includes a plurality of first feature points for indicating an outer contour of the preset flight area, and a second feature point for indicating an outer contour of the obstacle area within the preset flight area.
  • determining the edge line of the preset flight area according to the coordinates of the plurality of feature points comprises: connecting the feature points with the line segments and using the formed fold line as the edge line.
  • the step of determining an edge line of the preset flight area according to the coordinates of the plurality of feature points includes: making a circle or an ellipse with at least one of the feature points as a center, and all or part of the circle or the ellipse The points are used as edge lines.
  • the step of acquiring coordinates of a plurality of feature points for indicating a boundary of the topographic image of the preset flight area includes: receiving coordinates of the plurality of feature points input by the user through the input device; or extracting more from the terrain image by image recognition The coordinates of the feature points.
  • the method further includes: determining, according to the edge line, a flight path of the drone in the preset flight area; displaying the flight path.
  • the step of determining a flight path of the drone in the preset flight area according to the edge line further includes: acquiring a working parameter of the drone; determining a flight path of the drone in the preset flight area according to the edge line
  • the steps include: determining the flight path of the drone in the preset flight area according to the edge line and the operating parameters of the drone.
  • the operating parameters include at least the wind direction, the working width of the drone and the starting point.
  • the step of determining the flight path of the drone in the preset flight area comprises: obtaining the flight direction parallel to the flight direction of the drone in the preset flight area according to the edge line and the working width of the drone a plurality of route segments, wherein the flight direction of the drone is determined according to the wind direction; according to the intersection position of the route segment and the boundary, the preset flight region is divided into a plurality of work regions; and according to the plurality of work regions, the drone is determined to be in a preset flight Flight path within the area.
  • the step of determining a flight path of the drone in the preset flight area according to the plurality of work areas includes: determining a port point that can be used as an entry point or an exit point of each work area; according to the start point and port of the drone
  • the point calculation traverses the unconventional consumption of multiple candidate connection paths for all job areas, wherein the unconventional consumption includes at least the distance consumption between different work areas and the travel cost from the starting point to the work area; selecting the one with the least conventional consumption
  • a candidate connection path is used as a flight path of the drone.
  • the unconventional consumption further includes the travel consumption of the return flight maintenance; calculating the unconventional consumption of the plurality of candidate connection paths traversing all the work areas according to the start point and the port point of the drone further includes: calculating according to the working state of the drone The return point of the return flight maintenance; the travel consumption of the return maintenance is calculated according to the coordinates of the return point and the coordinates of the maintenance point.
  • the step of displaying the flight path includes: displaying the flight path, and displaying the number of return points and/or Or mark the return point in the flight path.
  • the present invention provides a flight path planning system for a drone, the system comprising: one or more processors, working alone or in cooperation, the processor is configured to: acquire a preset flight of the drone Geographic information of the area; according to the geographic information, the preset flight area is divided into a plurality of work areas; and according to the plurality of work areas, the flight path of the drone in the preset flight area is determined.
  • the step includes: a sensor, the sensor is in communication with the processor; the sensor is configured to capture geographic information of the preset flight area, and transmit the geographic information of the preset flight area to the processor.
  • the geographic information includes coordinate information for indicating a boundary of the preset flight area, wherein the boundary includes at least a first boundary for indicating an outer contour of the preset flight area.
  • the boundary further includes a second boundary for indicating an outer contour of the obstacle region within the preset flight region.
  • the processor is further configured to acquire latitude and longitude coordinates for representing a boundary; convert the latitude and longitude coordinates into two-dimensional coordinates.
  • the processor is further configured to convert the latitude and longitude coordinates into three-dimensional coordinates in the geocentric coordinate system; convert the three-dimensional coordinates into two-dimensional coordinates in a plane coordinate system tangent to the surface of the earth.
  • the processor is further configured to acquire, according to the position information of the boundary and the working width of the drone, a plurality of route segments in a preset flight area parallel to the flight direction of the drone; and according to the intersection position of the route segment and the boundary, Divide route segments into multiple work areas.
  • the spacing between two adjacent route segments is equal to the working width of the drone, and the endpoints of each route segment are located on the boundary.
  • the flight direction of the drone is determined according to the wind direction of the preset flight area.
  • the processor is further configured to obtain a tangent point of a line parallel to the flight direction of the drone; the boundary is divided into a plurality of edge segments according to the tangent point; and the routes of the two end points respectively located on the same edge segment Segments are divided into the same work area.
  • the processor is further configured to determine a port point that can serve as an entry point or an exit point of each work area; and determine a flight path of the drone according to the port point of the work area.
  • the processor is further configured to use the two-end endpoints of the most outer route segments of the same work area as port points.
  • the processor is further configured to calculate, according to the starting point and the port point of the drone, an unconventional consumption of traversing a plurality of candidate connection paths of all the working areas, wherein the unconventional consumption includes at least a distance consumption between different working areas and from The distance from the start point to the work area is consumed; a candidate connection path with the least conventional consumption is selected as the flight path of the drone.
  • the processor is further configured to determine a candidate work area connection manner between the work areas; and determine a candidate between the start point of the drone and the port point of the work area and the port point of the work area according to the candidate work area connection manner; Port connection mode; calculates the unconventional consumption according to the candidate port connection mode.
  • the processor is further configured to determine a candidate work area connection manner by a permutation combination or according to an adjacency relationship between the work areas, wherein determining the candidate work area connection manner according to the adjacency relationship between the work areas comprises determining to traverse all the work areas and connecting Multiple candidate work area connections with the fewest number of non-contiguous work areas.
  • the processor is further configured to determine a candidate entry point and a candidate exit point of the work area according to the port point of each work area and the number of the route segments included in the work area for each candidate work area connection manner; according to the work area The candidate entry point and the candidate exit point determine the candidate port connection mode.
  • the processor is further configured to select, for the first work area, a port point closest to the starting point as a candidate entry point of the first work area, and according to the number of the route segments included in the first work area and the first a candidate entry point of the work area determines a candidate exit point of the first work area; for the remaining work areas, a port point closest to the candidate exit point of the previous work area of the current work area is selected as a candidate entry point of the current work area, and according to The number of route segments included in the current work area and the candidate entry points of the current work area determine candidate exit points for the current work area.
  • the processor is further configured to connect the candidate entry point of each work area with the candidate exit point of the previous work area or the starting point of the drone The intersection of the line segment and the edge of the obstacle area; if the intersection is empty, the distance consumed along the line segment is consumed as the distance between the two work areas or between the work area and the starting point of the drone, otherwise the line segment is The distance consumed by the intersection with the obstacle area is replaced by the distance consumed by the obstacle avoidance area, and the sum of the travel consumption generated by the obstacle avoidance area and the travel cost generated by the non-intersection portion of the line segment is calculated as The distance between the two work areas or between the work area and the starting point of the drone.
  • the travel consumption generated by the obstacle avoidance area includes: the travel consumption caused by bypassing the obstacle area by the bypass method or the travel consumption generated by crossing or crossing the obstacle area by raising or lowering.
  • the unconventional consumption further includes the travel consumption of the return flight maintenance;
  • the processor is further configured to calculate a return point to be returned for maintenance according to the state of the power source and/or the operation source of the drone; according to the coordinates of the return point and the maintenance point The coordinates calculate the travel cost of the return flight maintenance.
  • the processor is further configured to sequentially determine whether the current endpoint of the route segment satisfies the endurance condition, and if the endurance condition is not met, the current endpoint is used as the returning point.
  • the processor is further configured to sequentially determine whether the current endpoint of the route segment satisfies the endurance condition; if the endurance condition is not met, determine whether the current endpoint is the required route segment between the current endpoint and the next endpoint; if yes, at the current endpoint The return point is found on the route segment between the next endpoint, so that the drone can safely return to the maintenance point after flying to the return point, and if not, the current endpoint is used as the return point.
  • the endurance condition refers to the remaining amount of the power source and/or the working source of the drone flying to the current end point minus the preset safety amount being greater than or equal to flying from the current end point to the next end point and flying back and forth from the next end point.
  • Point power source and / or source consumption are examples of Point power source and / or source consumption.
  • the processor is further configured to calculate a maximum range of motion of the maintenance device or the personnel during the remaining flight time of the drone according to the remaining flight time of the drone, the motion path of the maintenance device or the person, the current position, and the motion speed; Specify a maintenance point within the range of motion.
  • the processor is further configured to use, as a maintenance point, a point at which the power source and/or the work source between the return range or the next end point consumes the least or the flight distance is the closest.
  • the processor is further configured to acquire coordinates of a special edge point for indicating an edge of the special work area in the preset flight area; calculate an intersection of the route segment and the edge of the special work area; and the edge of the route segment and the special work area The intersection point is inserted into the flight path so that the operation performed by the drone in the special work area is different from the operation performed by the drone outside the special work area.
  • the special work area includes at least one of a flightable non-work area, a high-altitude flight area, and a low-altitude flight area.
  • the processor is further configured to form a coordinate sequence of the plurality of task points for the flight path of the drone, such that the drone performs the work between the plurality of task points according to the coordinate sequence, wherein the task point includes at least the route segment The intersection of the endpoint, the route segment, and the edge of the special work area.
  • the present invention provides a control system for a drone, the system comprising: one or more processors, working alone or in cooperation, the processor is configured to: follow a preset route within a preset flight area Perform flight operations; adjust the flight path of the drone and/or perform operations to adapt the drone to the current operating environment or operating conditions.
  • the device further includes a positioning device, wherein the positioning device is in communication with the processor; the positioning device is configured to acquire current location information of the drone, and transmit the current location information to the processor, and the processor controls the current location information according to the current location information and the preset route.
  • the man-machine performs flight operations.
  • adjusting the flight path of the drone includes at least one of: changing the flight direction, changing the flight height, and stopping the flight; and adjusting the operation performed by the drone includes any one of the following: stopping the operation and starting the operation.
  • the working environment includes at least one of the following: an obstacle in front, a non-working area in front, and a working area in front; and the working state includes at least one of the following: insufficient power source, insufficient working source, lost navigation signal, and received control. command.
  • the working state includes insufficient power source and/or insufficient working source
  • adjusting the flight path of the drone includes changing the flight direction to fly to the maintenance point
  • the processor is further configured to acquire the power source and/or the working source of the drone State; calculate the return point that should be returned for maintenance according to the power source and/or source status of the drone; change the flight direction when the drone flies to the return point to fly to the maintenance point for maintenance.
  • the processor is further configured to acquire a current position coordinate of the drone; and determine whether the current position coordinate of the drone corresponds to a certain task point of the plurality of task points used to represent the flight path of the drone; if yes, obtain The state of the power source and/or source of the drone.
  • the processor is further configured to determine whether the state of the power source and/or the working source of the drone meets the endurance condition; if the endurance condition is not met, the current position of the drone is used as the return point.
  • the processor is further configured to determine whether the state of the power source and/or the working source of the drone meets the endurance condition; if the endurance condition is not met, determine the task corresponding to the current position of the drone and the current position of the drone Whether the next mission point of the point is the route segment to be operated; if so, looking for the return point on the route segment between the current position of the drone and the next mission point, so that the drone flies to the return point After that, it can still return to the maintenance point safely. If not, the current position of the drone is used as the return point.
  • the endurance condition refers to the remaining amount of the power source and/or the working source of the drone minus the preset safety amount greater than or equal to the task point corresponding to the current position of the drone from the current position of the drone to the drone The power source and/or source of operation of the next mission point.
  • the processor is further configured to estimate the remaining flight time of the drone according to the state of the power source and/or the working source of the drone; and calculate the maintenance device or the personnel according to the current position, the motion path, and the motion speed of the maintenance device or the personnel.
  • the maximum range of motion during the estimated remaining flight time; the maintenance point is specified within the maximum range of motion.
  • the processor is further configured to estimate the position of the return point according to the state of the power source and/or the working source of the drone; and minimize the power source consumption or the closest flight distance between the maximum motion range and the estimated return point Point as a maintenance point.
  • the present invention provides a control system for a drone, the system comprising: one or more processors operating separately or in cooperation, the processor for: obtaining the location of the current maintenance point of the drone According to the location of the current maintenance point, determine the location of the next maintenance point; send the location of the next maintenance point to the maintenance device or personnel.
  • the current maintenance point is the maintenance point of the last maintenance of the drone; the processor is further used to maintain the position of the current maintenance point, the estimated maximum flight time of the drone, and the maintenance road of the maintenance equipment or personnel.
  • the path and the speed of motion calculate the maximum range of motion of the maintenance equipment or personnel during the estimated maximum flight time; the position of the next maintenance point is determined within the maximum range of motion.
  • the processor is further configured to estimate, according to the location of the current maintenance point, the flight path of the drone, and the estimated maximum flight time, the next return point that the drone should perform the return maintenance from the current maintenance point;
  • the point at which the power source consumption is the smallest or the closest to the flight distance between the maximum range of motion and the next return point is used as the next maintenance point.
  • the current maintenance point is located at the current position of the maintenance device or the person; the processor is further configured to estimate the remaining flight time of the drone according to the state of the power source and/or the operation source of the drone; according to the maintenance device or personnel
  • the current position, the motion path, and the motion speed calculate the maximum range of motion of the maintenance equipment or personnel during the estimated maximum flight time; the position of the next maintenance point is determined within the maximum motion range.
  • the processor is further configured to estimate the position of the return point according to the state of the power source and/or the working source of the drone; and minimize the power source consumption or the closest flight distance between the maximum motion range and the estimated return point Point as the next maintenance point.
  • the present invention provides a flight path planning system for a drone, the system comprising: a display screen for displaying a topographic image of a preset flight area; one or more processors, alone or in concert Working, the processor is connected to the display screen; the processor is configured to acquire coordinates of a plurality of feature points for indicating a boundary of the topographic image of the preset flight area; and determine an edge of the preset flight area according to coordinate information of the plurality of feature points line.
  • the method further includes an input device, wherein the input device is in communication with the processor, and is configured to receive a plurality of feature points for inputting a boundary of the topographic image representing the preset flight area and obtain coordinates thereof.
  • the processor is further configured to extract coordinates of the plurality of feature points from the topographic image by image recognition.
  • the feature point includes a plurality of first feature points for indicating an outer contour of the preset flight area, and a second feature point for indicating an outer contour of the obstacle area within the preset flight area.
  • the processor is further configured to connect the plurality of feature points with the line segment and form the formed fold line as the edge line.
  • the processor is further configured to make a circle or an ellipse with at least one of the feature points as a center, and use all or part of the circle or the ellipse as an edge line.
  • the processor is further configured to determine a flight path of the drone in the preset flight area according to the edge line; and display the flight path.
  • the processor is further configured to acquire a working parameter of the drone; and determine a flight path of the drone in the preset flight area according to the edge line and the operating parameter of the drone.
  • the operating parameters include at least the wind direction, the working width of the drone and the starting point.
  • the processor is further configured to acquire, according to the edge line and the working width of the drone, a plurality of route segments in a preset flight area parallel to the flight direction of the drone, wherein the flight direction of the drone is determined according to the wind direction;
  • the preset flight area is divided into a plurality of work areas according to the intersection position of the route segment and the boundary; and the flight path of the drone in the preset flight area is determined according to the plurality of work areas.
  • the processor is further configured to determine a port point that can serve as an entry point or an exit point of each work area; calculate an unconventional consumption of the plurality of candidate connection paths traversing all the work areas according to the start point and the port point of the drone, wherein Unconventional consumption includes at least the distance consumption between different work areas and the travel cost from the starting point to the work area; selecting a candidate connection path that is the least conventionally consumed as the flight path of the drone.
  • the unconventional consumption further includes the travel consumption of the return flight maintenance; the processor is further configured to calculate the return flight point that should be returned to the maintenance according to the working state of the drone; calculate the travel cost of the return maintenance according to the coordinates of the return point and the coordinates of the maintenance point .
  • the processor is further configured to control the display screen to display the flight path and display the number of return points and/or mark the return point in the flight path.
  • the invention has the beneficial effects that: by dividing the preset flight area into a plurality of work areas, and determining the flight path of the drone according to the plurality of work areas, the flight path planning of the complex terrain preset flight area can be processed, and the flight path is reduced. The amount of calculations planned.
  • FIG. 1 is a flow chart of a first embodiment of a flight path planning method for a drone of the present invention
  • FIG. 2 is a flow chart of a second embodiment of a flight path planning method for a drone of the present invention
  • FIG. 3 is a flow chart of a third embodiment of a flight path planning method for a drone of the present invention.
  • FIG. 4 is a schematic diagram of dividing a work area in an embodiment of a flight path planning method of the unmanned aerial vehicle of the present invention
  • FIG. 5 is a flow chart of a fourth embodiment of a flight path planning method for a drone of the present invention.
  • FIG. 6 is a flow chart of a fifth embodiment of a flight path planning method for a drone of the present invention.
  • FIG. 7 is a schematic diagram showing a connection relationship between different work areas in an embodiment of a flight path planning method of the unmanned aerial vehicle of the present invention.
  • FIG. 8 is a schematic diagram of an evasive obstacle area in an embodiment of a flight path planning method of the unmanned aerial vehicle of the present invention.
  • FIG. 9 is a flow chart of calculating a return maintenance maintenance consumption of a candidate connection path in an embodiment of a flight path planning method of the unmanned aerial vehicle of the present invention.
  • FIG. 10 is a flow chart of a sixth embodiment of a flight path planning method for a drone of the present invention.
  • FIG. 11 is a flow chart of a seventh embodiment of a flight path planning method for a drone of the present invention.
  • FIG. 12 is a flow chart of an eighth embodiment of a flight path planning method for a drone of the present invention.
  • FIG. 13 is a flow chart of a ninth embodiment of a flight path planning method for a drone of the present invention.
  • Figure 14 is a flow chart showing a first embodiment of the control method of the drone of the present invention.
  • Figure 15 is a flow chart showing a second embodiment of the control method of the drone of the present invention.
  • Figure 16 is a flow chart showing a third embodiment of the control method of the drone of the present invention.
  • Figure 17 is a flow chart showing a fourth embodiment of the control method of the drone of the present invention.
  • FIG. 18 is a schematic structural view of a first embodiment of a flight path planning system for a drone of the present invention.
  • FIG. 19 is a schematic structural view of a second embodiment of a flight path planning system of the drone of the present invention.
  • FIG. 20 is a schematic structural view of a third embodiment of a flight path planning system of the drone of the present invention.
  • 21 is a schematic structural view of a fourth embodiment of a flight path planning system of the drone of the present invention.
  • Figure 22 is a schematic structural view of a first embodiment of a control system for a drone of the present invention.
  • Figure 23 is a schematic structural view of a second embodiment of the control system of the drone of the present invention.
  • Figure 24 is a block diagram showing the structure of a third embodiment of the control system of the drone of the present invention.
  • a first embodiment of a flight path planning method for a drone of the present invention includes:
  • S11 Obtain geographic information of a preset flight area of the drone.
  • the geographic information includes coordinate information for indicating a boundary of the preset flight area.
  • the coordinate information may include coordinate information for indicating a plurality of feature points of the preset flight area boundary.
  • the coordinate information of the feature point input by the user may be received through the input device, and the coordinate information of the feature point may be acquired from the feature image of the preset flight area by image recognition.
  • Feature points can be on the boundary or not on the boundary.
  • a straight line segment or a curved segment may be used to connect feature points on the boundary as all or part of the boundary; or a feature point that is not on the boundary may be a circle or an ellipse, and all or part of the circle or ellipse may be all or part of boundary.
  • the boundary includes at least a first boundary for representing an outer contour of the preset flight area.
  • the boundary when there is an obstacle area in the preset flight area, the boundary further includes a second boundary for indicating an outer contour of the obstacle area.
  • the obstacle area here refers to an obstacled area that the drone needs to bypass, such as a no-fly area, an area where there is an obstacle that is not suitable for flying, such as a house or a utility pole.
  • S12 According to geographic information, the preset flight area is divided into multiple work areas.
  • a plurality of route segments parallel to the flight direction of the drone in the preset flight area are obtained, and the route segment is the working path of the planned drone in the flight area.
  • the route segment is divided into a plurality of work areas according to the intersection position of the route segment and the boundary, that is, the end position of the route segment. In general, refer to the operating characteristics of the drone, including less turning, flying nearby, traversing the preset flight area, etc., and segmenting the preset flight area.
  • the endpoint of the route segment is located on the boundary, and the spacing between adjacent two route segments is equal to the working width of the drone.
  • the flight direction of the drone can be determined according to the wind direction of the preset flight area, and the flight direction of the drone can be determined by other methods, for example, the direction of the longest side of the preset flight area is used as the flight direction of the drone.
  • Unconventional consumption refers to the consumption of the drone's flight operations, including at least the distance between different work areas. Consumption and travel distance from the starting point to the work area.
  • the preset flight area is divided into multiple work areas, and the flight path of the drone is determined according to the plurality of work areas, the flight path planning of the preset terrain of the complex terrain can be processed, and the flight path planning is reduced. The amount of calculation.
  • Step S11 specifically includes:
  • S111 Acquire latitude and longitude coordinates for indicating a boundary.
  • the coordinates of the feature points for representing the boundary are obtained as latitude and longitude coordinates.
  • S112 Convert the latitude and longitude coordinates into three-dimensional coordinates in the geocentric coordinate system.
  • the earth is regarded as a sphere, and the latitude and longitude coordinates are converted into three-dimensional coordinates in a three-dimensional right-angle geocentric coordinate system with the center of the earth as the origin.
  • S113 Convert the three-dimensional coordinates into two-dimensional coordinates in a plane coordinate system tangent to the surface of the earth.
  • the tangent point between the plane and the earth can be the starting point, or it can be the point inside the preset flying area or on the boundary.
  • the projection of the warp and the latitude passing through the tangent plane on the tangent plane may be taken as the x-axis and the y-axis, and other vectors may be used as the x-axis and the y-axis.
  • the preset flight area is small compared to the entire earth area and can be approximated as a plane. If the coordinates of the feature points adopt latitude and longitude coordinates, the calculation process of the subsequent flight path planning is very complicated, and may even bring more errors. This projecting the latitude and longitude coordinates to the plane coordinates of the earth's surface tangent, which can effectively reduce the subsequent calculation.
  • Step S12 specifically includes:
  • S121 Acquire, according to the position information of the boundary and the working width of the drone, a plurality of route segments in a preset flight area parallel to the flight direction of the drone.
  • the route segment is part of the flight path, and the drone needs to fly along the route segment and perform the work when needed.
  • the spacing between two adjacent route segments is equal to the operating width of the drone, and the endpoints of each route segment are located on the boundary.
  • the flight direction of the drone is determined according to the wind direction of the preset flight area.
  • S122 Acquire a tangent point of a line parallel to the flight direction of the drone and the boundary.
  • the tangent points are sorted according to the adjacency relationship, and the boundary between each two tangent points is an edge segment.
  • the working area is specifically illustrated by way of example with reference to the accompanying drawings.
  • FIG. 4 there are obstacles in the preset flight area that require the drone to bypass.
  • a plurality of feature points (not shown) for indicating a boundary of the preset flight area are located on the boundary, and the feature points include a first feature point located on an outer contour of the preset flight area and a first position on the outer contour of the obstacle Two feature points.
  • a vector inner product with the feature points to obtain a first inner product value that is, the feature point is in the first vector Projection on.
  • the inner product of the outer contour of the obstacle completely located inside the preset flight area may not be inner product when the vector inner product is performed, that is, only the first feature point and the first vector are Perform a vector inner product. Then, the feature point A corresponding to the minimum value min of the first inner product value is taken as the minimum boundary point, and the feature point B corresponding to the maximum value max of the first inner product value is taken as the maximum boundary point.
  • the intersection of the plurality of straight lines parallel to the flight direction of the drone and the boundary of the preset flight area between the maximum boundary point and the minimum boundary point is calculated, and the distance between the adjacent straight lines is equal to the working width of the drone.
  • the working width is the spray width, that is, the effective spray width of the drone on the ground at the specified height and the spray direction. It should be noted that if there is an obstacle in the preset flight area, the first boundary and the second boundary must be considered at the same time.
  • the line segments in which the plurality of straight lines are located in the preset flight area are regarded as flightable route segments, for example, the broken lines in the block 4, the block 5, the block 6, and the block 7 in FIG. segment.
  • a second vector for indicating the flight direction of the drone is set This step can also be earlier than or with the first vector. The steps are completed at the same time. A vector inner product of the coordinates of the intersection of the second vector and each straight line is respectively obtained to obtain a second inner product value. Regardless of the tangent case, the number m of intersections of a straight line and a boundary of a preset flight area is an even number.
  • intersection points are sorted according to the size of the second inner product value and then recorded as the point set [0, m-1], then the line segments [0, 1], [2, 3], [4, 5]... [m-2, m-1] is a flightable route segment, [1, 2], [3, 4], etc. are internal segments of the obstacle zone. Repeat the above steps for each straight line to obtain all route segments.
  • the third vector and the fourth vector defined by the adjacent feature points on both sides are respectively calculated, for example, the vector a and the vector b pointing from the point A to the two sides in FIG.
  • First vector An inner product is respectively performed with the third vector and the fourth vector to obtain a third inner product value and a fourth inner product value. It is judged whether the third inner product value and the fourth inner product value are the same number. If the same number is positive or negative at the same time, the feature point is a tangent point, for example, point A in FIG.
  • the preset flight area is divided into eight working areas, which are represented by blocks 1-8, wherein the solid lines separating the different working areas are only schematic and are not necessarily drawn.
  • the first vector may be converted in the process of converting the latitude and longitude coordinates of the feature point into the plane coordinate.
  • the second vector As the x-axis of the plane coordinate system
  • the first vector As the y-axis of the plane coordinate system
  • the second vector As the x-axis of the plane coordinate system.
  • the arbitrary object and the first vector Second vector The calculation process of the vector inner product can be simplified to the process of taking the x or y value of the object.
  • Step S13 specifically includes:
  • S131 Determine a port point that can be used as an entry point or an exit point of each work area.
  • the two end points of the most outer route segment of the same work area are used as port points.
  • a rectangular-like work area may have four port points.
  • S132 Calculate the unconventional consumption of the plurality of candidate connection paths traversing all the work areas according to the starting point and the port point of the drone.
  • Unconventional consumption includes at least the distance travel between different work areas and the travel cost from the starting point to the work area, and may further include obstacle avoidance consumption and/or return maintenance consumption. If an end point is provided, the unconventional consumption is further consumed by the distance from the work area to the end point.
  • Step S132 specifically includes:
  • the candidate work area connection mode can be determined by arrangement and combination. For n work areas, it is necessary to traverse all work areas, and there are n! For the candidate work area connection method, as the number of work areas increases, the amount of calculation increases sharply.
  • the candidate work area connection manner may be determined according to the adjacency relationship between the work areas, that is, a plurality of work area connection manners that traverse all the work areas and connect the non-adjacent work areas to the least, as the candidate work area connection manner. Still taking the preset flight area in FIG. 4 as an example, the connection relationship diagram between different work areas in FIG. 4 is drawn, as shown in FIG. 7. The two work areas connected by lines in Fig.
  • the problem of determining the candidate work area connection manner according to the adjacency relationship between the work areas can be converted into a problem of solving the Hamilton path and/or the Euler path according to the connectivity diagram.
  • S1322 Determine a candidate port connection manner between the starting point of the drone and the port point of the working area and the port point of the working area according to the candidate working area connection manner.
  • the candidate entry point and the candidate exit point of the work area are determined according to the port point of each work area and the number of route segments included in the work area, and then candidate entry points and candidate exits according to the work area are selected.
  • a candidate port connection mode is determined by a point, and a candidate port connection mode of a candidate work area connection mode is a candidate connection path.
  • the candidate entry point and the candidate exit point may be determined by using a permutation combination, that is, each port point may be used as a candidate entry point or a candidate exit point; or each port point may be used as a candidate entry point respectively.
  • the candidate exit point is determined according to the number of route segments and the candidate entry point, that is, the port point that the drone leaves the work area after the round-trip flight from the candidate entry point to the work area is used as the candidate exit point.
  • only one candidate port connection mode may be adopted, that is, for the first work area, the port point closest to the start point is selected as a candidate for the first work area.
  • the entry point, and in the foregoing manner, the candidate exit point of the first work area is determined according to the number of route segments included in the first work area and the candidate entry points of the first work area; for the remaining work areas, the current work area is selected.
  • the port point closest to the candidate exit point of the previous work area is used as the candidate entry point of the current work area, and according to the foregoing manner, the current work area is determined according to the number of route segments included in the current work area and the candidate entry point of the current work area.
  • Candidate exit point for each candidate work area connection mode, only one candidate port connection mode may be adopted, that is, for the first work area, the port point closest to the start point is selected as a candidate for the first work area.
  • Unconventional consumption may include consumption of any non-work path during flight, for example, unconventional consumption may include at least the distance travel between different work areas and the travel cost of flying from the starting point to the work area.
  • the adjacency matrix can be used to calculate, and the i-th row and the j-th column element of the adjacency matrix represent the cost of connecting the i-th work area and the j-th work area, that is, the distance between the drone and the two work areas. Consumption.
  • Each work area has four port points.
  • the consumption of the connection When calculating the distance consumption, the specified port point connection consumption corresponding to different work areas can be found from the adjacency matrix, and then accumulated.
  • the starting point/end point can be counted as a special working area in the adjacency matrix, or the distance between the starting point/end point and the working area can be calculated separately.
  • drones can fly in straight lines between port points in different work areas, and between start point/end point and work area. If obstacles exist, further consideration should be given to obstacle avoidance.
  • the obstacle avoidance consumption that is, the travel consumption generated by the drone avoiding the obstacle area includes: the travel consumption caused by bypassing the obstacle area by means of bypass, such as bypassing the house, etc., and/or crossing or passing by means of elevation Reduce the travel cost of the way through the obstacle area, such as raising to cross trees, etc., to cross wires, bridges, etc. Any two port points in the two job areas of the elements of the adjacency matrix
  • the consumption can be a comprehensive distance consumption that has included obstacle avoidance consumption.
  • the specific process of calculating the integrated route consumption including the obstacle avoidance consumption includes: selecting a candidate entry point for each work area and a candidate exit point of the previous work area Or the line segment of the starting point of the drone intersects the edge of the obstacle area. If the intersection is empty, it means that there is no obstacle, no need to consider the obstacle avoidance consumption, and the travel consumption generated along the line segment is consumed as the distance between the two work areas or between the work area and the start point of the drone.
  • the distance consumption caused by the intersection of the line segment and the obstacle area is replaced by the distance consumption caused by the obstacle avoiding area, and the distance consumption caused by the obstacle avoiding area is calculated.
  • the sum of the distances consumed by the non-intersecting portions of the line segments is consumed as a comprehensive path between the two work areas or between the work area and the start point of the drone. For example, as shown in FIG. 8, the line segment between the port 3 of the work area i and the port 1 of the work area j intersects with the obstacle area indicated by the broken line, and the portion of the line segment within the broken line is replaced with the outer side of the broken line.
  • the line portion is calculated to calculate the integrated distance.
  • the bypass obstacle area shown in the figure is flying along the outer contour of the obstacle area.
  • other obstacles can be used to bypass the obstacle area, such as port points or port points and starting points/end points along different working areas. A fly between the broken lines around the obstacle area.
  • the unconventional consumption further includes the travel consumption of the return flight maintenance, and the specific steps of calculating the return maintenance maintenance consumption of the candidate connection path at this time include:
  • S1324 Calculate the return point that should be returned for maintenance according to the state of the power source and/or the source of the drone.
  • endurance conditions refer to the power source and/or source of operation of the drone to the current endpoint.
  • the remaining amount minus the preset amount of security is greater than or equal to the power source and/or job source consumption from the current endpoint to the next endpoint and from the next endpoint to the round-trip waypoint.
  • the current endpoint of the route segment in the candidate connection path satisfies the endurance condition. If the endurance condition is not met, the current endpoint may be directly used as the return point. In order to further improve the utilization rate of the power source and/or the operation source, it may further determine whether the current end point and the next end point are the route segments to be operated when the endurance condition is not satisfied; if yes, the current end point and the next end point The return point is found on the route segment so that the drone can safely return to the maintenance point after flying to the return point. If not, the current endpoint is used as the return point.
  • S1325 Calculate the travel consumption of the return maintenance according to the coordinates of the return point and the coordinates of the maintenance point.
  • the maintenance point can be fixed at a certain coordinate, or it can be movable, or an area.
  • the maintenance point When the maintenance point is movable, its position may be related to the flight path of the drone, specifically, according to the remaining flight time of the drone, the movement path of the maintenance equipment or personnel, the current position, and the movement speed. Maintain the maximum range of motion of the equipment or personnel during the remaining flight time of the drone, and maintain the minimum power source and/or source of operation between the return point or the next point in the maximum range of motion or the point closest to the flight distance as maintenance point.
  • the location of the maintenance point can also be unrelated to the flight path of the drone.
  • each of the candidate connection paths can be calculated for its return maintenance consumption and added to the unconventional consumption, and then the candidate connection path with the smallest unconventional consumption is used as the flight path of the drone.
  • the flight path of the drone can be determined without calculating the return maintenance consumption, and then the return point for returning maintenance should be calculated for the determined flight path, and the position and flight at the maintenance point can be calculated. The position of the maintenance point is further calculated when the path is related.
  • the sixth embodiment of the flight path planning method of the present invention is based on the fourth embodiment of the flight path planning method of the unmanned aerial vehicle of the present invention, and further includes:
  • S134 forming a coordinate sequence of a plurality of task points for a flight path of the drone.
  • the drone will work between multiple task points in a sequence of coordinates.
  • the mission point includes at least a point at which the drone needs to change the direction of flight. Specifically, at least the point in which the direction of the flight needs to be changed includes The endpoint of the line segment, and the point used to represent the bypass avoidance obstacle path, may further include a return point.
  • the coordinates of the calculated task points are generally based on the plane coordinates of the tangent plane.
  • the plane coordinates can be converted into the latitude and longitude coordinates.
  • This calculation process is the second implementation of the flight path planning method of the unmanned aerial vehicle of the present invention.
  • the inverse operation of converting latitude and longitude into plane coordinates can be referred to the specific description.
  • the coordinate sequence may include, in addition to the coordinates of each task point, the direction of flight and/or the operation performed at the task point.
  • the operations performed include at least starting and stopping operations to accommodate the flight status of the drone, such as when the drone moves from one work area to another to stop the operation from waste.
  • a special working area in the preset flight area specifically, at least one of a flightable non-working area, a high-altitude flying area, and a low-altitude flying area.
  • the operation performed by the drone in a special work area is different from the operation performed by the drone outside the special work area.
  • the coordinates of the particular edge point representing the edge of the special work area in the preset flight area should be obtained, and then the intersection of the route segment and the edge of the special work area should be calculated and inserted into the flight path.
  • the task point should include the intersection of the route segment and the edge of the special work area.
  • the execution body of the seventh embodiment of the flight path planning method of the present invention may be a control application of the drone, such as an app or the like, or a device running the control application, for example, no one.
  • the remote control of the machine, mobile phone, ipad, base station of the drone, etc. includes:
  • the landform image can be acquired on the display screen or projected by the aerial image.
  • S22 Acquire coordinates of a plurality of feature points for indicating a boundary of the topographic image of the preset flight area.
  • the coordinate information of the feature points input by the user may be received through an input device such as a touch screen, a keyboard, a mouse, a microphone, a button, etc., and the coordinate information of the feature points may also be extracted from the feature image by image recognition.
  • Feature points can be on the boundary or not on the boundary.
  • the feature points include at least a plurality of first feature points for representing an outer contour of the preset flight area.
  • the feature point may further include a second feature point for indicating an outer contour of the obstacle area.
  • the obstacle area here refers to an obstacled area that the drone needs to bypass, such as a no-fly area, an area where there is an obstacle that is not suitable for flying, such as a house or a utility pole.
  • S23 Determine an edge line of the preset flight area according to coordinates of the plurality of feature points.
  • edge line and the topographic image can be superimposed on the display.
  • the eighth embodiment of the flight path planning method of the unmanned aerial vehicle of the present invention is based on the seventh embodiment of the flight path planning method of the unmanned aerial vehicle of the present invention, and further includes:
  • the job parameters input by the user can be obtained through the input device, and the job parameters of the drone can also be obtained by other means, such as from the Internet or locally stored data.
  • the operating parameters include at least the wind direction, the operating width of the drone, and the starting point.
  • S25 Determine a flight path of the drone in the preset flight area according to the edge line and the operating parameters of the drone.
  • a plurality of route segments parallel to the flight direction of the drone in the preset flight area are obtained, and the flight direction of the drone is determined according to the wind direction, and the spacing between adjacent route segments Equal to the width of the drone's job.
  • the operating characteristics of the drone including less turns, nearby flights, etc., and connect these route segments with the starting point (plus the end point when needed), and select the unconventional minimum connection method as the flight path.
  • the flight path is displayed on the display, and the flight path and the terrain image can be superimposed.
  • the ninth embodiment of the flight path planning method of the unmanned aerial vehicle of the present invention is based on the eighth embodiment of the flight path planning method of the unmanned aerial vehicle of the present invention, and the step S25 specifically includes:
  • S251 Acquire a plurality of route segments in a preset flight area parallel to a flight direction of the drone according to the edge line and the working width of the drone.
  • S252 The preset flight area is divided into multiple work areas according to the intersection position of the route segment and the boundary.
  • S253 Determine a flight path of the drone in the preset flight area according to the plurality of work areas.
  • the operating parameters of the drone should include the parameters required to estimate the return point, such as the maximum flight time/distance of the drone, the motion path of the maintenance equipment or personnel. , speed of movement, etc.
  • the flight path is displayed, the number of return points can be displayed on the display and/or the return point can be marked in the flight path.
  • the first embodiment of the control method of the drone of the present invention includes:
  • S31 Perform a flight operation in a preset flight area according to a preset flight path.
  • S32 Adjust the flight path of the drone and/or the operation performed to adapt the drone to the current working environment or working state.
  • the situation faced by the drone during actual flight may be inconsistent with the preset flight path.
  • the maximum flight distance is not accurate, the battery parameters used in the actual battery and planning are different, new obstacles appear, and the original obstacle disappears.
  • Adapting the drone for example, when new obstacles appear in front, you can change the direction of the flight to bypass the obstacle and change the flying height to cross or cross the obstacle.
  • adjusting the flight path of the drone includes at least one of: changing the flight direction, changing the flight height, and stopping the flight; and adjusting the operation performed by the drone includes any one of the following: stopping the operation and starting the operation.
  • the working environment includes at least one of the following: an obstacle in front, a non-working area in front, and a working area in front; and the working state includes at least one of the following: insufficient power source, insufficient working source, missing navigation signal, and receiving a control command.
  • a second embodiment of the control method of the drone of the present invention is in the drone of the present invention.
  • the working state includes insufficient power source and/or insufficient working source.
  • Adjusting the flight path of the drone includes changing the flight direction to fly to the maintenance point.
  • Step S22 specifically includes:
  • S321 Acquire the state of the power source and/or the working source of the drone.
  • the state of the power source and/or the working source of the drone is obtained only at the task point in the flight path, and specifically includes: acquiring the current position coordinates of the drone; Determining whether the current position coordinates of the drone correspond to a certain one of the plurality of task points for indicating the flight path of the drone; if so, acquiring the state of the power source and/or the work source of the drone. It is of course also possible to obtain the state of the power source and/or the source of the drone at any point in the flight path.
  • S322 Calculate a return point that should be returned for maintenance according to the power source and/or the source status of the drone.
  • the endurance condition refers to the remaining amount of the power source and/or the working source of the drone minus the preset safety amount greater than or equal to the next task point corresponding to the current position of the drone from the current position of the drone to the drone The power source and/or source of operation of the task point.
  • the state of the power source and/or the working source of the drone meets the endurance condition; if the endurance condition is not met, the current position of the drone can be directly used as the return point.
  • the route segment to be operated if yes, find the return point on the route segment between the current position of the drone and the next mission point, so that the drone can safely return to the maintenance point after flying to the return point, if not , the current location of the drone is used as the return point.
  • Maintenance points can be fixed at a certain coordinate or movable.
  • the maintenance point is movable, and its position can be controlled by the drone or by the drone.
  • the implementation of the embodiment may be that the return point is calculated during the flight operation to perform the return flight maintenance in the case that the preset flight path does not include the return point, and the return point may be included in the preset flight path. In the case of the actual flight situation, the estimated return point is corrected.
  • the third embodiment of the control method of the unmanned aerial vehicle of the present invention is based on the second embodiment of the control method of the unmanned aerial vehicle of the present invention, and further includes:
  • S324 Estimating the remaining flight time of the drone according to the state of the power source and/or the working source of the drone.
  • the remaining amount of the power source and/or the working source of the drone is subtracted from the preset safety amount, and the remaining flight time of the drone can be estimated by dividing the corresponding consumption speed, when calculated according to the power source and the working source. When the two values are different, take a smaller value.
  • S325 Calculate the maximum range of motion of the maintenance equipment or personnel in the estimated remaining flight time according to the current position, the motion path, and the motion speed of the maintenance equipment or personnel.
  • the point at which the power source and/or the work source between the return range or the next end point consumes the least or the closest flight distance is used as a maintenance point. After the maintenance point is specified, the location of the maintenance point needs to be sent to the maintenance device or personnel to rush to the maintenance point.
  • the calculation of the maintenance point in the embodiment may be relatively independent from the calculation of the return point in the previous embodiment, for example, the location of the next maintenance point is specified after the return maintenance is completed; or it may be completed at the same time.
  • the fourth embodiment of the control method of the unmanned aerial vehicle of the present invention includes:
  • the location of the current maintenance point can be the location of the maintenance point where the drone was last maintained, or the current location of the maintenance device or personnel.
  • S42 Determine the location of the next maintenance point according to the location of the current maintenance point.
  • the maximum range of motion of the maintenance equipment or personnel is calculated based on the current maintenance point location, the flight time of the drone, the motion path of the maintenance equipment or personnel, and the motion speed, and the position of the next maintenance point is determined within the maximum motion range.
  • the flight time of the drone is the estimated maximum flight time of the drone. Determine the next maintenance point within the maximum motion range Specifically, according to the location of the current maintenance point, the flight path of the drone, and the estimated maximum flight time, it is estimated that the next return point of the drone that should be returned to the next maintenance from the current maintenance point; The point with the lowest power source consumption or the closest flight distance between the next return point is used as the next maintenance point.
  • the flight time of the drone refers to the remaining flight time of the drone estimated based on the state of the power source and/or the source of the drone. Determining the next maintenance point within the maximum range of motion specifically includes: estimating the position of the return point according to the state of the power source and/or the source of the drone; and consuming power source between the maximum range of motion and the estimated return point The point with the smallest or closest flight distance is used as the next maintenance point.
  • S43 Send the location of the next maintenance point to the maintenance device or personnel.
  • the first embodiment of the flight path planning system of the unmanned aerial vehicle of the present invention comprises: a processor 11. Only one processor is shown in the figure, and the actual number of processors can be more. When the number of processors is greater than one, each processor can work alone or in concert.
  • the processor 11 may be referred to as a Central Processing Unit (CPU) or a Microcontroller Unit (MCU).
  • the processor 11 may be an integrated circuit chip with signal processing capabilities.
  • the processor 11 can also be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA off-the-shelf programmable gate array
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the system may further include a memory (not shown) for storing instructions and data necessary for the processor 11 to operate, and for storing the received data.
  • the processor 11 is configured to acquire geographic information of a preset flight area of the drone; and divide the preset flight area into a plurality of work areas according to the geographic information; and determine, according to the multiple working areas, the drone in the preset flight area Flight path.
  • the processor 11 is a microprocessor that executes a program, including: a geographic information program configured to acquire geographic information of a preset flight area of the drone; And a partitioning program configured to divide the preset flight area into a plurality of work areas according to the geographic information; the path determining program is configured to divide the preset flight area into the plurality of work areas according to the geographic information.
  • the second embodiment of the flight path planning system of the unmanned aerial vehicle of the present invention is based on the first embodiment of the flight path planning system of the unmanned aerial vehicle of the present invention, further comprising a sensor 12, a sensor 12 and processing
  • the device 11 is connected in communication.
  • the sensor 12 is configured to capture geographic information of a preset flight area and transmit geographic information of the preset flight area to the processor.
  • the geographic information includes coordinate information for indicating a boundary of the preset flight area, wherein the boundary includes at least a first boundary for indicating an outer contour of the preset flight area.
  • the boundary further includes a second boundary for indicating an outer contour of the obstacle region within the preset flight region.
  • the processor is further configured to acquire latitude and longitude coordinates for representing a boundary; convert the latitude and longitude coordinates into two-dimensional coordinates.
  • the processor is further configured to convert the latitude and longitude coordinates into three-dimensional coordinates in the geocentric coordinate system; convert the three-dimensional coordinates into two-dimensional coordinates in a plane coordinate system tangent to the surface of the earth.
  • the processor is further configured to acquire, according to the position information of the boundary and the working width of the drone, a plurality of route segments in a preset flight area parallel to the flight direction of the drone; and according to the intersection position of the route segment and the boundary, Divide route segments into multiple work areas.
  • the spacing between two adjacent route segments is equal to the working width of the drone, and the endpoints of each route segment are located on the boundary.
  • the flight direction of the drone is determined according to the wind direction of the preset flight area.
  • the processor is further configured to obtain a tangent point of a line parallel to the flight direction of the drone; the boundary is divided into a plurality of edge segments according to the tangent point; and the routes of the two end points respectively located on the same edge segment Segments are divided into the same work area.
  • the processor is further configured to determine a port point that can serve as an entry point or an exit point of each work area; and determine a flight path of the drone according to the port point of the work area.
  • the processor is further configured to use both sides of the outermost route segment of the same work area Point as a port point.
  • the processor is further configured to calculate, according to the starting point and the port point of the drone, an unconventional consumption of traversing a plurality of candidate connection paths of all the working areas, wherein the unconventional consumption includes at least a distance consumption between different working areas and from The distance from the start point to the work area is consumed; a candidate connection path with the least conventional consumption is selected as the flight path of the drone.
  • the processor is further configured to determine a candidate work area connection manner between the work areas; and determine a candidate between the start point of the drone and the port point of the work area and the port point of the work area according to the candidate work area connection manner; Port connection mode; calculates the unconventional consumption according to the candidate port connection mode.
  • the processor is further configured to determine a candidate work area connection manner by a permutation combination or according to an adjacency relationship between the work areas, wherein determining the candidate work area connection manner according to the adjacency relationship between the work areas comprises determining to traverse all the work areas and connecting Multiple candidate work area connections with the fewest number of non-contiguous work areas.
  • the processor is further configured to determine a candidate entry point and a candidate exit point of the work area according to the port point of each work area and the number of the route segments included in the work area for each candidate work area connection manner; according to the work area The candidate entry point and the candidate exit point determine the candidate port connection mode.
  • the processor is further configured to select, for the first work area, a port point closest to the starting point as a candidate entry point of the first work area, and according to the number of the route segments included in the first work area and the first a candidate entry point of the work area determines a candidate exit point of the first work area; for the remaining work areas, a port point closest to the candidate exit point of the previous work area of the current work area is selected as a candidate entry point of the current work area, and according to The number of route segments included in the current work area and the candidate entry points of the current work area determine candidate exit points for the current work area.
  • the processor is further configured to connect the candidate entry point connecting each work area with the candidate exit point of the previous work area or the line segment of the starting point of the drone and the obstacle The intersection of the edges of the region; if the intersection is empty, the distance along the segment will be eliminated.
  • the consumption is the distance between the two work areas or between the work area and the starting point of the drone, otherwise the distance consumed by the intersection of the line segment and the obstacle area is replaced by the distance consumed by the obstacle avoiding area.
  • the travel consumption generated by the obstacle avoidance area includes: the travel consumption caused by bypassing the obstacle area by the bypass method or the travel consumption generated by crossing or crossing the obstacle area by raising or lowering.
  • the unconventional consumption further includes the travel consumption of the return flight maintenance;
  • the processor is further configured to calculate a return point to be returned for maintenance according to the state of the power source and/or the operation source of the drone; according to the coordinates of the return point and the maintenance point The coordinates calculate the travel cost of the return flight maintenance.
  • the processor is further configured to sequentially determine whether the current endpoint of the route segment satisfies the endurance condition, and if the endurance condition is not met, the current endpoint is used as the returning point.
  • the processor is further configured to sequentially determine whether the current endpoint of the route segment satisfies the endurance condition; if the endurance condition is not met, determine whether the current endpoint is the required route segment between the current endpoint and the next endpoint; if yes, at the current endpoint The return point is found on the route segment between the next endpoint, so that the drone can safely return to the maintenance point after flying to the return point, and if not, the current endpoint is used as the return point.
  • the endurance condition refers to the remaining amount of the power source and/or the working source of the drone flying to the current end point minus the preset safety amount being greater than or equal to flying from the current end point to the next end point and flying back and forth from the next end point.
  • Point power source and / or source consumption are examples of Point power source and / or source consumption.
  • the processor is further configured to calculate a maximum range of motion of the maintenance device or the personnel during the remaining flight time of the drone according to the remaining flight time of the drone, the motion path of the maintenance device or the person, the current position, and the motion speed; Specify a maintenance point within the range of motion.
  • the processor is further configured to use, as a maintenance point, a point at which the power source and/or the work source between the return range or the next end point consumes the least or the flight distance is the closest.
  • the processor is further configured to acquire a special work area for indicating a preset flight area.
  • the coordinates of the special edge points of the edge calculate the intersection of the route segment and the edge of the special work area; insert the intersection of the route segment and the edge of the special work area into the flight path, so that the operation performed by the drone in the special work area is different The operation of the drone outside the special work area.
  • the special work area includes at least one of a flightable non-work area, a high-altitude flight area, and a low-altitude flight area.
  • the processor is further configured to form a coordinate sequence of the plurality of task points for the flight path of the drone, such that the drone performs the work between the plurality of task points according to the coordinate sequence, wherein the task point includes at least the route segment The intersection of the endpoint, the route segment, and the edge of the special work area.
  • a third embodiment of the flight path planning system of the unmanned aerial vehicle of the present invention includes a processor 21 and a display screen 22.
  • the processor 21 is in communication with the display screen 22. Only one processor is shown in the figure, and the actual number of processors can be more. When the number of processors is greater than one, each processor can work alone or in concert.
  • the processor 21 may be referred to as a Central Processing Unit (CPU) or a Microcontroller Unit (MCU).
  • Processor 21 may be an integrated circuit chip with signal processing capabilities.
  • the processor 21 can also be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, and discrete hardware components.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the system may further include a memory (not shown) for storing instructions and data necessary for the processor 21 to operate, and for storing the received data.
  • the display screen 22 is for displaying a landscape image of a preset flight area under the control of the processor 21.
  • the processor 21 is configured to acquire coordinates of a plurality of feature points for indicating a boundary of the topographic image of the preset flight area; and determine an edge line of the preset flight area according to the coordinate information of the plurality of feature points.
  • the fourth embodiment of the flight path planning system of the unmanned aerial vehicle of the present invention is based on the third embodiment of the flight path planning system of the unmanned aerial vehicle of the present invention, further comprising an input device 23 and an input device 23 It is communicatively coupled to the processor 21.
  • the input device 23 is configured to receive a preset fly for input A plurality of feature points of the boundary of the topographic image of the row area and the coordinates thereof are obtained.
  • the processor is further configured to extract coordinates of the plurality of feature points from the topographic image by image recognition.
  • the feature point includes a plurality of first feature points for indicating an outer contour of the preset flight area, and a second feature point for indicating an outer contour of the obstacle area within the preset flight area.
  • the processor is further configured to connect the plurality of feature points with the line segment and form the formed fold line as the edge line.
  • the processor is further configured to make a circle or an ellipse with at least one of the feature points as a center, and use all or part of the circle or the ellipse as an edge line.
  • the processor is further configured to determine a flight path of the drone in the preset flight area according to the edge line; and display the flight path.
  • the processor is further configured to acquire a working parameter of the drone; and determine a flight path of the drone in the preset flight area according to the edge line and the operating parameter of the drone.
  • the operating parameters include at least the wind direction, the working width of the drone and the starting point.
  • the processor is further configured to acquire, according to the edge line and the working width of the drone, a plurality of route segments in a preset flight area parallel to the flight direction of the drone, wherein the flight direction of the drone is determined according to the wind direction;
  • the preset flight area is divided into a plurality of work areas according to the intersection position of the route segment and the boundary; and the flight path of the drone in the preset flight area is determined according to the plurality of work areas.
  • the processor is further configured to determine a port point that can serve as an entry point or an exit point of each work area; calculate an unconventional consumption of the plurality of candidate connection paths traversing all the work areas according to the start point and the port point of the drone, wherein Unconventional consumption includes at least the distance consumption between different work areas and the travel cost from the starting point to the work area; selecting a candidate connection path that is the least conventionally consumed as the flight path of the drone.
  • the unconventional consumption further includes the travel consumption of the return flight maintenance; the processor is further configured to calculate the return flight point that should be returned to the maintenance according to the working state of the drone; calculate the travel cost of the return maintenance according to the coordinates of the return point and the coordinates of the maintenance point .
  • the processor is further configured to control the display screen to display the flight path and display the number of return points and/or mark the return point in the flight path.
  • the first embodiment of the control system of the drone of the present invention comprises: a processor 31. Only one processor is shown in the figure, and the actual number of processors can be more. When the number of processors is greater than one, each processor can work alone or in concert.
  • the processor 31 can be a drone flight controller or a part thereof that controls the flight and operation of the drone.
  • the processor 31 may be referred to as a Central Processing Unit (CPU) or a Microcontroller Unit (MCU).
  • Processor 31 may be an integrated circuit chip with signal processing capabilities.
  • the processor 31 can also be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA off-the-shelf programmable gate array
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the system may further include a memory (not shown) for storing instructions and data necessary for the operation of the processor 31, as well as for storing the received data.
  • the processor 31 is configured to perform a flight operation in a preset flight area according to a preset route; adjust a flight path of the drone and/or an operation performed to adapt the drone to the current working environment or working state.
  • the second embodiment of the control system of the unmanned aerial vehicle of the present invention is based on the first embodiment of the control system of the unmanned aerial vehicle of the present invention, further comprising a positioning device 32, a positioning device 32 and a processor 31. Communication connection.
  • the positioning device 32 is configured to acquire current location information of the drone, and transmit the current location information to the processor 31, and the processor 31 controls the drone to perform the flight operation according to the current location information and the preset route.
  • adjusting the flight path of the drone includes at least one of: changing the flight direction, changing the flight height, and stopping the flight; and adjusting the operation performed by the drone includes any one of the following: stopping the operation and starting the operation.
  • the working environment includes at least one of the following: an obstacle in front, a non-working area in front, and a working area in front; and the working state includes at least one of the following: insufficient power source, insufficient working source, lost navigation signal, and received control. command.
  • the working state includes insufficient power source and/or insufficient working source
  • adjusting the flight path of the drone includes changing the flight direction to fly to the maintenance point
  • the processor is further configured to acquire the power source and/or the working source of the drone State; calculate the return point that should be returned for maintenance according to the power source and/or source status of the drone; change the flight direction when the drone flies to the return point to fly to the maintenance point for maintenance.
  • the processor is further configured to acquire a current position coordinate of the drone; and determine whether the current position coordinate of the drone corresponds to a certain task point of the plurality of task points used to represent the flight path of the drone; if yes, obtain The state of the power source and/or source of the drone.
  • the processor is further configured to determine whether the state of the power source and/or the working source of the drone meets the endurance condition; if the endurance condition is not met, the current position of the drone is used as the return point.
  • the processor is further configured to determine whether the state of the power source and/or the working source of the drone meets the endurance condition; if the endurance condition is not met, determine the task corresponding to the current position of the drone and the current position of the drone Whether the next mission point of the point is the route segment to be operated; if so, looking for the return point on the route segment between the current position of the drone and the next mission point, so that the drone flies to the return point After that, it can still return to the maintenance point safely. If not, the current position of the drone is used as the return point.
  • the endurance condition refers to the remaining amount of the power source and/or the working source of the drone minus the preset safety amount greater than or equal to the task point corresponding to the current position of the drone from the current position of the drone to the drone The power source and/or source of operation of the next mission point.
  • the processor is further configured to estimate the remaining flight time of the drone according to the state of the power source and/or the working source of the drone; and calculate the maintenance device or the personnel according to the current position, the motion path, and the motion speed of the maintenance device or the personnel.
  • the maximum range of motion during the estimated remaining flight time; the maintenance point is specified within the maximum range of motion.
  • the processor is further configured to estimate the position of the return point according to the state of the power source and/or the working source of the drone; minimize the power source consumption or the flight distance between the maximum motion range and the estimated return point The most recent point is the maintenance point.
  • a third embodiment of the control system of the drone of the present invention includes a processor 41. Only one processor is shown in the figure, and the actual number of processors can be more. When the number of processors is greater than one, each processor can work alone or in concert.
  • the processor 41 can be a drone flight controller or a part thereof that controls the flight and operation of the drone.
  • the processor 41 may be referred to as a Central Processing Unit (CPU) or a Microcontroller Unit (MCU).
  • Processor 41 may be an integrated circuit chip with signal processing capabilities.
  • the processor 41 can also be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA off-the-shelf programmable gate array
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the system may further include a memory (not shown) for storing instructions and data necessary for the processor 41 to operate, and for storing the received data.
  • the processor 41 is configured to acquire the location of the current maintenance point of the drone; determine the location of the next maintenance point according to the location of the current maintenance point; and send the location of the next maintenance point to the maintenance device or personnel.
  • the processor is a microprocessor that executes a program that includes a location acquisition program configured to be the location of a current maintenance point of the drone; a maintenance point program configured to be based on the location of the current maintenance point The location of the next maintenance point is determined; the sending program is configured to be sent to the maintenance device or personnel at the location of the next maintenance point.
  • the current maintenance point is the maintenance point of the last maintenance of the drone; the processor is further configured to calculate and maintain according to the current maintenance point location, the estimated maximum flight time of the drone, the motion path of the maintenance equipment or personnel, and the motion speed.
  • the maximum range of motion of the equipment or personnel during the estimated maximum flight time; the location of the next maintenance point is determined within the maximum range of motion.
  • the processor is further configured to estimate, according to the location of the current maintenance point, the flight path of the drone, and the estimated maximum flight time, the next return point that the drone should perform the return maintenance from the current maintenance point; Minimum power consumption or closest flight distance between the maximum range of motion and the next return point The point is the next maintenance point.
  • the current maintenance point is located at the current position of the maintenance device or the person; the processor is further configured to estimate the remaining flight time of the drone according to the state of the power source and/or the operation source of the drone; according to the maintenance device or personnel
  • the current position, the motion path, and the motion speed calculate the maximum range of motion of the maintenance equipment or personnel during the estimated maximum flight time; the position of the next maintenance point is determined within the maximum motion range.
  • the processor is further configured to estimate the position of the return point according to the state of the power source and/or the working source of the drone; and minimize the power source consumption or the closest flight distance between the maximum motion range and the estimated return point Point as the next maintenance point.
  • the disclosed flight path planning system and control system of the drone can be implemented in other manners.
  • the flight path planning system and the control system embodiment of the UAV described above are merely illustrative.
  • the division of the module or resource unit is only a logical function division, and the actual implementation may have another The manner of division, for example, multiple resource units or components may be combined or may be integrated into another system, or some features may be omitted or not performed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or resource unit, and may be in an electrical, mechanical or other form.
  • the resource units described as separate components may or may not be physically separated.
  • the components displayed as resource units may or may not be physical resource units, that is, may be located in one place, or may be distributed to multiple networks. On the resource unit. Some or all of the resource units may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
  • each functional resource unit in each embodiment of the present invention may be integrated into one processing resource unit, or each resource unit may exist physically separately, or two or more resource units may be integrated into one resource unit.
  • the above integrated resource unit can be implemented in the form of hardware or in the form of a software function resource unit.
  • the integrated resource unit if implemented in the form of a software functional resource unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the methods of the various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

无人机的飞行路径规划方法及系统，系统包括：一个或多个处理器，单独或协同工作，处理器用于执行方法：获取无人机的预设飞行区域的地理信息（S11）；根据地理信息，将预设飞行区域划分至多个作业区域（S12）；根据多个作业区域，确定无人机在预设飞行区域内的飞行路径（S13）。无人机的控制方法及系统，按照预设飞行路径在预设飞行区域内进行飞行作业（S31）；调整无人机的飞行路径和/或执行的操作，以使无人机适合当前的作业环境或工作状态（S32）。无人机的控制方法及系统，获取无人机的当前维护点的位置（S41）；根据当前维护点的位置，确定下一维护点的位置（S42）；将下一维护点的位置发送给维护设备或人员（S43）。无人机的飞行路径规划方法及系统，显示预设飞行区域的地貌图像（S21）；获取用于表示预设飞行区域的地貌图像的边界的多个特征点的坐标（S22）；根据多个特征点的坐标，确定预设飞行区域的边缘线（S23）。

Description

一种无人机的飞行路径规划、控制方法及系统 【技术领域】
本申请涉及无人机领域，特别是涉及一种无人机的飞行路径规划方法及系统、控制方法及系统。
【背景技术】
无人驾驶飞机简称“无人机”，是利用无线电遥控设备和自备的程序控制装置操纵的不载人飞机。无人机可用于农林植物保护作业，具有安全、高效、节省资源等优点。
无人机用于农林植物保护作业，可以由操纵者进行目视遥控，但是其效果并不理想，为此提出了自动作业。目前无人机的自动作业技术不完善，一般只能针对简单地形，而对于复杂地形的作业区域，例如凹凸混合的不规则地形，作业区域中具有复杂障碍物等，不能进行有效处理。此外，无人机的续航时间较短，这大大限制了无人机的应用。
【发明内容】
为了至少部分解决以上问题，本发明提出了一种无人机的飞行路径规划方法，该方法包括：获取无人机的预设飞行区域的地理信息；根据地理信息，将预设飞行区域划分至多个作业区域；根据多个作业区域，确定无人机在预设飞行区域内的飞行路径。
其中，地理信息包括用于表示预设飞行区域的边界的坐标信息，其中边界至少包括用于表示预设飞行区域的外部轮廓的第一边界。
其中，边界进一步包括用于表示预设飞行区域内的障碍物区域的外部轮廓的第二边界。
其中，获取无人机的预设飞行区域的地理信息的步骤包括：获取用于表示 边界的经纬度坐标；将经纬度坐标转换为二维坐标。
其中，将经纬度坐标转换为二维坐标的步骤包括：将经纬度坐标转换为地心坐标系下的三维坐标；将三维坐标转换为与地球表面相切的平面坐标系下的二维坐标。
其中，根据地理信息，将预设飞行区域划分至多个作业区域的步骤包括：根据边界的位置信息和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段；根据航线段与边界的相交位置，将航线段划分至多个作业区域。
其中，相邻两个航线段之间的间距等于无人机的作业宽度，每个航线段的端点位于边界上。
其中，无人机的飞行方向根据预设飞行区域的风向确定。
其中，根据航线段与边界的相交位置，将航线段划分至多个作业区域的步骤包括：获取平行于无人机的飞行方向的直线与边界的相切点；根据相切点将边界划分为多个边缘段；将两侧端点分别位于相同边缘段上的航线段划分至同一作业区域。
其中，根据多个作业区域，确定无人机在预设飞行区域内的飞行路径的步骤包括：确定可作为各作业区域的入口点或出口点的端口点；根据作业区域的端口点，确定无人机的飞行路径。
其中，确定可作为各作业区域的入口点或出口点的端口点的步骤包括：将同一作业区域的位于最外侧的航线段的两侧端点作为端口点。
其中，根据作业区域的端口点确定无人机的飞行路径的步骤包括：根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗，其中非常规消耗至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗；选择非常规消耗最小的一种候选连接路径作为无人机的飞行路径。
其中，根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连 接路径的非常规消耗的步骤包括：确定作业区域之间的候选作业区域连接方式；根据候选作业区域连接方式确定无人机的起始点与作业区域的端口点之间以及作业区域的端口点之间的候选端口连接方式；根据候选端口连接方式，计算非常规消耗。
其中，确定作业区域之间的候选作业区域连接方式的步骤包括：通过排列组合方式或根据作业区域之间的邻接关系确定候选作业区域连接方式，其中根据作业区域之间的邻接关系确定候选作业区域连接方式包括确定遍历所有作业区域且连接非邻接作业区域次数最少的多个候选作业区域连接方式。
其中，根据候选作业区域连接方式确定无人机的起始点与作业区域的端口点之间以及区段的端口点之间的候选端口连接方式的步骤包括：对于每一种候选作业区域连接方式，根据各作业区域的端口点以及作业区域内包括的航线段的数量确定作业区域的候选入口点和候选出口点；根据作业区域的候选入口点和候选出口点确定候选端口连接方式。
其中，对于每一种候选作业区域连接方式，根据各作业区域的端口点以及作业区域内包括的航线段的数量确定作业区域的候选入口点和候选出口点的步骤包括：对于第一个作业区域，选择离起始点最近的端口点作为第一个作业区域的候选入口点，并根据第一个作业区域包括的航线段的数量和第一个作业区域的候选入口点确定第一个作业区域的候选出口点；对于其余作业区域，选择当前作业区域距离上一个作业区域的候选出口点最近的端口点作为当前作业区域的候选入口点，并根据当前作业区域包括的航线段的数量和当前作业区域的候选入口点确定当前作业区域的候选出口点。
其中，预设飞行区域内存在至少一个障碍物区域；根据候选端口连接方式计算非常规消耗的步骤包括：将连接每个作业区域的候选入口点与前一作业区域的候选出口点或无人机的起始点的线段与障碍物区域的边缘求交集；若交集为空，则将沿线段所产生的路程消耗作为两个作业区域之间或作业区域与无人机的起始点之间的路程消耗，否则将线段与障碍物区域的交集部分所产生的路 程消耗替换为规避障碍物区域所产生的路程消耗，并计算规避障碍物区域所产生的路程消耗与线段的非交集部分所产生的路程消耗之和以作为两个作业区域之间或作业区域与无人机的起始点之间的路程消耗。
其中，规避障碍物区域所产生的路程消耗包括：通过绕行方式绕过障碍物区域所产生的路程消耗或者通过升高或降低方式跨越或穿越障碍物区域所产生的路程消耗。
其中，非常规消耗进一步包括返航维护的路程消耗；根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗进一步包括：根据无人机的动力源和/或作业源的状态计算应进行返航维护的返航点；根据返航点的坐标和维护点的坐标计算返航维护的路程消耗。
其中，根据无人机的工作状态计算应进行返航维护的返航点的步骤包括：依次判断航线段的当前端点是否满足续航条件，若不满足续航条件，则将当前端点作为返航点。
其中，根据无人机的工作状态计算应进行返航维护的返航点的步骤包括：依次判断航线段的当前端点是否满足续航条件；若不满足续航条件，则判断当前端点与下一端点之间是否为需作业的航线段；若是，则在当前端点与下一端点之间的航线段上寻找返航点，以使得无人机飞到返航点后仍能够安全返回维护点，若否，则将当前端点作为返航点。
其中，续航条件是指无人机飞到当前端点的动力源和/或作业源的剩余量减去预设的安全量大于或等于从当前端点飞往下一端点且从下一端点飞往返航点的动力源和/或作业源消耗量。
其中，根据返航点的坐标和维护点的坐标计算返航维护的路程消耗的步骤包括：根据无人机的剩余飞行时间、维护设备或人员的运动路径、当前位置以及运动速度计算维护设备或人员在无人机的剩余飞行时间内的最大运动范围；在最大运动范围内指定维护点。
其中，在最大运动范围内指定维护点的步骤包括：将最大运动范围内与返 航点或下一端点之间的动力源和/或作业源消耗最小或飞行距离最近的点作为维护点。
其中，进一步包括：获取用于表示预设飞行区域中的特殊作业区域的边缘的特殊边缘点的坐标；计算航线段与特殊作业区域的边缘的交点；将航线段与特殊作业区域的边缘的交点插入飞行路径，以使无人机在特殊作业区域内执行的操作不同于无人机在特殊作业区域外执行的操作。
其中，特殊作业区域包括可飞行的非作业区域、高空飞行区域和低空飞行区域中的至少一种。
其中，进一步包括：形成用于无人机的飞行路径的多个任务点的坐标序列，以使得无人机按照坐标序列在多个任务点之间进行作业，其中任务点至少包括航线段的端点、航线段与特殊作业区域的边缘的交点。
为了至少部分解决以上问题，本发明提出了一种无人机的控制方法，该方法包括：按照预设飞行路径在预设飞行区域内进行飞行作业；调整无人机的飞行路径和/或执行的操作，以使无人机适合当前的作业环境或工作状态。
其中，调整无人机的飞行路径包括如下至少一种：改变飞行方向、改变飞行高度、停止飞行；调整无人机执行的操作包括如下任意一种：停止作业，开始作业。
其中，作业环境包括如下至少一种：前方存在障碍物、前方存在非作业区域、前方为已作业区域；工作状态包括如下至少一种：动力源不足、作业源不足、导航信号丢失、接收到控制命令。
其中，工作状态包括动力源不足和/或作业源不足，调整无人机的飞行路径包括改变飞行方向以飞往维护点；调整无人机的飞行路径和/或执行的操作，以使无人机适合当前的作业环境或工作状态的步骤包括：获取无人机的动力源和/或作业源的状态；根据无人机的动力源和/或作业源状态计算应进行返航维护的返航点；在无人机飞行至返航点时改变飞行方向以飞往维护点进行维护。
其中，获取无人机的动力源和/或作业源的状态的步骤包括：
获取无人机的当前位置坐标；判断无人机的当前位置坐标是否对应用于表示无人机飞行路径的多个任务点中的某个任务点；若是，则获取无人机的动力源和/或作业源的状态。
其中，根据无人机的动力源和/或作业源的状态计算应进行返航维护的返航点的步骤包括：判断无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则将无人机的当前位置作为返航点。
其中，根据无人机的动力源和/或作业源的状态计算应进行返航维护的返航点的步骤包括：判断无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则判断无人机的当前位置与无人机的当前位置对应的任务点的下一任务点之间是否为需作业的航线段；若是，则在无人机的当前位置与下一任务点之间的航线段上寻找返航点，以使得无人机飞到返航点后仍能够安全返回维护点，若否，则将无人机的当前位置作为返航点。
其中，续航条件是指无人机的动力源和/或作业源的剩余量减去预设的安全量大于或等于从无人机的当前位置飞往无人机的当前位置对应的任务点的下一任务点的动力源和/或作业源的消耗。
其中，进一步包括：根据无人机的动力源和/或作业源的状态预估无人机的剩余飞行时间；根据维护设备或人员的当前位置、运动路径以及运动速度计算维护设备或人员在预估剩余飞行时间内的最大运动范围；在最大运动范围内指定维护点。
其中，在最大运动范围内指定维护点的步骤包括：根据无人机的动力源和/或作业源的状态预估返航点的位置；将最大运动范围内与预估的返航点之间动力源消耗最小或飞行距离最近的点作为维护点。
为了至少部分解决以上问题，本发明提出了一种无人机的控制方法，该方法包括：获取无人机的当前维护点的位置；根据当前维护点的位置，确定下一维护点的位置；将下一维护点的位置发送给维护设备或人员。
其中，当前维护点为无人机上次进行维护的维护点；根据当前维护点的位 置，确定下一维护点的位置的步骤包括：根据当前维护点的位置、无人机的预估最大飞行时间、维护设备或人员的运动路径以及运动速度计算维护设备或人员在预估最大飞行时间内的最大运动范围；在最大运动范围内确定下一维护点的位置。
其中，在最大运动范围内确定下一维护点的位置的步骤包括：根据当前维护点的位置以及无人机的飞行路径和预估最大飞行时间，预估无人机从当前维护点起下一次应进行返航维护的下一返航点；将最大运动范围内与下一返航点之间动力源消耗最小或飞行距离最近的点作为下一维护点。
其中，当前维护点的位置为维护设备或人员的当前位置；根据当前维护点的位置，确定下一维护点的位置的步骤包括：根据无人机的动力源和/或作业源的状态预估无人机的剩余飞行时间；根据维护设备或人员的当前位置、运动路径以及运动速度计算维护设备或人员在预估最大飞行时间内的最大运动范围；在最大运动范围内确定下一维护点的位置。
其中，在最大运动范围内确定下一维护点的位置的步骤包括：根据无人机的动力源和/或作业源的状态预估返航点的位置；将最大运动范围内与预估的返航点之间动力源消耗最小或飞行距离最近的点作为下一维护点。
为了至少部分解决以上问题，本发明提出了一种无人机的飞行路径规划方法，该方法包括：显示预设飞行区域的地貌图像；获取用于表示预设飞行区域的地貌图像的边界的多个特征点的坐标；根据多个特征点的坐标，确定预设飞行区域的边缘线。
其中，特征点包括用于表示预设飞行区域的外部轮廓的多个第一特征点，以及用于表示预设飞行区域内障碍物区域的外部轮廓的第二特征点。
其中，根据多个特征点的坐标，确定预设飞行区域的边缘线的步骤包括：利用线段连接特征点并将形成的折线作为边缘线。
其中，根据多个特征点的坐标，确定预设飞行区域的边缘线的步骤包括：以特征点中的至少一个作为圆心作圆或者椭圆，并将圆或者椭圆的全部或者部 分作为边缘线。
其中，获取用于表示预设飞行区域的地貌图像的边界的多个特征点的坐标的步骤包括：通过输入装置接收用户输入的多个特征点的坐标；或通过图像识别从地貌图像中提取多个特征点的坐标。
其中，进一步包括：根据边缘线，确定无人机在预设飞行区域内的飞行路径；显示飞行路径。
其中，根据边缘线，确定无人机在预设飞行区域内的飞行路径的步骤之前进一步包括：获取无人机的作业参数；根据边缘线，确定无人机在预设飞行区域内的飞行路径的步骤包括：根据边缘线和无人机的作业参数，确定无人机在预设飞行区域内的飞行路径。
其中，作业参数至少包括风向、无人机的作业宽度和起始点。
其中，根据边缘线，确定无人机在预设飞行区域内的飞行路径的步骤包括：根据边缘线和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段，其中无人机的飞行方向根据风向确定；根据航线段与边界的相交位置，将预设飞行区域划分至多个作业区域；根据多个作业区域，确定无人机在预设飞行区域内的飞行路径。
其中，根据多个作业区域，确定无人机在预设飞行区域内的飞行路径的步骤包括：确定可作为各作业区域的入口点或出口点的端口点；根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗，其中非常规消耗至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗；选择非常规消耗最小的一种候选连接路径作为无人机的飞行路径。
其中，非常规消耗进一步包括返航维护的路程消耗；根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗进一步包括：根据无人机的工作状态计算应进行返航维护的返航点；根据返航点的坐标和维护点的坐标计算返航维护的路程消耗。
其中，显示飞行路径的步骤包括：显示飞行路径，并显示返航点的数量和/ 或在飞行路径中标出返航点。
为了至少部分解决以上问题，本发明提出了一种无人机的飞行路径规划系统，该系统包括：一个或多个处理器，单独或协同工作，处理器用于：获取无人机的预设飞行区域的地理信息；根据地理信息，将预设飞行区域分隔为多个作业区域；根据多个作业区域，确定无人机在预设飞行区域内的飞行路径。
其中，一步包括：传感器，传感器与处理器通讯连接；传感器用于捕捉预设飞行区域的地理信息，并将预设飞行区域的地理信息传送给处理器。
其中，地理信息包括用于表示预设飞行区域的边界的坐标信息，其中边界至少包括用于表示预设飞行区域的外部轮廓的第一边界。
其中，边界进一步包括用于表示预设飞行区域内的障碍物区域的外部轮廓的第二边界。
其中，处理器进一步用于获取用于表示边界的经纬度坐标；将经纬度坐标转换为二维坐标。
其中，处理器进一步用于将经纬度坐标转换为地心坐标系下的三维坐标；将三维坐标转换为与地球表面相切的平面坐标系下的二维坐标。
其中，处理器进一步用于根据边界的位置信息和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段；根据航线段与边界的相交位置，将航线段划分至多个作业区域。
其中，相邻两个航线段之间的间距等于无人机的作业宽度，每个航线段的端点位于边界上。
其中，无人机的飞行方向根据预设飞行区域的风向确定。
其中，处理器进一步用于获取平行于无人机的飞行方向的直线与边界的相切点；根据相切点将边界划分为多个边缘段；将两侧端点分别位于相同边缘段上的航线段划分至同一作业区域。
其中，处理器进一步用于确定可作为各作业区域的入口点或出口点的端口点；根据作业区域的端口点，确定无人机的飞行路径。
其中，处理器进一步用于将同一作业区域的位于最外侧的航线段的两侧端点作为端口点。
其中，处理器进一步用于根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗，其中非常规消耗至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗；选择非常规消耗最小的一种候选连接路径作为无人机的飞行路径。
其中，处理器进一步用于确定作业区域之间的候选作业区域连接方式；根据候选作业区域连接方式确定无人机的起始点与作业区域的端口点之间以及作业区域的端口点之间的候选端口连接方式；根据候选端口连接方式，计算非常规消耗。
其中，处理器进一步用于通过排列组合方式或根据作业区域之间的邻接关系确定候选作业区域连接方式，其中根据作业区域之间的邻接关系确定候选作业区域连接方式包括确定遍历所有作业区域且连接非邻接作业区域次数最少的多个候选作业区域连接方式。
其中，处理器进一步用于对于每一种候选作业区域连接方式，根据各作业区域的端口点以及作业区域内包括的航线段的数量确定作业区域的候选入口点和候选出口点；根据作业区域的候选入口点和候选出口点确定候选端口连接方式。
其中，处理器进一步用于对于第一个作业区域，选择离起始点最近的端口点作为第一个作业区域的候选入口点，并根据第一个作业区域包括的航线段的数量和第一个作业区域的候选入口点确定第一个作业区域的候选出口点；对于其余作业区域，选择当前作业区域距离上一个作业区域的候选出口点最近的端口点作为当前作业区域的候选入口点，并根据当前作业区域包括的航线段的数量和当前作业区域的候选入口点确定当前作业区域的候选出口点。
其中，预设飞行区域内存在至少一个障碍物区域；处理器进一步用于将连接每个作业区域的候选入口点与前一作业区域的候选出口点或无人机的起始点 的线段与障碍物区域的边缘求交集；若交集为空，则将沿线段所产生的路程消耗作为两个作业区域之间或作业区域与无人机的起始点之间的路程消耗，否则将线段与障碍物区域的交集部分所产生的路程消耗替换为规避障碍物区域所产生的路程消耗，并计算规避障碍物区域所产生的路程消耗与线段的非交集部分所产生的路程消耗之和以作为两个作业区域之间或作业区域与无人机的起始点之间的路程消耗。
其中，规避障碍物区域所产生的路程消耗包括：通过绕行方式绕过障碍物区域所产生的路程消耗或者通过升高或降低方式跨越或穿越障碍物区域所产生的路程消耗。
其中，非常规消耗进一步包括返航维护的路程消耗；处理器进一步用于根据无人机的动力源和/或作业源的状态计算应进行返航维护的返航点；根据返航点的坐标和维护点的坐标计算返航维护的路程消耗。
其中，处理器进一步用于依次判断航线段的当前端点是否满足续航条件，若不满足续航条件，则将当前端点作为返航点。
其中，处理器进一步用于依次判断航线段的当前端点是否满足续航条件；若不满足续航条件，则判断当前端点与下一端点之间是否为需作业的航线段；若是，则在当前端点与下一端点之间的航线段上寻找返航点，以使得无人机飞到返航点后仍能够安全返回维护点，若否，则将当前端点作为返航点。
其中，续航条件是指无人机飞到当前端点的动力源和/或作业源的剩余量减去预设的安全量大于或等于从当前端点飞往下一端点且从下一端点飞往返航点的动力源和/或作业源消耗量。
其中，处理器进一步用于根据无人机的剩余飞行时间、维护设备或人员的运动路径、当前位置以及运动速度计算维护设备或人员在无人机的剩余飞行时间内的最大运动范围；在最大运动范围内指定维护点。
其中，处理器进一步用于将最大运动范围内与返航点或下一端点之间的动力源和/或作业源消耗最小或飞行距离最近的点作为维护点。
其中，处理器进一步用于获取用于表示预设飞行区域中的特殊作业区域的边缘的特殊边缘点的坐标；计算航线段与特殊作业区域的边缘的交点；将航线段与特殊作业区域的边缘的交点插入飞行路径，以使无人机在特殊作业区域内执行的操作不同于无人机在特殊作业区域外执行的操作。
其中，特殊作业区域包括可飞行的非作业区域、高空飞行区域和低空飞行区域中的至少一种。
其中，处理器进一步用于形成用于无人机的飞行路径的多个任务点的坐标序列，以使得无人机按照坐标序列在多个任务点之间进行作业，其中任务点至少包括航线段的端点、航线段与特殊作业区域的边缘的交点。
为了至少部分解决以上问题，本发明提出了一种无人机的控制系统，该系统包括：一个或多个处理器，单独或协同工作，处理器用于：按照预设航线在预设飞行区域内进行飞行作业；调整无人机的飞行路径和/或执行的操作，以使无人机适合当前的作业环境或工作状态。
其中，进一步包括定位装置，定位装置与处理器通讯连接；定位装置用于获取无人机的当前位置信息，并将当前位置信息传送给处理器，处理器根据当前位置信息以及预设航线控制无人机进行飞行作业。
其中，调整无人机的飞行路径包括如下至少一种：改变飞行方向、改变飞行高度、停止飞行；调整无人机执行的操作包括如下任意一种：停止作业，开始作业。
其中，作业环境包括如下至少一种：前方存在障碍物、前方存在非作业区域、前方为已作业区域；工作状态包括如下至少一种：动力源不足、作业源不足、导航信号丢失、接收到控制命令。
其中，工作状态包括动力源不足和/或作业源不足，调整无人机的飞行路径包括改变飞行方向以飞往维护点；处理器进一步用于获取无人机的动力源和/或作业源的状态；根据无人机的动力源和/或作业源状态计算应进行返航维护的返航点；在无人机飞行至返航点时改变飞行方向以飞往维护点进行维护。
其中，处理器进一步用于获取无人机的当前位置坐标；判断无人机的当前位置坐标是否对应用于表示无人机飞行路径的多个任务点中的某个任务点；若是，则获取无人机的动力源和/或作业源的状态。
其中，处理器进一步用于判断无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则将无人机的当前位置作为返航点。
其中，处理器进一步用于判断无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则判断无人机的当前位置与无人机的当前位置对应的任务点的下一任务点之间是否为需作业的航线段；若是，则在无人机的当前位置与下一任务点之间的航线段上寻找返航点，以使得无人机飞到返航点后仍能够安全返回维护点，若否，则将无人机的当前位置作为返航点。
其中，续航条件是指无人机的动力源和/或作业源的剩余量减去预设的安全量大于或等于从无人机的当前位置飞往无人机的当前位置对应的任务点的下一任务点的动力源和/或作业源的消耗。
其中，处理器进一步用于根据无人机的动力源和/或作业源的状态预估无人机的剩余飞行时间；根据维护设备或人员的当前位置、运动路径以及运动速度计算维护设备或人员在预估剩余飞行时间内的最大运动范围；在最大运动范围内指定维护点。
其中，处理器进一步用于根据无人机的动力源和/或作业源的状态预估返航点的位置；将最大运动范围内与预估的返航点之间动力源消耗最小或飞行距离最近的点作为维护点。
为了至少部分解决以上问题，本发明提出了一种无人机的控制系统，该系统包括：一个或多个处理器，单独或协同工作，处理器用于：获取无人机的当前维护点的位置；根据当前维护点的位置，确定下一维护点的位置；将下一维护点的位置发送给维护设备或人员。
其中，当前维护点为无人机上次进行维护的维护点；处理器进一步用于根据当前维护点的位置、无人机的预估最大飞行时间、维护设备或人员的运动路 径以及运动速度计算维护设备或人员在预估最大飞行时间内的最大运动范围；在最大运动范围内确定下一维护点的位置。
其中，处理器进一步用于根据当前维护点的位置以及无人机的飞行路径和预估最大飞行时间，预估无人机从当前维护点起下一次应进行返航维护的下一返航点；将最大运动范围内与下一返航点之间动力源消耗最小或飞行距离最近的点作为下一维护点。
其中，当前维护点的位置为维护设备或人员的当前位置；处理器进一步用于根据无人机的动力源和/或作业源的状态预估无人机的剩余飞行时间；根据维护设备或人员的当前位置、运动路径以及运动速度计算维护设备或人员在预估最大飞行时间内的最大运动范围；在最大运动范围内确定下一维护点的位置。
其中，处理器进一步用于根据无人机的动力源和/或作业源的状态预估返航点的位置；将最大运动范围内与预估的返航点之间动力源消耗最小或飞行距离最近的点作为下一维护点。
为了至少部分解决以上问题，本发明提出了一种无人机的飞行路径规划系统，该系统包括：显示屏，用于显示预设飞行区域的地貌图像；一个或多个处理器，单独或协同工作，处理器与显示屏通讯连接；处理器用于获取用于表示预设飞行区域的地貌图像的边界的多个特征点的坐标；根据多个特征点的坐标信息，确定预设飞行区域的边缘线。
其中，进一步包括输入装置，输入装置与处理器通讯连接，用于接收用于输入的表示预设飞行区域的地貌图像的边界的多个特征点并获取其坐标。
其中，处理器进一步用于通过图像识别从地貌图像中提取多个特征点的坐标。
其中，特征点包括用于表示预设飞行区域的外部轮廓的多个第一特征点，以及用于表示预设飞行区域内障碍物区域的外部轮廓的第二特征点。
其中，处理器进一步用于利用线段连接多个特征点并将形成的折线作为边缘线。
其中，处理器进一步用于以特征点中的至少一个作为圆心作圆或者椭圆，并将圆或者椭圆的全部或者部分作为边缘线。
其中，处理器进一步用于根据边缘线，确定无人机在预设飞行区域内的飞行路径；显示飞行路径。
其中，处理器进一步用于获取无人机的作业参数；根据边缘线和无人机的作业参数，确定无人机在预设飞行区域内的飞行路径。
其中，作业参数至少包括风向、无人机的作业宽度和起始点。
其中，处理器进一步用于根据边缘线和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段，其中无人机的飞行方向根据风向确定；根据航线段与边界的相交位置，将预设飞行区域划分至多个作业区域；根据多个作业区域，确定无人机在预设飞行区域内的飞行路径。
其中，处理器进一步用于确定可作为各作业区域的入口点或出口点的端口点；根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗，其中非常规消耗至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗；选择非常规消耗最小的一种候选连接路径作为无人机的飞行路径。
其中，非常规消耗进一步包括返航维护的路程消耗；处理器进一步用于根据无人机的工作状态计算应进行返航维护的返航点；根据返航点的坐标和维护点的坐标计算返航维护的路程消耗。
其中，处理器进一步用于控制显示屏显示飞行路径，并显示返航点的数量和/或在飞行路径中标出返航点。
本发明的有益效果是：通过将预设飞行区域划分至多个作业区域，再根据多个作业区域确定无人机的飞行路径，可以处理复杂地形预设飞行区域的飞行路径规划，并减少飞行路径规划的计算量。
【附图说明】
图1是本发明无人机的飞行路径规划方法第一实施例的流程图；
图2是本发明无人机的飞行路径规划方法第二实施例的流程图；
图3是本发明无人机的飞行路径规划方法第三实施例的流程图；
图4是本发明无人机的飞行路径规划方法一实施例中划分作业区域的示意图；
图5是本发明无人机的飞行路径规划方法第四实施例的流程图；
图6是本发明无人机的飞行路径规划方法第五实施例的流程图；
图7是本发明无人机的飞行路径规划方法一实施例中不同作业区域之间的连通关系示意图；
图8是本发明无人机的飞行路径规划方法一实施例中绕行规避障碍物区域的示意图；
图9是本发明无人机的飞行路径规划方法一实施例中计算候选连接路径的返航维护消耗的流程图；
图10是本发明无人机的飞行路径规划方法第六实施例的流程图；
图11是本发明无人机的飞行路径规划方法第七实施例的流程图；
图12是本发明无人机的飞行路径规划方法第八实施例的流程图；
图13是本发明无人机的飞行路径规划方法第九实施例的流程图；
图14是本发明无人机的控制方法第一实施例的流程图；
图15是本发明无人机的控制方法第二实施例的流程图；
图16是本发明无人机的控制方法第三实施例的流程图；
图17是本发明无人机的控制方法第四实施例的流程图；
图18是本发明无人机的飞行路径规划系统第一实施例的结构示意图；
图19是本发明无人机的飞行路径规划系统第二实施例的结构示意图；
图20是本发明无人机的飞行路径规划系统第三实施例的结构示意图；
图21是本发明无人机的飞行路径规划系统第四实施例的结构示意图；
图22是本发明无人机的控制系统第一实施例的结构示意图；
图23是本发明无人机的控制系统第二实施例的结构示意图；
图24是本发明无人机的控制系统第三实施例的结构示意图。
【具体实施方式】
下面结合附图和实施例对本发明进行详细说明。在不冲突的情况下，下述的实施例及实施例中的特征可以相互组合。
如图1所示，本发明无人机的飞行路径规划方法第一实施例包括：
S11：获取无人机的预设飞行区域的地理信息。
地理信息包括用于表示预设飞行区域的边界的坐标信息。在其中一个实施例中，为了便于标示边界，坐标信息可以包括用于表示预设飞行区域边界的多个特征点的坐标信息。可以通过输入装置接收用户输入的特征点的坐标信息，也可以通过图像识别从预设飞行区域的地貌图像中获取特征点的坐标信息。
特征点可以在边界上，也可以不在边界上。可以采用直线段或者曲线段连接在边界上的特征点并作为全部或部分边界；也可以以不在边界上的特征点为圆心作圆或椭圆，并将圆或椭圆的全部或者部分作为全部或部分边界。
边界至少包括用于表示预设飞行区域的外部轮廓的第一边界。在本发明无人机的飞行路径规划方法一实施例中，当预设飞行区域内存在障碍物区域时，边界进一步包括用于表示该障碍物区域的外部轮廓的第二边界。这里的障碍物区域是指无人机需要绕行的有障碍物的区域，例如禁飞区、存在房子、电线杆等不宜飞越的障碍物的区域。
S12：根据地理信息，将预设飞行区域划分至多个作业区域。
根据边界的位置信息和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段，航线段为规划中的无人机在飞行区域内的作业路径。根据航线段与边界的相交位置，即航线段的端点位置，将航线段划分至多个作业区域。一般而言，参考无人机的作业特点，包括少转弯、就近飞行、遍历预设飞行区域等，对预设飞行区域进行分块。
一般而言，航线段的端点位于边界上，相邻两个航线段之间的间距等于无人机的作业宽度。无人机的飞行方向可以根据预设飞行区域的风向确定，也可以采用其他方式确定无人机的飞行方向，例如以预设飞行区域的最长边的方向作为无人机的飞行方向。
S13：根据多个作业区域，确定无人机在预设飞行区域内的飞行路径。
一般而言，寻找能够遍历所有作业区域，并且非常规消耗最小的路径作为无人机的飞行路径，非常规消耗是指无人机飞行作业之外的消耗，至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗。
通过上述实施例的实施，将预设飞行区域划分至多个作业区域，再根据多个作业区域确定无人机的飞行路径，可以处理复杂地形预设飞行区域的飞行路径规划，并减少飞行路径规划的计算量。
如图2所示，本发明无人机的飞行路径规划方法第二实施例，是在本发明无人机的飞行路径规划方法第一实施例的基础上，步骤S11具体包括：
S111：获取用于表示边界的经纬度坐标。
获取用于表示边界的特征点的坐标为经纬度坐标。
S112：将经纬度坐标转换为地心坐标系下的三维坐标。
将地球看作一个球体，将经纬度坐标转换为以地心为原点的三维直角地心坐标系下的三维坐标。
S113：将三维坐标转换为与地球表面相切的平面坐标系下的二维坐标。
平面与地球的切点可以为起始点，也可以为预设飞行区域内部的或者边界上的点。
下面举例说明具体计算过程：特征点1的经纬度坐标为p1(α，β)，其中α表示经度，β表示纬度，将经纬度坐标转换为地心坐标系OXYZ下的三维坐标p2(X，Y，Z)，其中X＝R*cos(β*TO_RADIAN)*cos(α*TO_RADIAN)，Y＝R*cos(β*TO_RADIAN)*sin(α*TO_RADIAN)，Z＝R*sin(β*TO_RADIAN)，R为地球半径，TO_RANDIAN＝pi/180，pi为圆周率。将p2(X，Y，Z)绕OZ旋转-α0 度，绕OX旋转-(90-β0)度，其中(α0，β0)为平面与地球切点的经纬度坐标，获得以指向切点的方向为z轴的三维坐标系oxyz下的坐标p3(x，y，z)，再将坐标p3(x，y，z)坐标向切平面oxy投影获得平面坐标p4(x，y)。
可以以经过切点的经线和纬线在切平面上的投影作为x轴和y轴，也可以采用其他向量作为x轴和y轴。当然，也可以求出从地心坐标系OXYZ转换到平面坐标系oxy的转换矩阵，然后将地心坐标系OXYZ下的三维坐标与转换矩阵相乘以得到平面坐标。
一般而言，预设飞行区域相比整个地球面积很小，可以近似为平面，如果特征点的坐标采用经纬度坐标，后续飞行路径规划的计算过程非常复杂，甚至可能带来更大的误差，为此将经纬度坐标投影至地球表面相切的平面坐标，可有效的减少后续的计算量。
如图3所示，本发明无人机的飞行路径规划方法第三实施例，是在本发明无人机的飞行路径规划方法第一实施例的基础上，步骤S12具体包括：
S121：根据边界的位置信息和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段。
一般而言，航线段为飞行路径的一部分，无人机需要沿着航线段飞行并在需要时执行作业。相邻两个航线段之间的间距等于无人机的作业宽度，每个航线段的端点位于边界上。无人机的飞行方向根据预设飞行区域的风向确定。
S122：获取平行于无人机的飞行方向的直线与边界的相切点。
S123：根据相切点将边界划分为多个边缘段。
将相切点按照邻接关系排序，每两个相切点之间的边界为一个边缘段。
S124：将两侧端点分别位于相同边缘段上的航线段划分至同一作业区域。
结合附图举例具体说明如何划分作业区域，如图4所示，预设飞行区域内部存在需要无人机绕行的障碍物。用于表示预设飞行区域边界的多个特征点(图中未画出)均位于边界上，特征点包括位于预设飞行区域外部轮廓上的第一特征点和位于障碍物外部轮廓上的第二特征点。
设置一用于表示垂直于无人机飞行方向的横向平移方向的第一向量
Figure PCTCN2016092398-appb-000001
将第一向量
Figure PCTCN2016092398-appb-000002
分别与特征点进行向量内积，以获得第一内积值，即特征点在第一向量
Figure PCTCN2016092398-appb-000003
上的投影。为减少计算量，进行向量内积时可以不对用于表示完全位于预设飞行区域内部的障碍物外部轮廓的第二特征点进行内积，即只将第一特征点与第一向量
Figure PCTCN2016092398-appb-000004
进行向量内积。然后将第一内积值的最小值min对应的特征点A点作为最小边界点，第一内积值的最大值max对应的特征点B点作为最大边界点。
然后，计算最大边界点和最小边界点之间的平行于无人机的飞行方向的多条直线与预设飞行区域边界的交点，相邻直线之间的间距等于无人机的作业宽度。具体而言，无人机用于喷洒农药时，其作业宽度为其喷幅，即无人机在指定的高度以及喷洒方向下，在地面的有效喷洒宽度。需要注意的是，如果预设飞行区域中存在障碍物，则求交点时必须同时考虑第一边界和第二边界。根据多条直线与预设飞行区域边界的交点，将多条直线位于预设飞行区域内的线段作为可飞行的航线段，例如图4中块4、块5、块6、块7内的虚线段。具体而言，设置一用于表示无人机飞行方向的第二向量
Figure PCTCN2016092398-appb-000005
本步骤也可以早于或者与设置第一向量
Figure PCTCN2016092398-appb-000006
的步骤同时完成。将第二向量分别与每条直线的交点的坐标进行向量内积，以获得第二内积值。不考虑相切的情况，一条直线与预设飞行区域边界的交点的个数m为偶数。将n个交点按照第二内积值的大小排序后记为点集[0，m-1]，则线段[0，1]、[2，3]、[4，5]......[m-2，m-1]为可飞行的航线段，[1，2]、[3，4]等为障碍物区域内部线段，对每条直线重复上述步骤，即可获得所有航线段。
获取平行于无人机的飞行方向的直线与边界的相切点。具体而言，对于每个特征点，分别计算其指向两侧相邻特征点所定义的第三向量和第四向量，例如图4中从A点指向两侧的向量a和向量b。将第一向量
Figure PCTCN2016092398-appb-000007
分别与第三向量和第四向量进行内积，以获得第三内积值和第四内积值。判断第三内积值和第四内积值是否同号，若同号，即同时为正或同时为负，则该特征点为相切点， 例如图4中的A点。
找出所有相切点，根据相切点将边界划分为多个边缘段，如图4中的L1-L8。需要注意的是，本实施例中的经过相切点的平行于无人机的飞行方向的直线与边界的交点数量可以为1，也可以为大于1的奇数。
将航线段按照与第一向量
Figure PCTCN2016092398-appb-000008
的内积值大小顺序排序，然后依次对排序后的航线段进行划分，将两侧端点分别位于相同边缘段上的航线段划分至同一作业区域。图4中将预设飞行区域分成了8块作业区域，用块1-8表示，其中分隔不同作业区域的实线仅为示意，并不是必须画出的。
为了进一步简化计算，在其中一个实施例中，可以在将特征点的经纬度坐标转换为平面坐标的过程中，以第一向量
Figure PCTCN2016092398-appb-000009
作为平面坐标系的x轴，以作为第二向量
Figure PCTCN2016092398-appb-000010
作为平面坐标系的y轴；或者以第一向量
Figure PCTCN2016092398-appb-000011
作为平面坐标系的y轴，以作为第二向量
Figure PCTCN2016092398-appb-000012
作为平面坐标系的x轴。此时上述获取航线段以及将航线段划分至作业区域的计算过程中，任意对象与第一向量
Figure PCTCN2016092398-appb-000013
或第二向量
Figure PCTCN2016092398-appb-000014
向量内积的计算过程可简化为取该对象的x值或y值的过程。
本实施例可以与以上任一实施例相结合。
如图5所示，本发明无人机的飞行路径规划方法第四实施例，是在本发明无人机的飞行路径规划方法第一实施例的基础上，步骤S13具体包括：
S131：确定可作为各作业区域的入口点或出口点的端口点。
一般而言，将同一作业区域的位于最外侧的航线段的两侧端点作为端口点，例如，一个类似矩形的作业区域可以具有4个端口点。
S132：根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗。
非常规消耗至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗，此外还可以进一步包括避障消耗和/或返航维护消耗。如果设有终点，非常规消耗进一步从作业区域飞往终点的路程消耗。
S133：选择非常规消耗最小的一种候选连接路径作为无人机的飞行路径。
本实施例可以与以上任一实施例相结合。
如图6所示，本发明无人机的飞行路径规划方法第五实施例，是在本发明无人机的飞行路径规划方法第四实施例的基础上，步骤S132具体包括：
S1321：确定作业区域之间的候选作业区域连接方式。
可以通过排列组合方式来确定候选作业区域连接方式，对于n个作业区域，要遍历所有作业区域，有n！种候选作业区域连接方式，随着作业区域数量的增加，计算量急剧增大。为减少计算量，也可以根据作业区域之间的邻接关系确定候选作业区域连接方式，即以遍历所有作业区域且连接非邻接作业区域次数最少的多个作业区域连接方式作为候选作业区域连接方式。仍以图4中的预设飞行区域为例，画出图4中不同作业区域之间的连通关系图，如图7所示。图7中用线连接的两个作业区域表示这两个作业区域是邻接的，未用线连接的两个作业区域表示这两个作业区域是非邻接的。可将根据作业区域之间的邻接关系确定候选作业区域连接方式的问题转化为根据连通关系图求解汉密尔顿路径和/或欧拉路径的问题。
S1322：根据候选作业区域连接方式确定无人机的起始点与作业区域的端口点之间以及作业区域的端口点之间的候选端口连接方式。
对于每一种候选作业区域连接方式，根据各作业区域的端口点以及作业区域内包括的航线段的数量确定作业区域的候选入口点和候选出口点，然后根据作业区域的候选入口点和候选出口点确定候选端口连接方式，一种候选作业区域连接方式的一种候选端口连接方式即为一条候选连接路径。
对于每个作业区域而言，可以采用排列组合方式确定候选入口点和候选出口点，即每个端口点都可以作为候选入口点或候选出口点；也可以分别将每个端口点作为候选入口点，候选出口点根据航线段的数量和候选入口点而决定，即将无人机从候选入口点进入作业区域后按照航线段往返飞行后离开作业区域的端口点作为候选出口点。
为了进一步减少计算量，对于每一种候选作业区域连接方式，可以只采用一种候选端口连接方式，即对于第一个作业区域，选择离起始点最近的端口点作为第一个作业区域的候选入口点，并按照前述方式，根据第一个作业区域包括的航线段的数量和第一个作业区域的候选入口点确定第一个作业区域的候选出口点；对于其余作业区域，选择当前作业区域距离上一个作业区域的候选出口点最近的端口点作为当前作业区域的候选入口点，并按照前述方式，根据当前作业区域包括的航线段的数量和当前作业区域的候选入口点确定当前作业区域的候选出口点。
S1323：根据候选端口连接方式，计算非常规消耗。
对于每种候选端口连接方式，即候选连接路径，计算其非常规消耗。非常规消耗可以包括飞行过程中任何非作业路径的消耗，例如，非常规消耗可以至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗。对于以上路程消耗，可以利用邻接矩阵来计算，邻接矩阵第i行第j列元素表示连接第i块作业区域和第j块作业区域的代价，即无人机在两块作业区域之间的路程消耗。每个作业区域均有四个端口点，则两个作业区域之间的连接方法共有4*4＝16种，邻接矩阵的元素为16维向量，分别表示两个作业区域的任意两个端口点连接的消耗。计算路程消耗时，可以从邻接矩阵中找到对应不同作业区域的指定端口点连接消耗，然后进行累加。起始点/终点可以作为一个特殊的作业区域计入邻接矩阵中，也可以单独计算起始点/终点和作业区域之间的路程消耗。
一般而言，无人机在不同作业区域的端口点之间，以及起始点/终点和作业区域之间可采用直线飞行，如果存在障碍物时，需要进一步考虑避障消耗。避障消耗，即无人机规避障碍物区域所产生的路程消耗包括：通过绕行方式绕过障碍物区域所产生的路程消耗，例如绕过房屋等，和/或通过升高方式跨越或通过降低方式穿越障碍物区域所产生的路程消耗，例如升高以跨越树木等、降低以穿越电线、桥梁等。邻接矩阵的元素中两个作业区域的任意两个端口点连接 的消耗可以为已包括了避障消耗的综合路程消耗。
本实施例可以与以上任一实施例相结合。
在本发明无人机的飞行路径规划方法一实施例中，计算包括了避障消耗的综合路程消耗的具体过程包括：将连接每个作业区域的候选入口点与前一作业区域的候选出口点或无人机的起始点的线段与障碍物区域的边缘求交集。若交集为空，则意味着没有障碍物，无须考虑避障消耗，将沿线段所产生的路程消耗作为两个作业区域之间或作业区域与无人机的起始点之间的路程消耗。若交集不为空，则意味着有障碍物，将线段与障碍物区域的交集部分所产生的路程消耗替换为规避障碍物区域所产生的路程消耗，并计算规避障碍物区域所产生的路程消耗与线段的非交集部分所产生的路程消耗之和以作为两个作业区域之间或作业区域与无人机的起始点之间的综合路程消耗。举例说明，如图8所示，作业区域i的端口3和作业区域j的端口1之间的线段与虚线表示的障碍物区域有交集，将线段在虚线内的部分替换为沿虚线外侧的实线部分以计算综合路程消耗。图中所示的绕过障碍物区域是沿着障碍物区域的外部轮廓飞行，实际也可以采用其他方式绕开障碍物区域，例如沿着不同作业区域的端口点或者端口点与起始点/终点之间的绕开障碍物区域的折线飞行等。本实施例可以与以上任一实施例相结合。
如图9所示，在本发明无人机的飞行路径规划方法一实施例中，非常规消耗进一步包括返航维护的路程消耗，此时计算候选连接路径的返航维护消耗的具体步骤包括：
S1324：根据无人机的动力源和/或作业源的状态计算应进行返航维护的返航点。
当无人机的动力源和/或作业源的状态不满足续航条件时，应进行返航维护。候选连接路径上的任意一点均可判断无人机的动力源和/或作业源的状态是否满足续航条件。在其中一个实施例中，为减少计算量，可以只在航线段的端点进行判断。一般而言，续航条件是指无人机飞到当前端点的动力源和/或作业源的 剩余量减去预设的安全量大于或等于从当前端点飞往下一端点且从下一端点飞往返航点的动力源和/或作业源消耗量。
具体的，依次判断候选连接路径中的航线段的当前端点是否满足续航条件，若不满足续航条件，则可以直接将当前端点作为返航点。为了进一步提高动力源和/或作业源的利用率，可以在不满足续航条件时，进一步判断当前端点与下一端点之间是否为需作业的航线段；若是，则在当前端点与下一端点之间的航线段上寻找返航点，以使得无人机飞到返航点后仍能够安全返回维护点，若否，则将当前端点作为返航点。
S1325：根据返航点的坐标和维护点的坐标计算返航维护的路程消耗。
维护点可以是固定在某个坐标的，也可以是可移动的，或者一个区域。
当维护点是可移动的时，其位置可以是与无人机的飞行路径相关的，具体而言，根据无人机的剩余飞行时间、维护设备或人员的运动路径、当前位置以及运动速度计算维护设备或人员在无人机的剩余飞行时间内的最大运动范围，将最大运动范围内与返航点或下一端点之间的动力源和/或作业源消耗最小或飞行距离最近的点作为维护点。当然，维护点的位置也可以是与无人机的飞行路径不相关的。
需要注意的是，可以对每条候选连接路径都计算其返航维护消耗并加入非常规消耗中，然后将非常规消耗最小的候选连接路径作为无人机的飞行路径。当然，为了减小计算量，也可以先不计算返航维护消耗而确定无人机的飞行路径，然后对已确定的飞行路径计算应进行返航维护的返航点，并可在维护点的位置与飞行路径相关时进一步计算维护点的位置。
如图10所示，本发明无人机的飞行路径规划方法第六实施例，是在本发明无人机的飞行路径规划方法第四实施例的基础上，进一步包括：
S134：形成用于无人机的飞行路径的多个任务点的坐标序列。
无人机将按照坐标序列在多个任务点之间进行作业。任务点中至少包括无人机需要改变飞行方向的点，具体而言，需要改变飞行方向的点中至少包括航 线段的端点，以及用于表示绕行规避障碍物路径的点，还可以进一步包括返航点。
计算得到的任务点的坐标一般是基于切平面的平面坐标，为了便于无人机的作业，可以将平面坐标转换为经纬度坐标，此计算过程为本发明无人机的飞行路径规划方法第二实施例中将经纬度转换为平面坐标的逆运算，可参考其中的具体描述。
坐标序列中除了包括每个任务点的坐标之外，可以进一步包括在该任务点的飞行方向和/或执行的操作。一般而言，执行的操作至少包括开始作业和停止作业，以适应无人机的飞行状态，例如在无人机从一个作业区域飞往另一个作业区域时应停止作业以免浪费。
在本发明无人机的飞行路径规划方法一实施例中，预设飞行区域中存在特殊作业区域，具体而言，包括可飞行的非作业区域、高空飞行区域和低空飞行区域中的至少一种。无人机在特殊作业区域内执行的操作不同于无人机在特殊作业区域外执行的操作。应获取用于表示预设飞行区域中的特殊作业区域的边缘的特殊边缘点的坐标，然后计算航线段与特殊作业区域的边缘的交点并将其插入飞行路径。此时任务点中应包括航线段与特殊作业区域的边缘的交点。
如图11所示，本发明无人机的飞行路径规划方法第七实施例的执行主体可以为无人机的控制应用程序，例如，app等，或者运行控制应用程序的设备，例如，无人机的遥控器，手机，ipad，无人机的基站等等。本实施例包括：
S21：显示预设飞行区域的地貌图像。
在显示屏上或者投影显示地貌图像，地貌图像可以为通过无人机航拍获取的。
S22：获取用于表示预设飞行区域的地貌图像的边界的多个特征点的坐标。
可以通过输入装置，例如触摸屏、键盘、鼠标、麦克风、按键等，接收用户输入的特征点的坐标信息，也可以通过图像识别从地貌图像中提取特征点的坐标信息。特征点可以在边界上，也可以不在边界上。
特征点至少包括用于表示预设飞行区域的外部轮廓的多个第一特征点。当预设飞行区域内存在障碍物时，特征点可进一步包括用于表示障碍物区域的外部轮廓的第二特征点。这里的障碍物区域是指无人机需要绕行的有障碍物的区域，例如禁飞区、存在房子、电线杆等不宜飞越的障碍物的区域。
S23：根据多个特征点的坐标，确定预设飞行区域的边缘线。
可以采用直线段或者曲线段连接在边界上的特征点并将形成的折线或曲线作为全部或部分边缘线；也可以以不在边界上的特征点为圆心作圆或椭圆，并将圆或椭圆的全部或者部分作为全部或部分边缘线。可以将边缘线与地貌图像叠加显示在显示屏上。
如图12所示，本发明无人机的飞行路径规划方法第八实施例，是在本发明无人机的飞行路径规划方法第七实施例的基础上，进一步包括：
S24：获取无人机的作业参数。
可以通过输入装置获取用户输入的作业参数，也可以通过其他方式，例如从互联网或者本地存储数据中获取无人机的作业参数。作业参数至少包括风向、无人机的作业宽度和起始点。
S25：根据边缘线和无人机的作业参数，确定无人机在预设飞行区域内的飞行路径。
根据边缘线和无人机的作业参数，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段，无人机的飞行方向根据风向确定，相邻航线段之间的间距等于无人机的作业宽度。一般而言，参考无人机的作业特点，包括少转弯、就近飞行等，将这些航线段与起始点(需要时加上终点)连接起来，选择非常规消耗最小的连接方式作为飞行路径。
S26：显示飞行路径。
在显示屏上显示飞行路径，可以将飞行路径与地貌图像叠加显示。
如图13所示，本发明无人机的飞行路径规划方法第九实施例，是在本发明无人机的飞行路径规划方法第八实施例的基础上，步骤S25具体包括：
S251：根据边缘线和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段。
S252：根据航线段与边界的相交位置，将预设飞行区域划分至多个作业区域。
S253：根据多个作业区域，确定无人机在预设飞行区域内的飞行路径。
以上各步骤具体如何实施可参考本发明无人机的飞行路径规划方法第七实施例之前的各实施例以及可能的组合，在此不再重复。
需要注意的是，当规划的飞行路径中包括返航点时，无人机的作业参数应包括估算返航点所需要的参数，例如无人机的最大飞行时间/距离、维护设备或人员的运动路径、运动速度等。显示飞行路径时，可以在显示屏上显示返航点的数量和/或在飞行路径中标出返航点。
如图14所示，本发明无人机的控制方法第一实施例包括：
S31：按照预设飞行路径在预设飞行区域内进行飞行作业。
S32：调整无人机的飞行路径和/或执行的操作，以使无人机适合当前的作业环境或工作状态。
无人机实际飞行时面临的情况可能与预设飞行路径不一致，例如最大飞行距离不准、实际电池与规划时用的电池参数不同、出现新的障碍物、原有障碍物消失等，因此需要对无人机进行适应性调整，例如在前方出现新的障碍物时，可以改变飞行方向绕开障碍物，过着改变飞行高度以跨越或穿越障碍物。
其中，调整无人机的飞行路径包括如下至少一种：改变飞行方向、改变飞行高度、停止飞行；调整无人机执行的操作包括如下任意一种：停止作业，开始作业。
作业环境包括如下至少一种：前方存在障碍物、前方存在非作业区域、前方为已作业区域；工作状态包括如下至少一种：动力源不足、作业源不足、导航信号丢失、接收到控制命令。
如图15所示，本发明无人机的控制方法第二实施例，是在本发明无人机的 控制方法第一实施例的基础上，工作状态包括动力源不足和/或作业源不足，调整无人机的飞行路径包括改变飞行方向以飞往维护点，步骤S22具体包括：
S321：获取无人机的动力源和/或作业源的状态。
在本发明无人机的控制方法一实施例中，只在飞行路径中的任务点处获取无人机的动力源和/或作业源的状态，具体包括：获取无人机的当前位置坐标；判断无人机的当前位置坐标是否对应用于表示无人机飞行路径的多个任务点中的某个任务点；若是，则获取无人机的动力源和/或作业源的状态。当然也可以在飞行路径中的任意一点获取无人机的动力源和/或作业源的状态。
S322：根据无人机的动力源和/或作业源状态计算应进行返航维护的返航点。
当无人机的动力源和/或作业源的状态不满足续航条件时，应进行返航维护。续航条件是指无人机的动力源和/或作业源的剩余量减去预设的安全量大于或等于从无人机的当前位置飞往无人机的当前位置对应的任务点的下一任务点的动力源和/或作业源的消耗。
具体的，判断无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则可以直接将无人机的当前位置作为返航点。为了进一步提高动力源和/或作业源的利用率，可以在不满足续航条件时，进一步判断无人机的当前位置与无人机的当前位置对应的任务点的下一任务点之间是否为需作业的航线段；若是，则在无人机的当前位置与下一任务点之间的航线段上寻找返航点，以使得无人机飞到返航点后仍能够安全返回维护点，若否，则将无人机的当前位置作为返航点。
S323：在无人机飞行至返航点时改变飞行方向以飞往维护点进行维护。
维护点可以是固定在某个坐标的，也可以是可移动的。维护点是可移动的是，其位置可以是受到无人机控制的，也可以是不受无人机控制的。
需要注意的是，本实施例的实施可以是在预设飞行路径中不包括返航点的情况下在飞行作业过程中计算返航点以进行返航维护，也可以在预设飞行路径中包括返航点的情况下根据实际飞行情况对预估的返航点进行修正。
如图16所示，本发明无人机的控制方法第三实施例，是在本发明无人机的控制方法第二实施例的基础上，进一步包括：
S324：根据无人机的动力源和/或作业源的状态预估无人机的剩余飞行时间。
将无人机的动力源和/或作业源的剩余量减去预设的安全量，除以对应的消耗速度，可以估算出无人机的剩余飞行时间，当根据动力源和作业源计算出的两个值不同时，取较小的值。
S325：根据维护设备或人员的当前位置、运动路径以及运动速度计算维护设备或人员在预估剩余飞行时间内的最大运动范围。
一般而言，维护设备或人员在预设飞行区域外运动。
S326：在最大运动范围内指定维护点。
将最大运动范围内与返航点或下一端点之间的动力源和/或作业源消耗最小或飞行距离最近的点作为维护点。指定维护点后需要将维护点的位置发给维护设备或人员，以使其赶往维护点。
本实施例中维护点的计算与上一实施例中返航点的计算可以是相对独立的，例如在本次返航维护结束后就指定下次维护点的位置；也可以是同时完成的。
如图17所示，本发明无人机的控制方法第四实施例包括：
S41：获取无人机的当前维护点的位置。
当前维护点的位置可以为无人机上次进行维护的维护点的位置，也可以为维护设备或人员的当前位置。
S42：根据当前维护点的位置，确定下一维护点的位置。
根据当前维护点的位置、无人机的飞行时间、维护设备或人员的运动路径以及运动速度计算维护设备或人员的最大运动范围，在最大运动范围内确定下一维护点的位置。
当当前维护点的位置为无人机上次进行维护的维护点的位置时，无人机的飞行时间是指无人机的预估最大飞行时间。在最大运动范围内确定下一维护点 具体包括：根据当前维护点的位置以及无人机的飞行路径和预估最大飞行时间，预估无人机从当前维护点起下一次应进行返航维护的下一返航点；将最大运动范围内与下一返航点之间动力源消耗最小或飞行距离最近的点作为下一维护点。
当当前维护点的位置为维护设备或人员的当前位置时，无人机的飞行时间是指根据无人机的动力源和/或作业源的状态预估的无人机的剩余飞行时间。在最大运动范围内确定下一维护点具体包括：根据无人机的动力源和/或作业源的状态预估返航点的位置；将最大运动范围内与预估的返航点之间动力源消耗最小或飞行距离最近的点作为下一维护点。
S43：将下一维护点的位置发送给维护设备或人员。
以通知维护设备或人员赶往下一维护点。
如图18所示，本发明无人机的飞行路径规划系统第一实施例包括：处理器11。图中只画出了一个处理器，实际处理器的数量可以更多。处理器的数量大于一时，各处理器可单独或协同工作。
处理器11可以被称为中央处理单元(Central Processing Unit，CPU)或微控制单元(Microcontroller Unit，MCU)。处理器11可能是一种集成电路芯片，具有信号的处理能力。处理器11还可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
系统可以进一步包括存储器(图中未画出)，存储器用于存储处理器11工作所必需的指令及数据，也可以存储接收的数据。
处理器11用于获取无人机的预设飞行区域的地理信息；根据地理信息，将预设飞行区域分隔为多个作业区域；根据多个作业区域，确定无人机在预设飞行区域内的飞行路径。在图示的实施例中，处理器11为执行程序的微处理器，其包括：地理信息程序，被配置为获取无人机的预设飞行区域的地理信息；分 隔程序，被配置为根据地理信息将预设飞行区域分隔为多个作业区域；路径确定程序，被配置为根据地理信息将预设飞行区域分隔为多个作业区域。
如图19所示，本发明无人机的飞行路径规划系统第二实施例，是在本发明无人机的飞行路径规划系统第一实施例的基础上，进一步包括传感器12，传感器12与处理器11通讯连接。传感器12用于捕捉预设飞行区域的地理信息，并将预设飞行区域的地理信息传送给处理器。
其中，地理信息包括用于表示预设飞行区域的边界的坐标信息，其中边界至少包括用于表示预设飞行区域的外部轮廓的第一边界。
其中，边界进一步包括用于表示预设飞行区域内的障碍物区域的外部轮廓的第二边界。
其中，处理器进一步用于获取用于表示边界的经纬度坐标；将经纬度坐标转换为二维坐标。
其中，处理器进一步用于将经纬度坐标转换为地心坐标系下的三维坐标；将三维坐标转换为与地球表面相切的平面坐标系下的二维坐标。
其中，处理器进一步用于根据边界的位置信息和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段；根据航线段与边界的相交位置，将航线段划分至多个作业区域。
其中，相邻两个航线段之间的间距等于无人机的作业宽度，每个航线段的端点位于边界上。
其中，无人机的飞行方向根据预设飞行区域的风向确定。
其中，处理器进一步用于获取平行于无人机的飞行方向的直线与边界的相切点；根据相切点将边界划分为多个边缘段；将两侧端点分别位于相同边缘段上的航线段划分至同一作业区域。
其中，处理器进一步用于确定可作为各作业区域的入口点或出口点的端口点；根据作业区域的端口点，确定无人机的飞行路径。
其中，处理器进一步用于将同一作业区域的位于最外侧的航线段的两侧端 点作为端口点。
其中，处理器进一步用于根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗，其中非常规消耗至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗；选择非常规消耗最小的一种候选连接路径作为无人机的飞行路径。
其中，处理器进一步用于确定作业区域之间的候选作业区域连接方式；根据候选作业区域连接方式确定无人机的起始点与作业区域的端口点之间以及作业区域的端口点之间的候选端口连接方式；根据候选端口连接方式，计算非常规消耗。
其中，处理器进一步用于通过排列组合方式或根据作业区域之间的邻接关系确定候选作业区域连接方式，其中根据作业区域之间的邻接关系确定候选作业区域连接方式包括确定遍历所有作业区域且连接非邻接作业区域次数最少的多个候选作业区域连接方式。
其中，处理器进一步用于对于每一种候选作业区域连接方式，根据各作业区域的端口点以及作业区域内包括的航线段的数量确定作业区域的候选入口点和候选出口点；根据作业区域的候选入口点和候选出口点确定候选端口连接方式。
其中，处理器进一步用于对于第一个作业区域，选择离起始点最近的端口点作为第一个作业区域的候选入口点，并根据第一个作业区域包括的航线段的数量和第一个作业区域的候选入口点确定第一个作业区域的候选出口点；对于其余作业区域，选择当前作业区域距离上一个作业区域的候选出口点最近的端口点作为当前作业区域的候选入口点，并根据当前作业区域包括的航线段的数量和当前作业区域的候选入口点确定当前作业区域的候选出口点。
其中，预设飞行区域内存在至少一个障碍物区域；处理器进一步用于将连接每个作业区域的候选入口点与前一作业区域的候选出口点或无人机的起始点的线段与障碍物区域的边缘求交集；若交集为空，则将沿线段所产生的路程消 耗作为两个作业区域之间或作业区域与无人机的起始点之间的路程消耗，否则将线段与障碍物区域的交集部分所产生的路程消耗替换为规避障碍物区域所产生的路程消耗，并计算规避障碍物区域所产生的路程消耗与线段的非交集部分所产生的路程消耗之和以作为两个作业区域之间或作业区域与无人机的起始点之间的路程消耗。
其中，规避障碍物区域所产生的路程消耗包括：通过绕行方式绕过障碍物区域所产生的路程消耗或者通过升高或降低方式跨越或穿越障碍物区域所产生的路程消耗。
其中，非常规消耗进一步包括返航维护的路程消耗；处理器进一步用于根据无人机的动力源和/或作业源的状态计算应进行返航维护的返航点；根据返航点的坐标和维护点的坐标计算返航维护的路程消耗。
其中，处理器进一步用于依次判断航线段的当前端点是否满足续航条件，若不满足续航条件，则将当前端点作为返航点。
其中，处理器进一步用于依次判断航线段的当前端点是否满足续航条件；若不满足续航条件，则判断当前端点与下一端点之间是否为需作业的航线段；若是，则在当前端点与下一端点之间的航线段上寻找返航点，以使得无人机飞到返航点后仍能够安全返回维护点，若否，则将当前端点作为返航点。
其中，续航条件是指无人机飞到当前端点的动力源和/或作业源的剩余量减去预设的安全量大于或等于从当前端点飞往下一端点且从下一端点飞往返航点的动力源和/或作业源消耗量。
其中，处理器进一步用于根据无人机的剩余飞行时间、维护设备或人员的运动路径、当前位置以及运动速度计算维护设备或人员在无人机的剩余飞行时间内的最大运动范围；在最大运动范围内指定维护点。
其中，处理器进一步用于将最大运动范围内与返航点或下一端点之间的动力源和/或作业源消耗最小或飞行距离最近的点作为维护点。
其中，处理器进一步用于获取用于表示预设飞行区域中的特殊作业区域的 边缘的特殊边缘点的坐标；计算航线段与特殊作业区域的边缘的交点；将航线段与特殊作业区域的边缘的交点插入飞行路径，以使无人机在特殊作业区域内执行的操作不同于无人机在特殊作业区域外执行的操作。
其中，特殊作业区域包括可飞行的非作业区域、高空飞行区域和低空飞行区域中的至少一种。
其中，处理器进一步用于形成用于无人机的飞行路径的多个任务点的坐标序列，以使得无人机按照坐标序列在多个任务点之间进行作业，其中任务点至少包括航线段的端点、航线段与特殊作业区域的边缘的交点。
如图20所示，本发明无人机的飞行路径规划系统第三实施例包括：处理器21和显示屏22。处理器21与显示屏22通讯连接。图中只画出了一个处理器，实际处理器的数量可以更多。处理器的数量大于一时，各处理器可单独或协同工作。
处理器21可以被称为中央处理单元(Central Processing Unit，CPU)或微控制单元(Microcontroller Unit，MCU)。处理器21可能是一种集成电路芯片，具有信号的处理能力。处理器21还可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
系统可以进一步包括存储器(图中未画出)，存储器用于存储处理器21工作所必需的指令及数据，也可以存储接收的数据。
显示屏22用于在处理器21的控制下显示预设飞行区域的地貌图像。
处理器21用于获取用于表示预设飞行区域的地貌图像的边界的多个特征点的坐标；根据多个特征点的坐标信息，确定预设飞行区域的边缘线。
如图21所示，本发明无人机的飞行路径规划系统第四实施例，是在本发明无人机的飞行路径规划系统第三实施例的基础上，进一步包括输入装置23，输入装置23与处理器21通讯连接。输入装置23用于接收用于输入的表示预设飞 行区域的地貌图像的边界的多个特征点并获取其坐标。
其中，处理器进一步用于通过图像识别从地貌图像中提取多个特征点的坐标。
其中，特征点包括用于表示预设飞行区域的外部轮廓的多个第一特征点，以及用于表示预设飞行区域内障碍物区域的外部轮廓的第二特征点。
其中，处理器进一步用于利用线段连接多个特征点并将形成的折线作为边缘线。
其中，处理器进一步用于以特征点中的至少一个作为圆心作圆或者椭圆，并将圆或者椭圆的全部或者部分作为边缘线。
其中，处理器进一步用于根据边缘线，确定无人机在预设飞行区域内的飞行路径；显示飞行路径。
其中，处理器进一步用于获取无人机的作业参数；根据边缘线和无人机的作业参数，确定无人机在预设飞行区域内的飞行路径。
其中，作业参数至少包括风向、无人机的作业宽度和起始点。
其中，处理器进一步用于根据边缘线和无人机的作业宽度，获取预设飞行区域内的平行于无人机的飞行方向的多条航线段，其中无人机的飞行方向根据风向确定；根据航线段与边界的相交位置，将预设飞行区域划分至多个作业区域；根据多个作业区域，确定无人机在预设飞行区域内的飞行路径。
其中，处理器进一步用于确定可作为各作业区域的入口点或出口点的端口点；根据无人机的起始点和端口点计算遍历所有作业区域的多种候选连接路径的非常规消耗，其中非常规消耗至少包括不同作业区域之间的路程消耗以及从起始点飞往作业区域的路程消耗；选择非常规消耗最小的一种候选连接路径作为无人机的飞行路径。
其中，非常规消耗进一步包括返航维护的路程消耗；处理器进一步用于根据无人机的工作状态计算应进行返航维护的返航点；根据返航点的坐标和维护点的坐标计算返航维护的路程消耗。
其中，处理器进一步用于控制显示屏显示飞行路径，并显示返航点的数量和/或在飞行路径中标出返航点。
本发明无人机的飞行路径规划系统各实施例中各个部分的功能具体可参考本发明无人机的飞行路径规划方法对应实施例中的描述，在此不再重复。
如图22所示，本发明无人机的控制系统第一实施例包括：处理器31。图中只画出了一个处理器，实际处理器的数量可以更多。处理器的数量大于一时，各处理器可单独或协同工作。
处理器31可以是无人机飞行控制器，也可以是其中的一部分，控制无人机的飞行和操作。处理器31可以被称为中央处理单元(Central Processing Unit，CPU)或微控制单元(Microcontroller Unit，MCU)。处理器31可能是一种集成电路芯片，具有信号的处理能力。处理器31还可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
系统可以进一步包括存储器(图中未画出)，存储器用于存储处理器31工作所必需的指令及数据，也可以存储接收的数据。
处理器31用于按照预设航线在预设飞行区域内进行飞行作业；调整无人机的飞行路径和/或执行的操作，以使无人机适合当前的作业环境或工作状态。
如图23所示，本发明无人机的控制系统第二实施例，是在本发明无人机的控制系统第一实施例的基础上，进一步包括定位装置32，定位装置32与处理器31通讯连接。定位装置32用于获取无人机的当前位置信息，并将当前位置信息传送给处理器31，处理器31根据当前位置信息以及预设航线控制无人机进行飞行作业。
其中，调整无人机的飞行路径包括如下至少一种：改变飞行方向、改变飞行高度、停止飞行；调整无人机执行的操作包括如下任意一种：停止作业，开始作业。
其中，作业环境包括如下至少一种：前方存在障碍物、前方存在非作业区域、前方为已作业区域；工作状态包括如下至少一种：动力源不足、作业源不足、导航信号丢失、接收到控制命令。
其中，工作状态包括动力源不足和/或作业源不足，调整无人机的飞行路径包括改变飞行方向以飞往维护点；处理器进一步用于获取无人机的动力源和/或作业源的状态；根据无人机的动力源和/或作业源状态计算应进行返航维护的返航点；在无人机飞行至返航点时改变飞行方向以飞往维护点进行维护。
其中，处理器进一步用于获取无人机的当前位置坐标；判断无人机的当前位置坐标是否对应用于表示无人机飞行路径的多个任务点中的某个任务点；若是，则获取无人机的动力源和/或作业源的状态。
其中，处理器进一步用于判断无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则将无人机的当前位置作为返航点。
其中，处理器进一步用于判断无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则判断无人机的当前位置与无人机的当前位置对应的任务点的下一任务点之间是否为需作业的航线段；若是，则在无人机的当前位置与下一任务点之间的航线段上寻找返航点，以使得无人机飞到返航点后仍能够安全返回维护点，若否，则将无人机的当前位置作为返航点。
其中，续航条件是指无人机的动力源和/或作业源的剩余量减去预设的安全量大于或等于从无人机的当前位置飞往无人机的当前位置对应的任务点的下一任务点的动力源和/或作业源的消耗。
其中，处理器进一步用于根据无人机的动力源和/或作业源的状态预估无人机的剩余飞行时间；根据维护设备或人员的当前位置、运动路径以及运动速度计算维护设备或人员在预估剩余飞行时间内的最大运动范围；在最大运动范围内指定维护点。
其中，处理器进一步用于根据无人机的动力源和/或作业源的状态预估返航点的位置；将最大运动范围内与预估的返航点之间动力源消耗最小或飞行距离 最近的点作为维护点。
如图24所示，本发明无人机的控制系统第三实施例包括：处理器41。图中只画出了一个处理器，实际处理器的数量可以更多。处理器的数量大于一时，各处理器可单独或协同工作。
处理器41可以是无人机飞行控制器，也可以是其中的一部分，控制无人机的飞行和操作。处理器41可以被称为中央处理单元(Central Processing Unit，CPU)或微控制单元(Microcontroller Unit，MCU)。处理器41可能是一种集成电路芯片，具有信号的处理能力。处理器41还可以是通用处理器、数字信号处理器(DSP)、专用集成电路(ASIC)、现成可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
系统可以进一步包括存储器(图中未画出)，存储器用于存储处理器41工作所必需的指令及数据，也可以存储接收的数据。
处理器41用于获取无人机的当前维护点的位置；根据当前维护点的位置，确定下一维护点的位置；将下一维护点的位置发送给维护设备或人员。在图示的实施例中，处理器为执行程序的微处理器，其包括获取位置程序，被配置为无人机的当前维护点的位置；维护点程序，被配置为根据当前维护点的位置，确定下一维护点的位置；发送程序，被配置为下一维护点的位置发送给维护设备或人员。
其中，当前维护点为无人机上次进行维护的维护点；处理器进一步用于根据当前维护点的位置、无人机的预估最大飞行时间、维护设备或人员的运动路径以及运动速度计算维护设备或人员在预估最大飞行时间内的最大运动范围；在最大运动范围内确定下一维护点的位置。
其中，处理器进一步用于根据当前维护点的位置以及无人机的飞行路径和预估最大飞行时间，预估无人机从当前维护点起下一次应进行返航维护的下一返航点；将最大运动范围内与下一返航点之间动力源消耗最小或飞行距离最近 的点作为下一维护点。
其中，当前维护点的位置为维护设备或人员的当前位置；处理器进一步用于根据无人机的动力源和/或作业源的状态预估无人机的剩余飞行时间；根据维护设备或人员的当前位置、运动路径以及运动速度计算维护设备或人员在预估最大飞行时间内的最大运动范围；在最大运动范围内确定下一维护点的位置。
其中，处理器进一步用于根据无人机的动力源和/或作业源的状态预估返航点的位置；将最大运动范围内与预估的返航点之间动力源消耗最小或飞行距离最近的点作为下一维护点。
本发明无人机的控制系统各实施例中各个部分的功能具体可参考本发明无人机的控制方法对应实施例中的描述，在此不再重复。
在本发明所提供的几个实施例中，应该理解到，所揭露的无人机的飞行路径规划系统和控制系统，可以通过其它的方式实现。例如，以上所描述的无人机的飞行路径规划系统和控制系统实施方式仅仅是示意性的，例如，所述模块或资源单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个资源单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或资源单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的资源单元可以是或者也可以不是物理上分开的，作为资源单元显示的部件可以是或者也可以不是物理资源单元，即可以位于一个地方，或者也可以分布到多个网络资源单元上。可以根据实际的需要选择其中的部分或者全部资源单元来实现本实施方式方案的目的。
另外，在本发明各个实施例中的各功能资源单元可以集成在一个处理资源单元中，也可以是各个资源单元单独物理存在，也可以两个或两个以上资源单元集成在一个资源单元中。上述集成的资源单元既可以采用硬件的形式实现，也可以采用软件功能资源单元的形式实现。
所述集成的资源单元如果以软件功能资源单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(processor)执行本发明各个实施方式所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述仅为本发明的实施方式，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。

Claims (111)

  1. 一种无人机的飞行路径规划方法，其特征在于，包括：
    获取无人机的预设飞行区域的地理信息；
    根据所述地理信息，将所述预设飞行区域划分至多个作业区域；
    根据所述多个作业区域，确定所述无人机在所述预设飞行区域内的飞行路径。
  2. 根据权利要求1所述的方法，其特征在于，
    所述地理信息包括用于表示预设飞行区域的边界的坐标信息，其中所述边界至少包括用于表示所述预设飞行区域的外部轮廓的第一边界。
  3. 根据权利要求2所述的方法，其特征在于，
    所述边界进一步包括用于表示所述预设飞行区域内的障碍物区域的外部轮廓的第二边界。
  4. 根据权利要求2或3所述的方法，其特征在于，
    所述获取无人机的预设飞行区域的地理信息的步骤包括：
    获取用于表示所述边界的经纬度坐标；
    将所述经纬度坐标转换为二维坐标。
  5. 根据权利要求4所述的方法，其特征在于，所述将所述经纬度坐标转换为二维坐标的步骤包括：
    将所述经纬度坐标转换为地心坐标系下的三维坐标；
    将所述三维坐标转换为与地球表面相切的平面坐标系下的所述二维坐标。
  6. 根据权利要求2或3所述的方法，其特征在于，
    所述根据所述地理信息，将所述预设飞行区域划分至多个作业区域的步骤包括：
    根据所述边界的位置信息和所述无人机的作业宽度，获取所述预设飞行区域内的平行于所述无人机的飞行方向的多条航线段；
    根据所述航线段与所述边界的相交位置，将所述航线段划分至多个作业区域。
  7. 根据权利要求6所述的方法，其特征在于，
    相邻两个所述航线段之间的间距等于所述无人机的作业宽度，每个所述航线段的端点位于所述边界上。
  8. 根据权利要求6所述的方法，其特征在于，
    所述无人机的飞行方向根据所述预设飞行区域的风向确定。
  9. 根据权利要求6所述的方法，其特征在于，
    所述根据所述航线段与所述边界的相交位置，将所述航线段划分至多个作业区域的步骤包括：
    获取平行于所述无人机的飞行方向的直线与所述边界的相切点；
    根据所述相切点将所述边界划分为多个边缘段；
    将两侧端点分别位于相同所述边缘段上的所述航线段划分至同一作业区域。
  10. 根据权利要求6所述的方法，其特征在于，
    所述根据所述多个作业区域，确定所述无人机在所述预设飞行区域内的飞行路径的步骤包括：
    确定可作为各所述作业区域的入口点或出口点的端口点；
    根据所述作业区域的端口点，确定所述无人机的飞行路径。
  11. 根据权利要求10所述的方法，其特征在于，
    所述确定可作为各所述作业区域的入口点或出口点的端口点的步骤包括：
    将同一作业区域的位于最外侧的所述航线段的两侧端点作为所述端口点。
  12. 根据权利要求10所述的方法，其特征在于，
    所述根据所述作业区域的端口点确定所述无人机的飞行路径的步骤包 括：
    根据所述无人机的起始点和所述端口点计算遍历所有所述作业区域的多种候选连接路径的非常规消耗，其中所述非常规消耗至少包括不同所述作业区域之间的路程消耗以及从所述起始点飞往所述作业区域的路程消耗；
    选择所述非常规消耗最小的一种候选连接路径作为所述无人机的飞行路径。
  13. 根据权利要求12所述的方法，其特征在于，
    所述根据所述无人机的起始点和所述端口点计算遍历所有所述作业区域的多种候选连接路径的非常规消耗的步骤包括：
    确定所述作业区域之间的候选作业区域连接方式；
    根据所述候选作业区域连接方式确定所述无人机的起始点与所述作业区域的端口点之间以及所述作业区域的端口点之间的候选端口连接方式；
    根据所述候选端口连接方式，计算所述非常规消耗。
  14. 根据权利要求13所述的方法，其特征在于，
    所述确定所述作业区域之间的候选作业区域连接方式的步骤包括：
    通过排列组合方式或根据所述作业区域之间的邻接关系确定所述候选作业区域连接方式，其中根据所述作业区域之间的邻接关系确定所述候选作业区域连接方式包括确定遍历所有所述作业区域且连接非邻接作业区域次数最少的多个所述候选作业区域连接方式。
  15. 根据权利要求13所述的方法，其特征在于，
    所述根据所述候选作业区域连接方式确定所述无人机的起始点与所述作业区域的端口点之间以及所述区段的端口点之间的候选端口连接方式的步骤包括：
    对于每一种候选作业区域连接方式，根据各所述作业区域的端口点以及所述作业区域内包括的所述航线段的数量确定所述作业区域的候选入口 点和候选出口点；
    根据所述作业区域的候选入口点和候选出口点确定所述候选端口连接方式。
  16. 根据权利要求15所述的方法，其特征在于，
    所述对于每一种候选作业区域连接方式，根据各所述作业区域的端口点以及所述作业区域内包括的所述航线段的数量确定所述作业区域的候选入口点和候选出口点的步骤包括：
    对于第一个所述作业区域，选择离所述起始点最近的端口点作为所述第一个作业区域的候选入口点，并根据所述第一个作业区域包括的所述航线段的数量和所述第一个作业区域的候选入口点确定所述第一个作业区域的候选出口点；
    对于其余所述作业区域，选择当前所述作业区域距离上一个所述作业区域的候选出口点最近的端口点作为所述当前作业区域的候选入口点，并根据所述当前作业区域包括的所述航线段的数量和所述当前作业区域的候选入口点确定所述当前作业区域的候选出口点。
  17. 根据权利要求13所述的方法，其特征在于，
    所述预设飞行区域内存在至少一个障碍物区域；
    所述根据所述候选端口连接方式计算所述非常规消耗的步骤包括：
    将连接每个所述作业区域的候选入口点与前一所述作业区域的候选出口点或所述无人机的起始点的线段与所述障碍物区域的边界求交集；
    若所述交集为空，则将沿所述线段所产生的路程消耗作为所述两个作业区域之间或所述作业区域与所述无人机的起始点之间的路程消耗，否则将所述线段与所述障碍物区域的交集部分所产生的路程消耗替换为规避所述障碍物区域所产生的路程消耗，并计算所述规避所述障碍物区域所产生的路程消耗与所述线段的非交集部分所产生的路程消耗之和以作为所述两个作业区域之间或所述作业区域与所述无人机的起始点之间的路程消耗。
  18. 根据权利要求17所述的方法，其特征在于，
    所述规避所述障碍物区域所产生的路程消耗包括：通过绕行方式绕过所述障碍物区域所产生的路程消耗或者通过升高或降低方式跨越或穿越所述障碍物区域所产生的路程消耗。
  19. 根据权利要求12所述的方法，其特征在于，
    所述非常规消耗进一步包括返航维护的路程消耗；
    所述根据所述无人机的起始点和所述端口点计算遍历所有所述作业区域的多种候选连接路径的非常规消耗进一步包括：
    根据所述无人机的动力源和/或作业源的状态计算应进行返航维护的返航点；
    根据所述返航点的坐标和维护点的坐标计算所述返航维护的路程消耗。
  20. 根据权利要求19所述的方法，其特征在于，
    所述根据所述无人机的工作状态计算应进行返航维护的返航点的步骤包括：
    依次判断所述航线段的当前端点是否满足续航条件，若不满足续航条件，则将所述当前端点作为返航点。
  21. 根据权利要求19所述的方法，其特征在于，
    所述根据所述无人机的工作状态计算应进行返航维护的返航点的步骤包括：
    依次判断所述航线段的当前端点是否满足续航条件；
    若不满足续航条件，则判断所述当前端点与下一端点之间是否为需作业的航线段；
    若是，则在所述当前端点与所述下一端点之间的航线段上寻找返航点，以使得所述无人机飞到所述返航点后仍能够安全返回所述维护点，若否，则将所述当前端点作为返航点。
  22. 根据权利要求20或21所述的方法，其特征在于，
    所述续航条件是指所述无人机飞到所述当前端点的动力源和/或作业源的剩余量减去预设的安全量大于或等于从所述当前端点飞往下一所述端点且从下一所述端点飞往所述返航点的动力源和/或作业源消耗量。
  23. 根据权利要求22所述的方法，其特征在于，
    所述根据所述返航点的坐标和维护点的坐标计算所述返航维护的路程消耗的步骤包括：
    根据所述无人机的剩余飞行时间、维护设备或人员的运动路径、当前位置以及运动速度计算所述维护设备或人员在所述无人机的剩余飞行时间内的最大运动范围；
    在所述最大运动范围内指定所述维护点。
  24. 根据权利要求23所述的方法，其特征在于，
    所述在所述最大运动范围内指定所述维护点的步骤包括：
    将所述最大运动范围内与所述返航点或所述下一端点之间的动力源和/或作业源消耗最小或飞行距离最近的点作为所述维护点。
  25. 根据权利要求10所述的方法，其特征在于，所述方法进一步包括：
    获取用于表示所述预设飞行区域中的特殊作业区域的边缘的特殊边缘点的坐标；
    计算所述航线段与所述特殊作业区域的边缘的交点；
    将所述航线段与所述特殊作业区域的边缘的交点插入所述飞行路径，以使所述无人机在所述特殊作业区域内执行的操作不同于所述无人机在所述特殊作业区域外执行的操作。
  26. 根据权利要求25所述的方法，其特征在于，
    所述特殊作业区域包括可飞行的非作业区域、高空飞行区域和低空飞行区域中的至少一种。
  27. 根据权利要求25所述的方法，其特征在于，所述方法进一步包括：
    形成用于所述无人机的飞行路径的多个任务点的坐标序列，以使得所述无人机按照所述坐标序列在所述多个任务点之间进行作业，其中所述任务点至少包括所述航线段的端点、所述航线段与所述特殊作业区域的边缘的交点。
  28. 一种无人机的控制方法，其特征在于，包括：
    按照预设飞行路径在预设飞行区域内进行飞行作业；
    调整所述无人机的飞行路径和/或执行的操作，以使所述无人机适合当前的作业环境或工作状态。
  29. 根据权利要求28所述的方法，其特征在于，
    所述调整所述无人机的飞行路径包括如下至少一种：改变飞行方向、改变飞行高度、停止飞行；
    所述调整所述无人机执行的操作包括如下任意一种：停止作业，开始作业。
  30. 根据权利要求28所述的方法，其特征在于，
    所述作业环境包括如下至少一种：前方存在障碍物、前方存在非作业区域、前方为已作业区域；
    所述工作状态包括如下至少一种：动力源不足、作业源不足、导航信号丢失、接收到控制命令。
  31. 根据权利要求28所述的方法，其特征在于，所述工作状态包括动力源不足和/或作业源不足，所述调整所述无人机的飞行路径包括改变飞行方向以飞往维护点；
    所述调整所述无人机的飞行路径和/或执行的操作，以使所述无人机适合当前的作业环境或工作状态的步骤包括：
    获取所述无人机的动力源和/或作业源的状态；
    根据所述无人机的动力源和/或作业源状态计算应进行返航维护的返航点；
    在所述无人机飞行至所述返航点时改变飞行方向以飞往所述维护点进行维护。
  32. 根据权利要求31所述的方法，其特征在于，
    所述获取所述无人机的动力源和/或作业源的状态的步骤包括：
    获取所述无人机的当前位置坐标；
    判断所述无人机的当前位置坐标是否对应用于表示无人机飞行路径的多个任务点中的某个任务点；
    若是，则获取所述无人机的动力源和/或作业源的状态。
  33. 根据权利要求32所述的方法，其特征在于，
    所述根据所述无人机的动力源和/或作业源的状态计算应进行返航维护的返航点的步骤包括：
    判断所述无人机的动力源和/或作业源的状态是否满足续航条件；
    若不满足续航条件，则将所述无人机的当前位置作为返航点。
  34. 根据权利要求32所述的方法，其特征在于，
    所述根据所述无人机的动力源和/或作业源的状态计算应进行返航维护的返航点的步骤包括：
    判断所述无人机的动力源和/或作业源的状态是否满足续航条件；
    若不满足续航条件，则判断所述无人机的当前位置与所述无人机的当前位置对应的任务点的下一任务点之间是否为需作业的航线段；
    若是，则在所述无人机的当前位置与所述下一任务点之间的航线段上寻找返航点，以使得所述无人机飞到所述返航点后仍能够安全返回所述维护点，若否，则将所述无人机的当前位置作为返航点。
  35. 根据权利要求33或34所述的方法，其特征在于，
    所述续航条件是指所述无人机的动力源和/或作业源的剩余量减去预设的安全量大于或等于从所述无人机的当前位置飞往所述无人机的当前位置对应的任务点的下一任务点的动力源和/或作业源的消耗。
  36. 根据权利要求31所述的方法，其特征在于，所述方法进一步包括：
    根据所述无人机的动力源和/或作业源的状态预估所述无人机的剩余飞行时间；
    根据维护设备或人员的当前位置、运动路径以及运动速度计算所述维护设备或人员在所述预估剩余飞行时间内的最大运动范围；
    在所述最大运动范围内指定所述维护点。
  37. 根据权利要求36所述的方法，其特征在于，
    所述在所述最大运动范围内指定所述维护点的步骤包括：
    根据所述无人机的动力源和/或作业源的状态预估返航点的位置；
    将所述最大运动范围内与所述预估的返航点之间动力源消耗最小或飞行距离最近的点作为所述维护点。
  38. 一种无人机的控制方法，其特征在于，包括：
    获取无人机的当前维护点的位置；
    根据所述当前维护点的位置，确定下一维护点的位置；
    将所述下一维护点的位置发送给维护设备或人员。
  39. 根据权利要求38所述的方法，其特征在于，
    所述当前维护点为所述无人机上次进行维护的维护点；
    所述根据所述当前维护点的位置，确定下一维护点的位置的步骤包括：
    根据所述当前维护点的位置、所述无人机的预估最大飞行时间、所述维护设备或人员的运动路径以及运动速度计算所述维护设备或人员在所述预估最大飞行时间内的最大运动范围；
    在所述最大运动范围内确定所述下一维护点的位置。
  40. 根据权利要求39所述的方法，其特征在于，
    所述在所述最大运动范围内确定所述下一维护点的位置的步骤包括：
    根据所述当前维护点的位置以及所述无人机的飞行路径和预估最大飞行时间，预估所述无人机从所述当前维护点起下一次应进行返航维护的下 一返航点；
    将所述最大运动范围内与所述下一返航点之间动力源消耗最小或飞行距离最近的点作为所述下一维护点。
  41. 根据权利要求38所述的方法，其特征在于，
    所述当前维护点的位置为所述维护设备或人员的当前位置；
    所述根据所述当前维护点的位置，确定下一维护点的位置的步骤包括：
    根据所述无人机的动力源和/或作业源的状态预估所述无人机的剩余飞行时间；
    根据所述维护设备或人员的当前位置、运动路径以及运动速度计算所述维护设备或人员在所述预估最大飞行时间内的最大运动范围；
    在所述最大运动范围内确定所述下一维护点的位置。
  42. 根据权利要求41所述的方法，其特征在于，
    所述在所述最大运动范围内确定所述下一维护点的位置的步骤包括：
    根据所述无人机的动力源和/或作业源的状态预估返航点的位置；
    将所述最大运动范围内与所述预估的返航点之间动力源消耗最小或飞行距离最近的点作为所述下一维护点。
  43. 一种无人机的飞行路径规划方法，其特征在于，包括：
    显示预设飞行区域的地貌图像；
    获取用于表示所述预设飞行区域的地貌图像的边界的多个特征点的坐标；
    根据所述多个特征点的坐标，确定所述预设飞行区域的边缘线。
  44. 根据权利要求43所述的方法，其特征在于，
    所述特征点包括用于表示所述预设飞行区域的外部轮廓的多个第一特征点，以及用于表示所述预设飞行区域内障碍物区域的外部轮廓的第二特征点。
  45. 根据权利要求43或44所述的方法，其特征在于，
    所述根据所述多个特征点的坐标，确定所述预设飞行区域的边缘线的步骤包括：
    利用线段连接所述特征点并将形成的折线作为所述边缘线。
  46. 根据权利要求43或44所述的方法，其特征在于，
    所述根据所述多个特征点的坐标，确定所述预设飞行区域的边缘线的步骤包括：
    以所述特征点中的至少一个作为圆心作圆或者椭圆，并将所述圆或者椭圆的全部或者部分作为所述边缘线。
  47. 根据权利要求43或44所述的方法，其特征在于，
    所述获取用于表示所述预设飞行区域的地貌图像的边界的多个特征点的坐标的步骤包括：
    通过输入装置接收用户输入的所述多个特征点的坐标；
    或通过图像识别从所述地貌图像中提取所述多个特征点的坐标。
  48. 根据权利要求43或44所述的方法，其特征在于，进一步包括：
    根据所述边缘线，确定所述无人机在所述预设飞行区域内的飞行路径；
    显示所述飞行路径。
  49. 根据权利要求48所述的方法，其特征在于，
    所述根据所述边缘线，确定所述无人机在所述预设飞行区域内的飞行路径的步骤之前进一步包括：
    获取无人机的作业参数；
    所述根据所述边缘线，确定所述无人机在所述预设飞行区域内的飞行路径的步骤包括：
    根据所述边缘线和所述无人机的作业参数，确定所述无人机在所述预设飞行区域内的飞行路径。
  50. 根据权利要求49所述的方法，其特征在于，
    所述作业参数至少包括风向、所述无人机的作业宽度和起始点。
  51. 根据权利要求50所述的方法，其特征在于，
    所述根据所述边缘线，确定所述无人机在所述预设飞行区域内的飞行路径的步骤包括：
    根据所述线和所述无人机的作业宽度，获取所述预设飞行区域内的平行于所述无人机的飞行方向的多条航线段，其中所述无人机的飞行方向根据所述风向确定；
    根据所述航线段与所述边界的相交位置，将所述预设飞行区域划分至多个作业区域；
    根据所述多个作业区域，确定所述无人机在所述预设飞行区域内的飞行路径。
  52. 根据权利要求51所述的方法，其特征在于，
    所述根据所述多个作业区域，确定所述无人机在所述预设飞行区域内的飞行路径的步骤包括：
    确定可作为各所述作业区域的入口点或出口点的端口点；
    根据所述无人机的起始点和所述端口点计算遍历所有所述作业区域的多种候选连接路径的非常规消耗，其中所述非常规消耗至少包括不同所述作业区域之间的路程消耗以及从所述起始点飞往所述作业区域的路程消耗；
    选择所述非常规消耗最小的一种候选连接路径作为所述无人机的飞行路径。
  53. 根据权利要求52所述的方法，其特征在于，
    所述非常规消耗进一步包括返航维护的路程消耗；
    所述根据所述无人机的起始点和所述端口点计算遍历所有所述作业区域的多种候选连接路径的非常规消耗进一步包括：
    根据所述无人机的工作状态计算应进行返航维护的返航点；
    根据所述返航点的坐标和维护点的坐标计算所述返航维护的路程消 耗。
  54. 根据权利要求53所述的方法，其特征在于，
    所述显示所述飞行路径的步骤包括：
    显示所述飞行路径，并显示所述返航点的数量和/或在所述飞行路径中标出所述返航点。
  55. 一种无人机的飞行路径规划系统，其特征在于，包括：
    一个或多个处理器，单独或协同工作，所述处理器用于：
    获取无人机的预设飞行区域的地理信息；
    根据所述地理信息，将所述预设飞行区域分隔为多个作业区域；
    根据所述多个作业区域，确定所述无人机在所述预设飞行区域内的飞行路径。
  56. 根据权利要求55所述的系统，其特征在于，进一步包括：传感器，所述传感器与所述处理器通讯连接；
    所述传感器用于捕捉所述预设飞行区域的地理信息，并将所述预设飞行区域的地理信息传送给所述处理器。
  57. 根据权利要求55或56所述的系统，其特征在于，
    所述地理信息包括用于表示预设飞行区域的边界的坐标信息，其中所述边界至少包括用于表示所述预设飞行区域的外部轮廓的第一边界。
  58. 根据权利要求57所述的系统，其特征在于，
    所述边界进一步包括用于表示所述预设飞行区域内的障碍物区域的外部轮廓的第二边界。
  59. 根据权利要求55-58中任一项所述的系统，其特征在于，
    所述处理器进一步用于获取用于表示所述边界的经纬度坐标；将所述经纬度坐标转换为二维坐标。
  60. 根据权利要求59所述的系统，其特征在于，
    所述处理器进一步用于将所述经纬度坐标转换为地心坐标系下的三维 坐标；将所述三维坐标转换为与地球表面相切的平面坐标系下的所述二维坐标。
  61. 根据权利要求55-58中任一项所述的系统，其特征在于，
    所述处理器进一步用于根据所述边界的位置信息和所述无人机的作业宽度，获取所述预设飞行区域内的平行于所述无人机的飞行方向的多条航线段；根据所述航线段与所述边界的相交位置，将所述航线段划分至多个作业区域。
  62. 根据权利要求61所述的系统，其特征在于，
    相邻两个所述航线段之间的间距等于所述无人机的作业宽度，每个所述航线段的端点位于所述边界上。
  63. 根据权利要求61所述的系统，其特征在于，
    所述无人机的飞行方向根据所述预设飞行区域的风向确定。
  64. 根据权利要求61所述的系统，其特征在于，
    所述处理器进一步用于获取平行于所述无人机的飞行方向的直线与所述边界的相切点；根据所述相切点将所述边界划分为多个边缘段；将两侧端点分别位于相同所述边缘段上的所述航线段划分至同一作业区域。
  65. 根据权利要求61所述的系统，其特征在于，
    所述处理器进一步用于确定可作为各所述作业区域的入口点或出口点的端口点；根据所述作业区域的端口点，确定所述无人机的飞行路径。
  66. 根据权利要求65所述的系统，其特征在于，
    所述处理器进一步用于将同一作业区域的位于最外侧的所述航线段的两侧端点作为所述端口点。
  67. 根据权利要求65所述的系统，其特征在于，
    所述处理器进一步用于根据所述无人机的起始点和所述端口点计算遍历所有所述作业区域的多种候选连接路径的非常规消耗，其中所述非常规消耗至少包括不同所述作业区域之间的路程消耗以及从所述起始点飞往所 述作业区域的路程消耗；选择所述非常规消耗最小的一种候选连接路径作为所述无人机的飞行路径。
  68. 根据权利要求67所述的系统，其特征在于，
    所述处理器进一步用于确定所述作业区域之间的候选作业区域连接方式；根据所述候选作业区域连接方式确定所述无人机的起始点与所述作业区域的端口点之间以及所述作业区域的端口点之间的候选端口连接方式；根据所述候选端口连接方式，计算所述非常规消耗。
  69. 根据权利要求68所述的系统，其特征在于，
    所述处理器进一步用于通过排列组合方式或根据所述作业区域之间的邻接关系确定所述候选作业区域连接方式，其中根据所述作业区域之间的邻接关系确定所述候选作业区域连接方式包括确定遍历所有所述作业区域且连接非邻接作业区域次数最少的多个所述候选作业区域连接方式。
  70. 根据权利要求68所述的系统，其特征在于，
    所述处理器进一步用于对于每一种候选作业区域连接方式，根据各所述作业区域的端口点以及所述作业区域内包括的所述航线段的数量确定所述作业区域的候选入口点和候选出口点；根据所述作业区域的候选入口点和候选出口点确定所述候选端口连接方式。
  71. 根据权利要求70所述的系统，其特征在于，
    所述处理器进一步用于对于第一个所述作业区域，选择离所述起始点最近的端口点作为所述第一个作业区域的候选入口点，并根据所述第一个作业区域包括的所述航线段的数量和所述第一个作业区域的候选入口点确定所述第一个作业区域的候选出口点；对于其余所述作业区域，选择当前所述作业区域距离上一个所述作业区域的候选出口点最近的端口点作为所述当前作业区域的候选入口点，并根据所述当前作业区域包括的所述航线段的数量和所述当前作业区域的候选入口点确定所述当前作业区域的候选出口点。
  72. 根据权利要求68所述的系统，其特征在于，
    所述预设飞行区域内存在至少一个障碍物区域；
    所述处理器进一步用于将连接每个所述作业区域的候选入口点与前一所述作业区域的候选出口点或所述无人机的起始点的线段与所述障碍物区域的边缘求交集；若所述交集为空，则将沿所述线段所产生的路程消耗作为所述两个作业区域之间或所述作业区域与所述无人机的起始点之间的路程消耗，否则将所述线段与所述障碍物区域的交集部分所产生的路程消耗替换为规避所述障碍物区域所产生的路程消耗，并计算所述规避所述障碍物区域所产生的路程消耗与所述线段的非交集部分所产生的路程消耗之和以作为所述两个作业区域之间或所述作业区域与所述无人机的起始点之间的路程消耗。
  73. 根据权利要求72所述的系统，其特征在于，
    所述规避所述障碍物区域所产生的路程消耗包括：通过绕行方式绕过所述障碍物区域所产生的路程消耗或者通过升高或降低方式跨越或穿越所述障碍物区域所产生的路程消耗。
  74. 根据权利要求67所述的系统，其特征在于，
    所述非常规消耗进一步包括返航维护的路程消耗；
    所述处理器进一步用于根据所述无人机的动力源和/或作业源的状态计算应进行返航维护的返航点；根据所述返航点的坐标和维护点的坐标计算所述返航维护的路程消耗。
  75. 根据权利要求74所述的系统，其特征在于，
    所述处理器进一步用于依次判断所述航线段的当前端点是否满足续航条件，若不满足续航条件，则将所述当前端点作为返航点。
  76. 根据权利要求74所述的系统，其特征在于，
    所述处理器进一步用于依次判断所述航线段的当前端点是否满足续航条件；若不满足续航条件，则判断所述当前端点与下一端点之间是否为需 作业的航线段；若是，则在所述当前端点与所述下一端点之间的航线段上寻找返航点，以使得所述无人机飞到所述返航点后仍能够安全返回所述维护点，若否，则将所述当前端点作为返航点。
  77. 根据权利要求75或76所述的系统，其特征在于，
    所述续航条件是指所述无人机飞到所述当前端点的动力源和/或作业源的剩余量减去预设的安全量大于或等于从所述当前端点飞往下一所述端点且从下一所述端点飞往所述返航点的动力源和/或作业源消耗量。
  78. 根据权利要求77所述的系统，其特征在于，
    所述处理器进一步用于根据所述无人机的剩余飞行时间、维护设备或人员的运动路径、当前位置以及运动速度计算所述维护设备或人员在所述无人机的剩余飞行时间内的最大运动范围；在所述最大运动范围内指定所述维护点。
  79. 根据权利要求78所述的系统，其特征在于，
    所述处理器进一步用于将所述最大运动范围内与所述返航点或所述下一端点之间的动力源和/或作业源消耗最小或飞行距离最近的点作为所述维护点。
  80. 根据权利要求65所述的系统，其特征在于，
    所述处理器进一步用于获取用于表示所述预设飞行区域中的特殊作业区域的边缘的特殊边缘点的坐标；计算所述航线段与所述特殊作业区域的边缘的交点；将所述航线段与所述特殊作业区域的边缘的交点插入所述飞行路径，以使所述无人机在所述特殊作业区域内执行的操作不同于所述无人机在所述特殊作业区域外执行的操作。
  81. 根据权利要求80所述的系统，其特征在于，
    所述特殊作业区域包括可飞行的非作业区域、高空飞行区域和低空飞行区域中的至少一种。
  82. 根据权利要求80所述的系统，其特征在于，
    所述处理器进一步用于形成用于所述无人机的飞行路径的多个任务点的坐标序列，以使得所述无人机按照所述坐标序列在所述多个任务点之间进行作业，其中所述任务点至少包括所述航线段的端点、所述航线段与所述特殊作业区域的边缘的交点。
  83. 一种无人机的控制系统，其特征在于，包括：
    一个或多个处理器，单独或协同工作，所述处理器用于：
    按照预设航线在预设飞行区域内进行飞行作业；
    调整所述无人机的飞行路径和/或执行的操作，以使所述无人机适合当前的作业环境或工作状态。
  84. 根据权利要求83所述的系统，其特征在于，进一步包括定位装置，所述定位装置与所述处理器通讯连接；
    所述定位装置用于获取所述无人机的当前位置信息，并将所述当前位置信息传送给所述处理器，所述处理器根据所述当前位置信息以及所述预设航线控制所述无人机进行飞行作业。
  85. 根据权利要求83或84所述的系统，其特征在于，
    所述调整所述无人机的飞行路径包括如下至少一种：改变飞行方向、改变飞行高度、停止飞行；
    所述调整所述无人机执行的操作包括如下任意一种：停止作业，开始作业。
  86. 根据权利要求83或84所述的系统，其特征在于，
    所述作业环境包括如下至少一种：前方存在障碍物、前方存在非作业区域、前方为已作业区域；
    所述工作状态包括如下至少一种：动力源不足、作业源不足、导航信号丢失、接收到控制命令。
  87. 根据权利要求83或84所述的系统，其特征在于，所述工作状态包括动力源不足和/或作业源不足，所述调整所述无人机的飞行路径包括改变 飞行方向以飞往维护点；
    所述处理器进一步用于获取所述无人机的动力源和/或作业源的状态；根据所述无人机的动力源和/或作业源状态计算应进行返航维护的返航点；在所述无人机飞行至所述返航点时改变飞行方向以飞往所述维护点进行维护。
  88. 根据权利要求87所述的系统，其特征在于，
    所述处理器进一步用于获取所述无人机的当前位置坐标；判断所述无人机的当前位置坐标是否对应用于表示无人机飞行路径的多个任务点中的某个任务点；若是，则获取所述无人机的动力源和/或作业源的状态。
  89. 根据权利要求88所述的系统，其特征在于，
    所述处理器进一步用于判断所述无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则将所述无人机的当前位置作为返航点。
  90. 根据权利要求88所述的系统，其特征在于，
    所述处理器进一步用于判断所述无人机的动力源和/或作业源的状态是否满足续航条件；若不满足续航条件，则判断所述无人机的当前位置与所述无人机的当前位置对应的任务点的下一任务点之间是否为需作业的航线段；若是，则在所述无人机的当前位置与所述下一任务点之间的航线段上寻找返航点，以使得所述无人机飞到所述返航点后仍能够安全返回所述维护点，若否，则将所述无人机的当前位置作为返航点。
  91. 根据权利要求89或90所述的方法，其特征在于，
    所述续航条件是指所述无人机的动力源和/或作业源的剩余量减去预设的安全量大于或等于从所述无人机的当前位置飞往所述无人机的当前位置对应的任务点的下一任务点的动力源和/或作业源的消耗。
  92. 根据权利要求87所述的系统，其特征在于，
    所述处理器进一步用于根据所述无人机的动力源和/或作业源的状态预 估所述无人机的剩余飞行时间；根据维护设备或人员的当前位置、运动路径以及运动速度计算所述维护设备或人员在所述预估剩余飞行时间内的最大运动范围；在所述最大运动范围内指定所述维护点。
  93. 根据权利要求92所述的系统，其特征在于，
    所述处理器进一步用于根据所述无人机的动力源和/或作业源的状态预估返航点的位置；将所述最大运动范围内与所述预估的返航点之间动力源消耗最小或飞行距离最近的点作为所述维护点。
  94. 一种无人机的控制系统，其特征在于，包括：
    一个或多个处理器，单独或协同工作，所述处理器用于：
    获取无人机的当前维护点的位置；
    根据所述当前维护点的位置，确定下一维护点的位置；
    将所述下一维护点的位置发送给维护设备或人员。
  95. 根据权利要求94所述的方法，其特征在于，
    所述当前维护点为所述无人机上次进行维护的维护点；
    所述处理器进一步用于根据所述当前维护点的位置、所述无人机的预估最大飞行时间、所述维护设备或人员的运动路径以及运动速度计算所述维护设备或人员在所述预估最大飞行时间内的最大运动范围；在所述最大运动范围内确定所述下一维护点的位置。
  96. 根据权利要求95所述的方法，其特征在于，
    所述处理器进一步用于根据所述当前维护点的位置以及所述无人机的飞行路径和预估最大飞行时间，预估所述无人机从所述当前维护点起下一次应进行返航维护的下一返航点；将所述最大运动范围内与所述下一返航点之间动力源消耗最小或飞行距离最近的点作为所述下一维护点。
  97. 根据权利要求94所述的方法，其特征在于，
    所述当前维护点的位置为所述维护设备或人员的当前位置；
    所述处理器进一步用于根据所述无人机的动力源和/或作业源的状态预 估所述无人机的剩余飞行时间；根据所述维护设备或人员的当前位置、运动路径以及运动速度计算所述维护设备或人员在所述预估最大飞行时间内的最大运动范围；在所述最大运动范围内确定所述下一维护点的位置。
  98. 根据权利要求97所述的方法，其特征在于，
    所述处理器进一步用于根据所述无人机的动力源和/或作业源的状态预估返航点的位置；将所述最大运动范围内与所述预估的返航点之间动力源消耗最小或飞行距离最近的点作为所述下一维护点。
  99. 一种无人机的飞行路径规划系统，其特征在于，包括：
    显示屏，用于显示预设飞行区域的地貌图像；
    一个或多个处理器，单独或协同工作，所述处理器与所述显示屏通讯连接；
    所述处理器用于获取用于表示所述预设飞行区域的地貌图像的边界的多个特征点的坐标；根据所述多个特征点的坐标信息，确定所述预设飞行区域的边缘线。
  100. 根据权利要求99所述的系统，其特征在于，进一步包括输入装置，所述输入装置与所述处理器通讯连接，用于接收用于输入的表示所述预设飞行区域的地貌图像的边界的多个特征点并获取其坐标。
  101. 根据权利要求99所述的系统，其特征在于，
    所述处理器进一步用于通过图像识别从所述地貌图像中提取所述多个特征点的坐标。
  102. 根据权利要求99-101中任一项所述的系统，其特征在于，
    所述特征点包括用于表示所述预设飞行区域的外部轮廓的多个第一特征点，以及用于表示所述预设飞行区域内障碍物区域的外部轮廓的第二特征点。
  103. 根据权利要求99-102中任一项所述的系统，其特征在于，
    所述处理器进一步用于利用线段连接所述特征点并将形成的折线作为 所述边缘线。
  104. 根据权利要求99-102中任一项所述的系统，其特征在于，
    所述处理器进一步用于以所述特征点中的至少一个作为圆心作圆或者椭圆，并将所述圆或者椭圆的全部或者部分作为所述边缘线。
  105. 根据权利要求99-104中任一项所述的系统，其特征在于，
    所述处理器进一步用于根据所述边缘线，确定所述无人机在所述预设飞行区域内的飞行路径；显示所述飞行路径。
  106. 根据权利要求105所述的系统，其特征在于，
    所述处理器进一步用于获取无人机的作业参数；根据所述边缘线和所述无人机的作业参数，确定所述无人机在所述预设飞行区域内的飞行路径。
  107. 根据权利要求106所述的系统，其特征在于，
    所述作业参数至少包括风向、所述无人机的作业宽度和起始点。
  108. 根据权利要求107所述的系统，其特征在于，
    所述处理器进一步用于根据所述线和所述无人机的作业宽度，获取所述预设飞行区域内的平行于所述无人机的飞行方向的多条航线段，其中所述无人机的飞行方向根据所述风向确定；根据所述航线段与所述边界的相交位置，将所述预设飞行区域划分至多个作业区域；根据所述多个作业区域，确定所述无人机在所述预设飞行区域内的飞行路径。
  109. 根据权利要求108所述的系统，其特征在于，
    所述处理器进一步用于确定可作为各所述作业区域的入口点或出口点的端口点；根据所述无人机的起始点和所述端口点计算遍历所有所述作业区域的多种候选连接路径的非常规消耗，其中所述非常规消耗至少包括不同所述作业区域之间的路程消耗以及从所述起始点飞往所述作业区域的路程消耗；选择所述非常规消耗最小的一种候选连接路径作为所述无人机的飞行路径。
  110. 根据权利要求109所述的系统，其特征在于，
    所述非常规消耗进一步包括返航维护的路程消耗；
    所述处理器进一步用于根据所述无人机的工作状态计算应进行返航维护的返航点；根据所述返航点的坐标和维护点的坐标计算所述返航维护的路程消耗。
  111. 根据权利要求110所述的系统，其特征在于，
    所述处理器进一步用于控制所述显示屏显示所述飞行路径，并显示所述返航点的数量和/或在所述飞行路径中标出所述返航点。
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