CN113624235A - Method for dynamically adjusting navigation path in real time by unmanned aerial vehicle - Google Patents
Method for dynamically adjusting navigation path in real time by unmanned aerial vehicle Download PDFInfo
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- CN113624235A CN113624235A CN202110878209.8A CN202110878209A CN113624235A CN 113624235 A CN113624235 A CN 113624235A CN 202110878209 A CN202110878209 A CN 202110878209A CN 113624235 A CN113624235 A CN 113624235A
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- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000004927 fusion Effects 0.000 claims abstract description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000009429 distress Effects 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 239000007787 solid Substances 0.000 claims description 2
- 230000004888 barrier function Effects 0.000 abstract 1
- 230000000694 effects Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
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- 238000005457 optimization Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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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
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- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Aviation & Aerospace Engineering (AREA)
- Navigation (AREA)
Abstract
The invention discloses a method for dynamically adjusting a navigation path in real time by an unmanned aerial vehicle, which generates an equivalent digital elevation model according to the fusion of an original earth surface digital elevation model of a flight area and obstacle information; selecting a navigation starting point and a navigation end point in the equivalent digital elevation model, and sequentially setting a plurality of intermediate points of a random route; constructing a constraint condition according to the unmanned aerial vehicle navigation height, the barrier information and the maximum range; and a navigation cost model is established according to the constraint conditions and the air route, and an optimal navigation path is selected according to the navigation cost model, so that the flexibility and safety of the unmanned aerial vehicle can be effectively improved.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle navigation, and particularly relates to a method for dynamically adjusting a navigation path in real time by an unmanned aerial vehicle.
Background
At present, the development of the unmanned aerial vehicle technology is very rapid, certain progress is made in the aspects of military use, civil use and the like, and the unmanned aerial vehicle mainly depends on the advantages of relative safety, high maneuverability, lower cost, flexible operation and the like of the unmanned aerial vehicle. However, the unmanned aerial vehicle transportation technology is difficult to popularize nationwide, and the application region is limited, mainly because universal air traffic control standards and schemes for unmanned aerial vehicles in a unified way, a standardized airline and route planning theory between the deficient navigation points, the lack of obstacle avoidance technology and the like are not established, and the marketized application scale of the unmanned aerial vehicle technology is limited. In the flight process of the unmanned aerial vehicle, the flight path of the unmanned aerial vehicle is generally calculated and fixed before flight, and the flight path needs to be optimized due to reasons such as traffic control, severe weather, obstacles, self electric quantity loss and the like.
Disclosure of Invention
Aiming at the defects and shortcomings of the existing unmanned aerial vehicle navigation method, the invention provides an optimized method for dynamically adjusting the navigation path of the unmanned aerial vehicle in real time, which can improve the real-time performance and accuracy of unmanned aerial vehicle navigation, optimize the flight path and reduce the use cost of the unmanned aerial vehicle.
The technical scheme adopted by the invention for solving the technical problems is as follows:
s1, generating an equivalent digital elevation model according to the original earth surface digital elevation model of the flight area and the obstacle information fusion;
s2, selecting a navigation starting point and a navigation end point in the equivalent digital elevation model, and sequentially setting a plurality of intermediate points of a random route;
s3, constructing a constraint condition according to the unmanned aerial vehicle navigation height, the obstacle information and the maximum range;
and S4, constructing a navigation cost model according to the constraint conditions and the air route, and selecting the optimal navigation path according to the navigation cost model.
Furthermore, the constraint conditions further comprise a horizontal rotation angle, a pitch angle, a minimum step length of a flight line and a safe distance of a flight point.
Further, before step S1, the drone receives the flight no-fly zone and the extreme weather information sent by the server in real time.
Further, after step S1, the unmanned aerial vehicle receives the information of the no-fly zone and the extreme weather sent by the server in real time, and fuses the information into the equivalent digital elevation model to generate an optimized equivalent digital elevation model.
Further, the cost model comprises a range cost, a height cost and a safety cost.
Further, the cost model specifically includes:
wherein, w1，w2，w3Is an index weight, and w1+w2+w3＝1，w1，w2，w3Not less than 0; i is a point in the flight path, and j represents the midpoint of the feasible region of i; dijRepresenting the distance between points i, j, g representing the acceleration of gravity, m representing the mass of the drone, Δ zijDenotes the altitude between i, j, dijAnd represents the sum of plane straight line distances of the plane center of the no-fly zone.
Furthermore, after the starting point and the end point of the unmanned aerial vehicle flight area are set, the whole route can be calculated according to the variable step size three-dimensional A-star algorithm, and meanwhile, the local route is calculated according to the cost model and the whole route and the three-dimensional artificial potential field algorithm;
further, set up GPS autonomous navigation system and sensor on the unmanned aerial vehicle, the sensor includes: the system comprises a magnetometer, a three-axis accelerometer, a barometric altimeter and a three-axis gyroscope, and is used for acquiring a real-time pitch angle according to a sensor and adjusting the flight attitude.
Further, when the GPS signal is weak, an infrared camera on the unmanned aerial vehicle can be started, and the unmanned aerial vehicle can fly away according to the information of the front obstacle in the distance acquired by the infrared camera.
Further, when the GPS signal is invalid, the unmanned aerial vehicle will search for flat ground to land, and after landing, the unmanned aerial vehicle will send distress signals outwards.
The invention has the following beneficial effects:
the method can improve the real-time performance and the accuracy of unmanned aerial vehicle navigation, optimize the flight path and reduce the use cost of the unmanned aerial vehicle, has the characteristics of accurate positioning effect, strong path optimization and the like, and simultaneously realizes the safe flight of the unmanned aerial vehicle when the GPS signal is weak.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above description and other objects, features, and advantages of the present invention more clearly understandable, preferred embodiments are specifically described below.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of an embodiment of a method for dynamically adjusting a navigation path in real time by an unmanned aerial vehicle according to the present invention
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be connected or detachably connected or integrated; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Example 1
The method for dynamically adjusting the navigation path in real time by the unmanned aerial vehicle comprises the following steps: s1, generating an equivalent digital elevation model according to the original earth surface digital elevation model of the flight area and the obstacle information fusion;
s2, selecting a navigation starting point and a navigation end point in the equivalent digital elevation model, and sequentially setting a plurality of intermediate points of a random route;
s3, constructing a constraint condition according to the unmanned aerial vehicle navigation height, the obstacle information and the maximum range;
and S4, constructing a navigation cost model according to the constraint conditions and the air route, and selecting the optimal navigation path according to the navigation cost model.
Furthermore, the constraint conditions further comprise a horizontal rotation angle, a pitch angle, a minimum step length of a flight line and a safe distance of a flight point.
Further, before step S1, the drone receives the flight no-fly zone and the extreme weather information sent by the server in real time.
Further, after step S1, the unmanned aerial vehicle receives the information of the no-fly zone and the extreme weather sent by the server in real time, and fuses the information into the equivalent digital elevation model to generate an optimized equivalent digital elevation model.
Further, the cost model comprises a range cost, a height cost and a safety cost.
Further, the cost model specifically includes:
wherein, w1，w2，w3Is an index weight, and w1+w2+w3＝1，w1，w2，w3Not less than 0; i is a point in the flight path, and j represents the midpoint of the feasible region of i; dijRepresenting the distance between points i, j, g representing the acceleration of gravity, m representing the mass of the drone, Δ zijDenotes the altitude between i, j, dijAnd represents the sum of plane straight line distances of the plane center of the no-fly zone.
Furthermore, after the starting point and the end point of the unmanned aerial vehicle flight area are set, the whole route can be calculated according to the variable step size three-dimensional A-star algorithm, and meanwhile, the local route is calculated according to the cost model and the whole route and the three-dimensional artificial potential field algorithm;
further, set up GPS autonomous navigation system and sensor on the unmanned aerial vehicle, the sensor includes: the system comprises a magnetometer, a three-axis accelerometer, a barometric altimeter and a three-axis gyroscope, and is used for acquiring a real-time pitch angle according to a sensor and adjusting the flight attitude.
Further, when the GPS signal is weak, an infrared camera on the unmanned aerial vehicle can be started, and the unmanned aerial vehicle can fly away according to the information of the front obstacle in the distance acquired by the infrared camera.
Further, when the GPS signal is invalid, the unmanned aerial vehicle will search for flat ground to land, and after landing, the unmanned aerial vehicle will send distress signals outwards.
Example 2
The method for dynamically adjusting the navigation path in real time by the unmanned aerial vehicle comprises the following steps:
s0, the unmanned aerial vehicle receives the information of the navigation no-fly zone and the extreme weather sent by the server in real time,
s1, generating an equivalent digital elevation model according to the original earth surface digital elevation model of the flight area and the obstacle information fusion;
s11, periodically fusing the information of the navigation no-fly zone and the extreme weather sent by the server and received by the unmanned aerial vehicle in real time into an equivalent digital elevation model to generate an optimized equivalent digital elevation model;
s2, selecting a navigation starting point and a navigation end point in the equivalent digital elevation model;
s3, constructing a constraint condition according to the unmanned aerial vehicle navigation height, the obstacle information and the maximum range;
s4, constructing a navigation cost model according to the constraint conditions and the route, and selecting an overall optimal navigation path according to the navigation cost model and a variable step size solid A-x algorithm;
and S5, calculating a local optimal route according to the cost model and the overall route by combining a three-dimensional artificial potential field algorithm.
The unmanned aerial vehicle variable-step-size three-dimensional A-x algorithm model specifically comprises the following steps:
min S(Cij)＝-w1·f1+w2·f2+w3·f3
wherein f is1Represents a security cost, f2Represents a height cost, f3Representing voyage cost, CijRepresenting points in the voyage, hminRepresents the minimum flying height, (x)k，yk) Central plane coordinates representing the k-th obstacle, for a total of λ, (x)goal，ygoal，Zgoal) Indicating the navigation endpoint.
The invention has the advantages that: the method has the advantages of being accurate in positioning effect, strong in path optimizing and the like, and meanwhile, when the GPS signal is weak, the unmanned aerial vehicle can fly safely.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for dynamically adjusting a navigation path in real time by an unmanned aerial vehicle is characterized by comprising the following steps:
s1, generating an equivalent digital elevation model according to the original earth surface digital elevation model of the flight area and the obstacle information fusion;
s2, selecting a navigation starting point and a navigation end point in the equivalent digital elevation model, and sequentially setting a plurality of intermediate points of a random route;
s3, constructing a constraint condition according to the unmanned aerial vehicle navigation height, the obstacle information and the maximum range;
and S4, constructing a navigation cost model according to the constraint conditions and the air route, and selecting the optimal navigation path according to the navigation cost model.
2. The method for real-time dynamic adjustment of a navigation path by an unmanned aerial vehicle according to claim 1, wherein the constraint conditions further include a horizontal rotation angle, a pitch angle, a minimum step length of a flight path, and a safe distance of a flight point.
3. The method for real-time dynamic adjustment of the navigation path by the unmanned aerial vehicle according to claim 1, wherein before step S1, the unmanned aerial vehicle receives the flight barring area and the extreme weather information sent by the server in real time.
4. The method for real-time dynamic adjustment of the navigation path by the unmanned aerial vehicle as claimed in claim 3, wherein after step S1, the navigation no-fly zone and the extreme weather information sent by the real-time unmanned aerial vehicle receiving server are merged into the equivalent digital elevation model to generate the optimized equivalent digital elevation model.
5. The method for real-time dynamic adjustment of a navigation path by a drone of claim 1, wherein the cost model includes a range cost, an altitude cost, and a safety cost.
6. The method for real-time dynamic adjustment of the navigation path by the unmanned aerial vehicle according to claim 5, wherein the cost model is specifically:
wherein, w1，w2，w3Is an index weight, and w1+w2+w3＝1，w1，w2，w3Not less than 0; i is a point in the flight path, and j represents the midpoint of the feasible region of i; dijRepresenting the distance between points i, j, g representing the acceleration of gravity, yin representing the mass of the drone, Δ zijDenotes the altitude between i, j, dijAnd represents the sum of plane straight line distances of the plane center of the no-fly zone.
7. The method for real-time dynamic adjustment of the navigation path by the unmanned aerial vehicle as claimed in claim 1, wherein after the start point and the end point of the flight area of the unmanned aerial vehicle are set, the whole flight path is calculated according to a variable step size solid a x algorithm, and meanwhile, the local flight path is calculated according to a cost model and the whole flight path in combination with a three-dimensional artificial potential field algorithm.
8. The method for real-time dynamic adjustment of the navigation path by the unmanned aerial vehicle according to claim 1, wherein a GPS autonomous navigation system and sensors are provided on the unmanned aerial vehicle, and the sensors comprise: the system comprises a magnetometer, a three-axis accelerometer, a barometric altimeter and a three-axis gyroscope, and is used for acquiring a real-time pitch angle according to a sensor and adjusting the flight attitude.
9. The method for the unmanned aerial vehicle to dynamically adjust the navigation path in real time according to claim 1, wherein when a GPS signal is weak, an infrared camera on the unmanned aerial vehicle can be turned on, and evasive flight is performed according to information of an obstacle ahead of the distance acquired by the infrared camera.
10. The method for real-time dynamic adjustment of the navigation path by the unmanned aerial vehicle according to claim 1, wherein when the GPS signal fails, the unmanned aerial vehicle will search for a flat ground for landing, and after landing, the unmanned aerial vehicle will send a distress signal to the outside.
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