CN117289301A - Air-ground unmanned platform collaborative path planning method under unknown off-road scene - Google Patents

Air-ground unmanned platform collaborative path planning method under unknown off-road scene Download PDF

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CN117289301A
CN117289301A CN202311214146.1A CN202311214146A CN117289301A CN 117289301 A CN117289301 A CN 117289301A CN 202311214146 A CN202311214146 A CN 202311214146A CN 117289301 A CN117289301 A CN 117289301A
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grid
unmanned
aerial vehicle
unmanned aerial
local
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宋文杰
王容川
付梦印
毛梓豪
侯胜宇
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Beijing Institute of Technology BIT
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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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • 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
    • G01C21/1652—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 with ranging devices, e.g. LIDAR or RADAR
    • 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
    • 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
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/933—Lidar systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
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  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Automation & Control Theory (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a method for planning a collaborative path of an air-ground unmanned platform in an unknown off-road scene, which comprises the steps that an unmanned aerial vehicle carries a laser radar to construct an unmanned aerial vehicle local risk map in a overlooking view at the current moment; according to the unmanned aerial vehicle local risk map, based on an active exploration strategy, extracting prospective navigation points of the unmanned aerial vehicle and the current moment of the unmanned aerial vehicle; generating an unmanned aerial vehicle flight path and providing a reference path for the unmanned aerial vehicle according to the prospective navigation point; the unmanned vehicle generates a driving path according to the reference path by combining local terrain risk perception information and vehicle kinematic characteristics; finally, repeating the steps until the unmanned vehicles and the unmanned aerial vehicle successfully reach the target destination of the navigation task; the method is a robust collaborative track planning method, and can realize that the unmanned air-ground platform can safely, efficiently and reliably complete collaborative navigation tasks.

Description

Air-ground unmanned platform collaborative path planning method under unknown off-road scene
Technical Field
The invention belongs to the technical field of multi-agent cooperative path planning, and particularly relates to a space unmanned platform cooperative path planning method under an unknown off-road scene.
Background
The unmanned vehicle autonomous intelligent navigation technology in unknown off-road scenes is always a hot problem of concern at home and abroad facing application requirements of disaster relief, battlefield support, material transportation and the like. However, compared with the structured scenes such as urban traffic, factory logistics and the like with high-precision global maps or priori data rules, in an unknown off-road environment, autonomous navigation of an unmanned vehicle faces the problems of lack of priori information, limited perception field of view and the like, and a safe, efficient and reliable navigation path is difficult to generate only by means of a single unmanned vehicle platform.
At present, with the development of unmanned aerial vehicle technology and multi-agent cooperative technology, a multi-agent cooperative system combining unmanned aerial vehicles and unmanned aerial vehicles has been widely used. Compared with an unmanned vehicle, the unmanned vehicle has stronger trafficability and flexibility, can fly over special obstacles existing in various off-road scenes such as steep slopes and boulders, and can carry depth sensors such as laser radars and depth cameras to acquire off-road terrain information under a larger visual field range from overlooking view. Therefore, the unmanned aerial vehicle can be used as the third eye in the air by combining the advantages of the unmanned aerial vehicle and the unmanned aerial vehicle, and the visual field advantage is utilized to plan the driving path of the unmanned aerial vehicle from the overlooking view angle so as to guide the unmanned aerial vehicle to go to the navigation task target. And then, the unmanned aerial vehicle optimizes the driving path provided by the unmanned aerial vehicle in real time by combining the vehicle motion characteristics and high-precision local environment perception information, so that safe, efficient and reliable autonomous navigation under an unknown off-road environment is realized.
Disclosure of Invention
In view of the above, the invention provides a collaborative path planning method for an air-ground unmanned platform under an unknown off-road scene, so that unmanned vehicles can fuse cross-view path planning information from unmanned vehicles and cooperatively reach a navigation task end point.
A method for planning a cooperative path of an air-ground unmanned platform in an unknown off-road scene comprises the following steps:
step 1: the unmanned aerial vehicle builds a local risk map of the unmanned aerial vehicle according to measurement information of the overlooking laser radar sensor at the current moment
Step 2: local risk map according to current moment of unmanned aerial vehicleExtracting a prospective navigation point Gcur at the next moment, and calculating a reference path of the unmanned aerial vehicle reaching the prospective navigation point +.>
Step 3: the unmanned aerial vehicle establishes a local risk map of the unmanned aerial vehicle according to the measurement information of the round-the-clock laser radar sensor at the current momentAccording to the unmanned vehicle local risk map and the reference path +.>Optimizing the motion trail in real time and performing control tracking to obtain the final driving path +.>
Step 4: and repeatedly executing the steps until the unmanned aerial vehicle and the unmanned aerial vehicle reach the target end point of the collaborative navigation task.
Preferably, the step 1 specifically includes:
positioning information and overlooking laser output by forward vision inertial sensor of unmanned aerial vehiclePoint cloud measurement information output by radar is used for establishing a local point cloud map of the unmanned aerial vehicle at t moment
According to the set resolution ratio, the unmanned plane t moment local point cloud mapRasterizing, wherein each grid c i Height value z of (2) i To be set to the maximum height of the point cloud within the grid; grid c in a local grid map for an unmanned aerial vehicle i Two topographical feature parameters of the grid are calculated: height jump value h i And slope value s i ；
Calculating each grid c according to the local grid map of the unmanned aerial vehicle after the topographic feature parameter calculation is completed i Risk value r of (2) i The formula is as follows:
wherein omega 1 、ω 2 Is the weight value of 1 added up and h crit 、s crit Threshold values for various terrain parameters; calculating each grid risk value according to a calculation formula to obtain a local risk map of the unmanned plane at the time t
Preferably, in the step 1, the height jump value h i The calculation method comprises the following steps:
calculating the height difference delta h between the current grid and the adjacent grids within the set distance range d around the current grid i For all height differences Δh i And sorting from large to small, and taking the maximum value as the height jump value of the current grid.
Preferably, in the step 1, the gradient value s i The calculation method of (1) comprises the following steps:
by principal component analysisBy a method of at a grid c i Fitting around a local plane pi i Obtaining the normal vector n of the plane i Calculating a algorithm vector n i The angle with the Z axis of the global coordinate system is used as the gradient value s of the grid i 。
Preferably, the step 2 specifically includes:
local risk map of unmanned aerial vehicle at time tLocal risk map with time t-1 +.>Comparing to obtain grid +.>Therefore, a new risk sub-map of the unmanned aerial vehicle at the moment t is obtained>And will->Each grid of the plurality of grids is stored in a queue Q to be detected t ；
For queue Q t Each grid of (a)Judging whether the edge grid is a valid edge grid according to the following judging conditions: if the grid risk value satisfies->r safe To set the threshold, any one of four grids adjacent to each other in the vertical direction is not present in the local risk map +.>In (1), consider the grid->Is an effective edge grid and is denoted +.>Traversing queue Q t To obtain an effective edge grid set +.>Subsequently +.>Obtaining corresponding effective edge point ++using Euclidean distance clustering method>Thereby obtaining the total set of valid edge points +.>
For effective edge point collectionIs +.>The cost function is constructed as follows to calculate the cost value E i ：
Wherein omega r ，ω d ，ω h Respectively aiming at adjustable weight parameters in different scenes, R i To form edge pointsEdge grid set->Sum of risk values of grids d i Is the current position and edge point of the unmanned vehicleDistance, h i For edge points->And collaborative navigation task endpoint G final N is>The number of mid-edge points;
for the effective edge point setIs +.>According to the cost value E i Sequencing the sizes, and taking the edge point with the minimum cost value as a prospective navigation point G of the air-ground unmanned platform at the moment t cur Generating a flight path based on the existing PID algorithm for the unmanned aerial vehicle and controlling the forward looking navigation point G of the unmanned aerial vehicle cur 。
Preferably, in the step 2, when a valid edge grid is detectedUsing breadth-first search method, in local risk map +.>In the above, according to the above-mentioned determination condition, searching for another effective edge grid in the vicinity is started from the grid, and after the effective edge grid is obtained, searching for another effective edge grid in the vicinity is continued with the grid as a starting point.
Preferably, in the step 2, according to the local risk map of the unmanned aerial vehicle at the time tLook-ahead navigation point G cur Position +.>Calculating a reference path of an unmanned lane forward-looking navigation point by using a risk constraint-based A star path planning method>The cost function G (i) and the heuristic function H (i) of the grid in the a star algorithm are expressed as follows:
H(i)＝λ||C i -G cur || 2
wherein r is i For the risk value of the current grid, lambda is a factor parameter for adjusting the risk cost and the distance cost, I 2 Representing the Euclidean distance before calculating two points; c (C) i And C i+1 Representing grid c i And c i+1 Is defined by the coordinates of (a).
Preferably, in the step 3, according to the unmanned vehicle local risk map and the reference pathOptimizing the motion trail in real time and performing control tracking to obtain the final driving path +.>The specific method of (2) comprises the following steps:
according to the reference track provided by the unmanned aerial vehicle at the time tFrom the reference track->Extracting track control point set { Q } i -and using third-order B-spline curve for control point set { Q } i Optimizing, and the expression is:
wherein B is i.k (t) is a base function of a Bezier curve, M is the number of control points extracted from the track;
for control point set { Q i Constructing a quadratic programming optimization problem:
wherein,represents the optimized control point set lambda s And lambda (lambda) d Balancing the weighting parameters of the optimization objective, f d For the smoothness cost function, the expression is:
wherein f s Local risk map by unmanned vehicle as risk cost functionInquiring to obtain;
calculated according to the optimization methodObtaining a driving track of the unmanned vehicle reaching a forward looking navigation point at the current moment>And controlling the unmanned vehicle to track the track through a PID algorithm, and finally completing the collaborative path planning at the current moment.
The invention has the following beneficial effects:
aiming at the difficult problems that a single unmanned vehicle in an unknown off-road environment is difficult to generate a safe, efficient and reliable navigation path due to the constraint of a perception view angle and the limited perception range, the invention designs a cooperative path planning method of the unmanned vehicle and the unmanned vehicle; the method considers the advantages of the unmanned aerial vehicle in the motion capability and the perception view, designs an unmanned aerial vehicle active exploration method and an unmanned aerial vehicle guiding path calculation flow, combines the unmanned aerial vehicle local environment perception information and the vehicle kinematic characteristics on the unmanned aerial vehicle to perform track optimization, and is a robust collaborative track planning method; and finally, the steps of active exploration and path planning are repeatedly executed, so that the unmanned air-ground platform can safely, efficiently and reliably complete the collaborative navigation task.
Drawings
FIG. 1 is a flow chart of a collaborative path planning method of an air-ground unmanned platform in an unknown off-road scene;
FIG. 2 is a schematic diagram of a simulated off-road scenario and a simulated vacuum ground unmanned platform of the present invention.
Detailed Description
The invention will now be described in detail by way of example with reference to the accompanying drawings.
As shown in fig. 1, the invention relates to a method for planning a cooperative path of an air-ground unmanned platform in an unknown off-road scene, which comprises the following specific technical processes:
step 1: the unmanned aerial vehicle builds a local risk map of the unmanned aerial vehicle according to measurement information of the overlooking laser radar sensor at the current moment
Step 2: local risk map according to current moment of unmanned aerial vehicleExtracting a prospective navigation point G of the unmanned aerial vehicle and the next moment of the unmanned aerial vehicle cur Calculating a reference path of the unmanned vehicle to the prospective navigation point>
Step 3: the unmanned aerial vehicle establishes a local risk map of the unmanned aerial vehicle according to the measurement information of the round-the-clock laser radar sensor at the current momentAccording to the local risk map and the reference path of the unmanned vehicle->Optimizing the motion trail in real time and performing control tracking to obtain the final driving path +.>
Step 4: and repeatedly executing the steps until the unmanned aerial vehicle and the unmanned aerial vehicle reach the target end point of the collaborative navigation task.
Further, the step 1 specifically includes: positioning information output by a forward vision inertial sensor of the unmanned aerial vehicle and point cloud measurement information output by a overlooking laser radar establish a local point cloud map of the unmanned aerial vehicle at the moment t
Further, according to the set 0.4m large resolution, the local point cloud map of the unmanned plane at the t moment is mappedRasterizing, wherein each grid c i Height value z of (2) i Will be set to the maximum height of the point cloud within the grid. Grid c in a local grid map for an unmanned aerial vehicle i Two topographical feature parameters of the grid are calculated: height jump value h i And slope value s i 。
Further, calculating the height difference delta h of the adjacent grids within the distance range d between the current grid and the surrounding grid i For all height differences Δh i Sorting from big to small, taking the maximum value as the height jump value of the current gridNamely:
h i ＝max{Δh i }
further, the principal component analysis method is used for the grid c i Fitting around a local plane pi i Obtaining the normal vector n of the plane i Calculating a algorithm vector n i The angle with the Z axis of the global coordinate system is used as the gradient value s of the grid i 。
Further, calculating each grid c according to the local grid map of the unmanned aerial vehicle after the calculation of the topographic feature parameters is completed i Risk value r of (2) i The formula is as follows:
wherein omega 1 、ω 2 Is the weight value of 1 added up and h crit 、s crit Is a threshold value for each terrain parameter. Calculating each grid risk value according to a calculation formula to obtain a local risk map of the unmanned plane at the time t
Further, the step 2 specifically includes the following substeps:
2.1, mapping the local risk of the unmanned plane at the time tLocal risk map with time t-1 +.>Comparing to obtain grid +.>Therefore, a new risk sub-map of the unmanned aerial vehicle at the moment t is obtained>And will->Each grid of the plurality of grids is stored in a queue Q to be detected t 。
For gridsJudging whether the edge grid is a valid edge grid according to the following judging conditions: if the grid risk value satisfiesr safe To set the threshold, any one of four grids adjacent to each other in the vertical direction is not present in the local risk map +.>In (1), consider the grid->Is an effective edge grid and is denoted +.>
Further, when a valid edge grid is detectedUsing breadth-first search method, in local risk map +.>In the above, according to the above-mentioned judgment condition, searching for other effective edge grids in the vicinity from the grid and obtaining an effective edge grid set +.>After each judgment, detecting the grid mark; subsequently +.>Obtaining corresponding effective edge point ++using Euclidean distance clustering method>After the clustering is completed, the effective edge grid set is +.>All grids in (1) are marked as detected and are from queue Q t Is removed. When queue Q t When the method is empty, a final effective edge point set is obtained
Further, for the valid edge point setIs +.>The cost function is constructed as follows to calculate the cost value E i 。
Wherein omega r ，ω d ，ω h Respectively aiming at adjustable weight parameters in different scenes, R i To form edge pointsEdge grid set->Sum of risk values of grids d i Is the current position and edge point of the unmanned vehicleDistance, h i For edge points->And collaborative navigation task endpoint G final N is>Number of mid-edge points.
Further, for the valid edge point setIs +.>According to the cost value E i Sorting the sizes, and taking edge points with minimum cost value +.>Prospective navigation point G serving as air-ground unmanned platform at t moment cur Generating a flight path based on the existing PID algorithm for the unmanned aerial vehicle and controlling the forward looking navigation point G of the unmanned aerial vehicle cur 。
2.2, according to the local risk map of the unmanned aerial vehicle at the moment tLook-ahead navigation point G cur Position +.>Calculating a reference path of an unmanned lane forward-looking navigation point by using a risk constraint-based A star path planning method>The cost function G (i) and the heuristic function H (i) of the ith grid in the a-star algorithm can be expressed as follows:
H(i)＝λ||C i -G cur || 2
wherein r is i For the risk value of the current grid, lambda is a factor parameter for adjusting the risk cost and the distance cost, I 2 Representing the Euclidean distance before calculating two points; c (C) i And C i+1 Representing grid c i And c i+1 Is defined by the coordinates of (a).
Further, the step 3 specifically includes the following sub-steps:
3.1, establishing a local point cloud map of the unmanned vehicle at the moment t through point cloud measurement information output by the looking-around laser radar based on an open source laser SLAM algorithm
Further, according to the set 0.1m small resolution, the unmanned vehicle t moment local point cloud mapPerforming rasterization, setting a grid height value according to the same construction flow as the unmanned aerial vehicle local map in the step 1, calculating the terrain risk characteristic parameters of the grid, and finally obtaining the unmanned aerial vehicle local risk map +.>
3.2, according to the reference track provided by the unmanned plane at the time tFrom the reference track->Extracting track control point set { Q } i -and using third-order B-spline curve for control point set { Q } i Optimizing, and the expression is:
wherein B is i，k (t) is a base function of the Bezier curve, M is the number of control points extracted from the trajectory.
Further, for the control point set { Q i Constructing a quadratic programming optimization problem:
wherein,represents the optimized control point set lambda s And lambda (lambda) d Balancing the weighting parameters of the optimization objective, f d For the smoothness cost function, the expression is as follows:
f s for the risk cost function, the safety of the motion control track is improved by a local risk map of the unmanned vehicleAnd inquiring to obtain the product.
3.3, calculating according to the optimization methodObtaining a driving track of the unmanned vehicle reaching a forward looking navigation point at the current moment>And controlling the unmanned vehicle to track the track through a PID algorithm, and finally completing the collaborative path planning at the current moment.
Examples:
the present invention uses an unmanned platform simulating off-road terrain and vacuum ground as shown in fig. 2. Wherein the coverage area of the unmanned aerial vehicle local risk map at each moment is 50 m-50 m,the coverage area of the unmanned aerial vehicle local risk map is 10m x 10m, the resolution of the unmanned aerial vehicle local risk map is 0.4m, and the resolution of the unmanned aerial vehicle local risk map is 0.1m. Furthermore, the weight ω for the local risk value calculation 1 、ω 2 Respectively 0.5 and 0.5, for calculating weight omega of edge point cost value r 、ω d 、ω h Respectively 1/3, 1/3 and 1/3 for the unmanned vehicle track optimization weight lambda s 、λ d 0.5 and 0.5, respectively. The quantitative comparison result of the existing method and the space unmanned platform collaborative path planning method provided by the invention is as follows:
algorithm Total length of path Total risk value of path
The invention is that 103.6m 61.3
Existing methods 152.3m 142.1
According to simulation results, the method for planning the cooperative path of the unmanned aerial vehicle platform in the air space under the unknown off-road scene can reduce the total path length to 103.6m and the total path risk value to 61.3, and the method utilizes the advantages of the unmanned aerial vehicle to actively explore and guide the path of the unmanned aerial vehicle from the air view angle, so that the unmanned aerial vehicle can safely, efficiently and reliably complete navigation tasks in the field environment.
In summary, the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. The method for planning the cooperative path of the unmanned air-ground platform in the unknown off-road scene is characterized by comprising the following steps:
step 1: the unmanned aerial vehicle builds a local risk map of the unmanned aerial vehicle according to measurement information of the overlooking laser radar sensor at the current moment
Step 2: local risk map according to current moment of unmanned aerial vehicleExtracting a look-ahead navigation point G at the next moment cur Calculating a reference path of the unmanned vehicle to the prospective navigation point>
Step 3: the unmanned aerial vehicle establishes a local risk map of the unmanned aerial vehicle according to the measurement information of the round-the-clock laser radar sensor at the current momentAccording to the unmanned vehicle local risk map and the reference path +.>Optimizing the motion trail in real time and performing control tracking to obtain the final driving path +.>
Step 4: and repeatedly executing the steps until the unmanned aerial vehicle and the unmanned aerial vehicle reach the target end point of the collaborative navigation task.
2. The method for planning the collaborative path of the open-air unmanned platform in the unknown off-road scene as set forth in claim 1, wherein the step 1 specifically includes:
positioning information output by a forward vision inertial sensor of the unmanned aerial vehicle and point cloud measurement information output by a overlooking laser radar establish a local point cloud map of the unmanned aerial vehicle at the moment t
According to the set resolution ratio, the unmanned plane t moment local point cloud mapRasterizing, wherein each grid c i Height value z of (2) i To be set to the maximum height of the point cloud within the grid; grid c in a local grid map for an unmanned aerial vehicle i Two topographical feature parameters of the grid are calculated: height jump value h i And slope value s i ；
Calculating each grid c according to the local grid map of the unmanned aerial vehicle after the topographic feature parameter calculation is completed i Risk value r of (2) i The formula is as follows:
wherein omega 1 、ω 2 Is the weight value of 1 added up and h crit 、s crit Threshold values for various terrain parameters; calculating each grid risk value according to a calculation formula to obtain a local risk map of the unmanned plane at the time t
3. A device according to claim 2The method for planning the cooperative path of the open-air unmanned platform in the unknown off-road scene is characterized in that in the step 1, the height jump value h i The calculation method comprises the following steps:
calculating the height difference delta h between the current grid and the adjacent grids within the set distance range d around the current grid i For all height differences Δh i And sorting from large to small, and taking the maximum value as the height jump value of the current grid.
4. The method for collaborative path planning for an open-air unmanned platform in an unknown off-road scene according to claim 2, wherein in step 1, the slope value s i The calculation method of (1) comprises the following steps:
on grid c by principal component analysis i Fitting around a local plane pi i Obtaining the normal vector n of the plane i Calculating a algorithm vector n i The angle with the Z axis of the global coordinate system is used as the gradient value s of the grid i 。
5. The method for planning the collaborative path of the open-air unmanned platform in the unknown off-road scene as set forth in claim 1, wherein the step 2 specifically includes:
local risk map of unmanned aerial vehicle at time tLocal risk map with time t-1 +.>Comparing to obtain grid +.>Therefore, a new risk sub-map of the unmanned aerial vehicle at the moment t is obtained>And will->Each grid of the plurality of grids is stored in a queue Q to be detected t ；
For queue Q t Each grid of (a)Judging whether the edge grid is a valid edge grid according to the following judging conditions: if the grid risk value satisfies->r safe To set the threshold, any one of four grids adjacent to each other in the vertical direction is not present in the local risk map +.>In (1), consider the grid->Is an effective edge grid and is denoted +.>Traversing queue Q t To obtain an effective edge grid set +.>Subsequently +.>Obtaining corresponding effective edge point ++using Euclidean distance clustering method>Thereby obtaining the total set of valid edge points +.>
For effective edge point collectionIs +.>The cost function is constructed as follows to calculate the cost value E i ：
Wherein omega r ，ω d ，ω h Respectively aiming at adjustable weight parameters in different scenes, R i To form edge pointsEdge grid set->Sum of risk values of grids d i Is the current position and edge point of the unmanned vehicle +.>Distance, h i For edge points->And collaborative navigation task endpoint G final N is>The number of mid-edge points;
for the effective edge point setIs +.>According to the cost value E i Sequencing the sizes, and taking the edge point with the minimum cost value as a prospective navigation point G of the air-ground unmanned platform at the moment t cur Generating a flight path based on the existing PID algorithm for the unmanned aerial vehicle and controlling the forward looking navigation point G of the unmanned aerial vehicle cur 。
6. The method for collaborative path planning for an open space unmanned platform in an unknown off-road scene according to claim 5, wherein in step 2, when a valid edge grid is detectedUsing breadth-first search method, in local risk map +.>In the above, according to the above-mentioned determination condition, searching for another effective edge grid in the vicinity is started from the grid, and after the effective edge grid is obtained, searching for another effective edge grid in the vicinity is continued with the grid as a starting point.
7. The method for collaborative path planning for an open-air unmanned platform in an unknown off-road scene as defined in claim 5, wherein in step 2, the unmanned aerial vehicle is based on a local risk map at time tLook-ahead navigation point G cur Position +.>Calculating a reference path of an unmanned lane forward-looking navigation point by using a risk constraint-based A star path planning method>The cost function G (i) and the heuristic function H (i) of the grid in the a star algorithm are expressed as follows:
H(i)＝λ‖C i -G cur ‖ 2
wherein r is i For the risk value of the current grid, lambda is a factor parameter for adjusting the risk cost and the distance cost, and II 2 Representing the Euclidean distance before calculating two points; c (C) i And C i+1 Representing grid c i And c i+1 Is defined by the coordinates of (a).
8. The method for collaborative path planning for an open-air unmanned platform in an unknown off-road scene according to claim 1, wherein in step 3, the method is characterized in that the method is based on an unmanned vehicle local risk map and a reference pathOptimizing the motion trail in real time and performing control tracking to obtain the final driving path +.>The specific method of (2) comprises the following steps:
according to the reference track provided by the unmanned aerial vehicle at the time tFrom the reference track->Extracting track control point set { Q } i -and using third-order B-spline curve for control point set { Q } i Optimizing, and the expression is:
wherein B is i， (t) is a base function of a Bezier curve, M is the number of control points extracted from the track;
for control point set { Q i Constructing a quadratic programming optimization problem:
wherein,represents the optimized control point set lambda s And lambda (lambda) d Balancing the weighting parameters of the optimization objective, f d For the smoothness cost function, the expression is:
wherein f s Local risk map by unmanned vehicle as risk cost functionInquiring to obtain;
calculated according to the optimization methodObtaining a driving track of the unmanned vehicle reaching a forward looking navigation point at the current momentAnd controlling the unmanned vehicle to track the track through a PID algorithm, and finally completing the collaborative path planning at the current moment.
CN202311214146.1A 2023-09-20 2023-09-20 Air-ground unmanned platform collaborative path planning method under unknown off-road scene Pending CN117289301A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117685994A (en) * 2024-02-04 2024-03-12 北京航空航天大学 Unmanned vehicle path planning method for air-ground coordination
CN117685994B (en) * 2024-02-04 2024-05-17 北京航空航天大学 Unmanned vehicle path planning method for air-ground coordination

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