Multi-index dynamic priority unmanned aerial vehicle cooperative path planning method

Abstract

The application provides a multi-index dynamic priority unmanned aerial vehicle cooperative path planning method, and the path planning method comprises the following steps: acquiring robot parameter information, path constraint information, task environment information and algorithm parameter information; acquiring obstacle information in a simulation environment, and constructing a simulation environment grid map, so as to uniformly divide a map into grid maps with equal proportions; obtaining an unmanned aerial vehicle cooperative path planning configuration space according to the simulation environment grid map; and searching in the unmanned aerial vehicle cooperative path planning configuration space by using an unmanned aerial vehicle cooperative path planning algorithm based on a WM-MI-CS algorithm, so as to obtain a feasible unmanned aerial vehicle cooperative path. According to an example task requirement, the multi-index dynamic priority unmanned aerial vehicle cooperative path planning method has the advantages of low operation cost, short task completion time and fast algorithm speed.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more particularly to a collaborative path planning method for unmanned aerial vehicles (UAVs) with dynamic priority based on multiple indicators. Background Technology

[0002] With the rapid development of technology, robots are being applied to various fields. In recent years, unmanned aerial vehicle (UAV) swarms have played an increasingly important role in logistics delivery, power line inspection, and other fields. To ensure that multiple UAVs can efficiently and collaboratively complete tasks in complex environments, it is necessary to scientifically and rationally design the paths of the UAV swarm. To better balance the efficiency of collaborative planning and path quality, it is necessary to plan a feasible path that saves operating costs, increases task efficiency, and optimizes specific performance indicators.

[0003] Multi-UAV cooperative path planning aims to generate safe and feasible flight paths for multiple UAVs while considering cooperative constraints and achieving the task collaboratively at the lowest cost. Handling cooperative constraints is crucial in multi-UAV cooperative path planning, and it can be divided into two categories based on the constraint handling method: coupled planning and decoupled planning. Compared with coupled planning, decoupled planning, although it cannot guarantee the shortest path, has significant efficiency advantages. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a multi-index dynamic priority UAV cooperative path planning method (WM-MI-CS) to overcome or at least partially solve the above problems.

[0005] According to one aspect of the present invention, a collaborative path planning method for unmanned aerial vehicles (UAVs) with dynamic priority based on multiple indicators is provided, the path planning method comprising:

[0006] Acquire UAV parameter information, path constraint information, mission environment information, and algorithm parameter information;

[0007] Obtain obstacle information in the simulation environment and construct a grid map of the simulation environment, dividing the map into grids of equal size.

[0008] The configuration space for UAV cooperative path planning is obtained based on the simulated environment grid map;

[0009] A UAV cooperative path planning algorithm based on the WM-MI-CS algorithm is used to search in the UAV cooperative path planning configuration space to obtain feasible UAV cooperative paths.

[0010] Optionally, the drone parameter information includes the drone's dimensions, the path constraint information includes the drone's starting position and target position, and the mission environment information includes map size, number of obstacles, and obstacle locations.

[0011] The step of constructing UAV collaborative path planning based on the simulated environment grid map specifically includes:

[0012] The obstacle avoidance environment map of a single UAV is obtained by expanding the obstacle area in the simulated environment grid map.

[0013] Based on the Lazy Theta* algorithm, a congestion weight map is introduced to obtain obstacle avoidance environment maps for multiple drones;

[0014] The drone cooperative path planning configuration space is obtained based on the obstacle avoidance environment map of the single drone and the obstacle avoidance environment maps of the multiple drones.

[0015] The path planning method also includes:

[0016] Obtain the task indicators and constraints for drone collaborative path planning;

[0017] Establish the following multi-UAV collaborative planning indicators:

[0018] T = max T i (i=1,2,...,n) (1)

[0019] The overall task completion time is taken as the maximum task completion time in the UAV swarm, reflecting the task completion efficiency of the UAV system. In the formula, T... i Let be the task completion time of the i-th drone.

[0020] The constraints of collaborative path planning can be expressed as:

[0021] Power constraints:

[0022] p ie ≥e min (2)

[0023] p ie e represents the remaining battery power of the i-th drone. min The minimum battery level required to ensure a safe landing for the drone;

[0024] Obstacle avoidance constraints:

[0025] During drone flight, to ensure it can avoid obstacles in the environment, the distance D between the drone and the obstacle must meet the following requirements:

[0026]

[0027] In the formula, L is the wheelbase of the UAV, and s is the safe flight distance;

[0028] Inter-machine collision avoidance constraints:

[0029] To ensure that drones do not collide, collision constraints need to be considered. Therefore, the distance d between the center points of the drones must satisfy:

[0030] d>L+c (4)

[0031] In the formula, c is the safe distance between machines.

[0032] The UAV cooperative path planning algorithm based on the WM-MI-CS algorithm searches the UAV cooperative path planning configuration space to obtain feasible UAV cooperative paths, specifically including:

[0033] During node expansion, the arrival time at the current node is calculated, and the occupancy time of the region where the current node is located is queried. If the current time is within the occupancy time, then M is set equal to the congestion weight corresponding to the occupancy time; otherwise, M is set to 0. The node value evaluation function is expressed as:

[0034] f = g + w * h + M (5)

[0035] Where f is the cost of a node; g is the cumulative cost, representing the path length from the current point to the starting point; h is the estimated cost, representing the path length from the current point to the ending point; and w is the weight of the estimated cost value, where a larger w indicates that the node is more likely to be "closer to the ending point".

[0036] Considering the planning strategy for the coordination layer of a multi-machine system, the key steps specifically include:

[0037] Approximate calculation of the time corresponding to the path point: In the decoupled framework, assume the velocity is v = (v x ,v y Given a time step of T, the x and y coordinates of the i-th UAV at time k. It can be calculated by equation (6), and the set of x and y coordinates of the i-th UAV is obtained. i y i Stored separately in the flight x and y coordinate set Φ x =[x 1 ,x 2 ,...,x n ], Φ y =[y 1 ,y 2 ,...,y n ].

[0038]

[0039] In the formula, β is the directional angle from the previous moment to the current state.

[0040] Conflict detection and priority update: Conflict detection is performed by determining the position of other drones at the corresponding moment in the previous iteration for each drone. pos and Φ UAV Used to store the location of the conflict point and the number of the drone that clashed; calculates the priority of drones that collide at the same point and completes the update.

[0041] Conflict resolution: Instructs lower-priority drones to adopt appropriate obstacle avoidance strategies. Once a round of conflict resolution is completed, the system will proceed to the next iteration until a flight path that satisfies all cooperative path planning constraints is obtained.

[0042] The specific implementation of the above steps includes:

[0043] Considering collision risk, total distance S, and waiting time T w , distance ratio S r A dynamic priority calculation model is constructed. After the above indicators are standardized by min-max using equation (7), the priority P of the i-th UAV is calculated using equation (8). i :

[0044]

[0045]

[0046] In the formula, S c The remaining path when a conflict occurs, where α, β, and γ are the influence weights of the corresponding indicators;

[0047] When the collision risk is the same, the influence weight of each indicator is determined by the Analytic Hierarchy Process (AHP).

[0048] To determine whether there is a logical contradiction in the pairwise comparison discriminant matrix, a consistency check is required using equation (9):

[0049]

[0050] In the formula, the average random consistency index RI can be given according to the order of the comparison matrix. When the random consistency ratio CR < 0.1, the calculated weights are considered to be correct and reasonable.

[0051] Considering local inter-drone collisions, path replanning offers strong coordination capabilities but has a relatively long planning time; while starting point waiting offers shorter planning time but extends UAV flight time. Therefore, this invention adopts a combined strategy of "path replanning + starting point waiting" to avoid inter-drone conflicts. A strategy selection formula is established based on the conflict situation, which can be expressed as:

[0052] θ≥180°-ε(0°≤ε≤180°) (10)

[0053] In the formula, θ is the angle between conflict vectors. When θ ≥ 180° - ε, a path replanning strategy is executed; otherwise, a starting point waiting strategy is executed. ε is an allowable value that can be given manually to balance coordination capability and solution speed.

[0054] This invention provides a multi-index dynamic priority method for UAV cooperative path planning. The path planning method includes: acquiring UAV parameter information, path constraint information, mission environment information, and algorithm parameter information; acquiring obstacle information in a simulation environment and constructing a simulation environment grid map, dividing the map into grids of equal size; obtaining a UAV cooperative path planning configuration space based on the simulation environment grid map; and using a UAV cooperative path planning algorithm based on the WM-MI-CS algorithm to search in the UAV cooperative path planning configuration space to obtain feasible UAV cooperative paths.

[0055] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart of a UAV cooperative path planning system is provided for an embodiment of the present invention.

[0058] Figure 2 This is a hierarchical model diagram provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of hedging conflict provided in an embodiment of the present invention;

[0060] Figure 4This is a schematic diagram of the starting point conflict provided in an embodiment of the present invention;

[0061] Figure 5 The flowchart of the WM-LazyTheta* algorithm provided in the embodiments of the present invention is shown below;

[0062] Figure 6 This is a congestion weight map provided in an embodiment of the present invention at t=10s;

[0063] Figure 7 This is a schematic diagram of the expanded nodes after introducing a congestion weight map, as provided in an embodiment of the present invention.

[0064] Figure 8 These are diagrams showing the collaborative path planning results for three cluster sizes provided in this embodiment of the invention.

[0065] Figure 9 These are curves showing the variation of the minimum distance between machines under three different scales provided in this embodiment of the invention.

[0066] Figure 10 This is a diagram showing the remaining battery power for completing tasks under three cluster sizes, as provided in this embodiment of the invention.

[0067] Figure 11 This is a comparison chart of task completion time and solution time under different cluster sizes provided in the embodiments of the present invention;

[0068] Figure 12 This is a comparison chart of the flight time windows of fifteen drones provided in an embodiment of the present invention;

[0069] Figure 13 The task completion time and solution time of the five UAVs provided in the embodiments of the present invention;

[0070] Figure 14 The task completion time and solution time for 10 UAVs provided in this embodiment of the invention;

[0071] Figure 15 The task completion time and solution time for 15 UAVs provided in this embodiment of the invention;

[0072] Figure 16 This is a diagram showing the flight time window results based on Lazy Theta* provided in an embodiment of the present invention;

[0073] Figure 17 This is a diagram showing the flight time window results based on WM-Lazy Theta* provided in an embodiment of the present invention; Detailed Implementation

[0074] Exemplary embodiments of the present invention will now be described in more detail 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 implemented 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.

[0075] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0076] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0077] like Figure 1 As shown, the present invention discloses an overall process for collaborative path planning. For the problem of collaborative path planning of multiple UAVs, the collaborative path planning is divided into a single-UAV path planning layer and a multi-UAV system coordination layer. The single-UAV path planning layer plans a global path for each UAV that does not collide with obstacles in the environment, and then the multi-UAV system coordination layer completes the collaborative movement between UAVs.

[0078] This invention discloses a multi-index dynamic priority method for UAV cooperative path planning, comprising the following steps:

[0079] Step 1: Obtain UAV parameter information, path constraint information, task environment information, and algorithm parameter information. UAV parameter information includes the robot's dimensions. Path constraint information includes the starting and target positions of multiple UAVs. Task environment information includes map size, number of obstacles, and their locations.

[0080] Step 2: Based on obstacle information in the simulation environment, construct a simulation environment grid map, in which the map is evenly divided into grids of equal size. Dilate the obstacle area in the simulation environment grid map to obtain an environment map considering obstacle avoidance for a single UAV. Introduce a congestion weight map based on the Lazy Theta* algorithm to obtain obstacle avoidance environment maps for multiple UAVs, thus obtaining the configuration space for UAV cooperative path planning.

[0081] Step 3: Construct the task indicators and constraints that need to be considered in collaborative path planning.

[0082] To address the problem of collaborative path planning for unmanned aerial vehicles (UAVs), and in order to reduce operating costs and increase mission efficiency in obstacle-prone environments, the following collaborative planning indicators are established, taking into account factors such as collision risk, total distance traveled, and waiting time, with the overall mission completion time of multiple UAVs as the evaluation criterion:

[0083] T = max Ti (i=1,2,...,n) (1)

[0084] In the formula, T i Let be the task completion time of the i-th drone.

[0085] The following constraints are imposed on the problem of cooperative path planning for unmanned aerial vehicles (UAVs):

[0086] Power constraints:

[0087] p ie ≥e min (2)

[0088] p ie e represents the remaining battery power of the i-th drone. min The minimum battery level required to ensure a safe landing for the drone;

[0089] Obstacle avoidance constraint: During the flight of a drone, to ensure that it can avoid obstacles in the environment, the distance D between the drone and the obstacle must meet the following requirements:

[0090]

[0091] In the formula, L is the wheelbase of the UAV, and s is the safe flight distance;

[0092] Inter-drone collision avoidance constraints: To ensure that no collisions occur between drones, inter-drone collision constraints need to be considered. Therefore, the distance d between the center points of the drones must satisfy:

[0093] d>L+c (4)

[0094] In the formula, c is the safe distance between machines.

[0095] Step 4: For individual drone path planning, a congestion weight map is introduced based on the Lazy Theta* algorithm to guide drones to avoid congested areas and reduce the possibility of conflict.

[0096] Step 5: Based on the positions of the UAVs and target points, as well as the obstacle map of the simulation environment, use the approximate calculations of the corresponding times of the above path points to determine whether there are path conflicts between multiple UAVs.

[0097] Step Six: Utilize the proposed multi-index dynamic priority update strategy, considering collision risk, total distance S, and waiting time T. w , distance ratio S r A dynamic priority calculation model is constructed. Specifically, when collision risks are equal, the influence weights of each indicator are determined using the Analytic Hierarchy Process (AHP). The constructed hierarchical model consists of... Figure 2 As shown.

[0098] Step 7: To address the issue of inter-machine collision avoidance, the proposed combined strategy of "path replanning + starting point waiting" is used to avoid inter-machine conflicts. The specific process is as follows:

[0099] Step 1): Traverse the set of conflicting drones. r_flag is the replanning flag. When θ≥180°-ε, r_flag is True, i.e., the replanning strategy is executed. Otherwise, the starting point waiting strategy is executed.

[0100] Step 2): Let the first of the consecutive conflict points be the position to be avoided. Treat it as an obstacle and perform path planning to generate a new path new_path. If a new path cannot be generated, i.e. new_path is empty, then execute the starting point waiting strategy to avoid the conflict.

[0101] Step 3): To avoid a drone repeatedly executing the starting point waiting strategy in the same iteration round, Exec_wait records the drones that have executed the starting point waiting strategy in a single iteration round. Determine whether the current drone has executed the starting point waiting strategy in this iteration round. If not, wait at the starting point for one time step T.

[0102] Step 4): After processing all conflicts in this iteration, determine whether there are still conflicts in the system. If there are no conflicts in the system, exit the loop; otherwise, continue to the next iteration.

[0103] Example 1:

[0104] The specific implementation steps of the UAV cooperative path planning method with multi-index dynamic priority provided in this embodiment are as follows:

[0105] Step 1: Input UAV parameter information, path constraint information, mission environment information, and algorithm parameter information.

[0106] The robot size is set to 0.4m*0.4m, the number of drones is divided into three cases: 5, 10, and 15, the initial speed is 0.2m / s, the number of target points corresponds to the number of drones in the three cases, the map size is 20m*12m, and there are 8 obstacles.

[0107] Step Two: Based on the parameter inputs of the specific example above, create an environmental map for this UAV cooperative path planning problem. For example... Figure 8 As shown in the map, the environment map is 20m*12m in size, with a resolution of 0.4m, and the grid size is 0.5m*0.3m, containing 8 obstacles.

[0108] like Figure 8As shown, an expansion is performed on the obstacles in the simulation environment map to obtain a new environment map. The black area represents the obstacle area in the real environment, the dark gray area represents the hard obstacle area obtained by expanding according to the size of a single drone (0.4m), and a single drone can pass through this area. The white area is recorded as the passable area.

[0109] To ensure the safe operation of drones, obstacle detection is required during path planning to ensure that the planned path does not pass through impassable areas.

[0110] Step 3: Construct the constraints and task indicators that need to be considered in the collaborative path planning of UAVs.

[0111] T = maxT i (i = 1, 2, ..., n)

[0112] p ie ≥e min

[0113]

[0114] d>L+c

[0115] In this embodiment, e is taken as... min =5, L=0.4, s=c=0.5.

[0116] Step 4: Using the WM-MI-CS method proposed in this invention, search the constructed configuration space to obtain feasible cooperative paths. The WM-MI-CS method flow is as follows:

[0117] Step 1): Based on the positions of the UAVs and target points, and the obstacle map of the simulation environment, use the approximate calculations of the corresponding times of the above path points to determine whether there are path conflicts between multiple UAVs.

[0118] Step 2): Utilize the proposed multi-index dynamic priority update strategy, considering collision risk, total distance S, and waiting time T. w , distance ratio S r A dynamic priority calculation model was constructed. In particular, when the collision risks are the same, the influence weights of each indicator were determined using the Analytic Hierarchy Process (AHP).

[0119] Step 3): To address the problem of avoiding collisions between machines, the proposed combination strategy of "path replanning + starting point waiting" is used to avoid conflicts between machines.

[0120] Step 4): For individual drone path planning, a congestion weight map is introduced on the basis of the Lazy Theta* algorithm to guide drones to avoid congested areas and reduce the possibility of conflict.

[0121] For the specific example above, the path result obtained using the UAV cooperative path planning method with multi-index dynamic priority described in this embodiment is as follows: Figure 8 The three result figures show the minimum distance between machines at different scales, as follows: Figure 9 As shown, and the remaining power after completing the task at different scales, as shown in the figure. Figure 10 As shown.

[0122] The task completion time and solution time obtained by the UAV cooperative path planning method with multi-index dynamic priority are compared with other methods, for example... Figure 11 As shown. Although other methods can obtain path results that can avoid obstacles and conflicts to reach the target location, the solution time and task completion time change significantly with the increase in the scale of UAVs. The path planning method proposed in this patent not only improves the coordination ability of multi-UAV systems, but also greatly improves the solution speed.

[0123] As can be seen from the aforementioned simulation results and analysis of UAV cooperative path generation, the UAV cooperative path planning method with multi-index dynamic priority described in this embodiment can provide feasible paths for multiple UAVs that meet the complex constraints of reality. Compared with other methods, the planning results have the advantages of reduced operating costs, shorter task completion time, and faster algorithm calculation speed. Therefore, this invention has engineering applicability and can achieve the expected invention purpose.

[0124] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-index dynamic priority method for UAV cooperative path planning, characterized in that, include: Step 1: Obtain UAV parameter information, path constraint information, mission environment information, and algorithm parameter information. UAV parameter information includes the size of the UAV, path constraint information includes the starting position and target position of multiple UAVs, and mission environment information includes map size, number of obstacles, and their locations. Step 2: Create a simulation environment grid map: Obtain obstacle information in the simulation environment and construct a simulation environment grid map, dividing the map into grids of equal size; expand the obstacle area in the simulation environment grid map to a preset width in all directions to obtain a single-drone obstacle avoidance environment map that takes into account obstacle avoidance of a single drone. Step 3: Establish drone collaborative planning indicators: In the formula, T i For the first i Mission completion time for each drone. n Number of drones; p ie Indicates the first i The remaining battery power of the drone, e min The minimum battery level required to ensure a safe landing for the drone; D The distance between the drone and the obstacle. L For the wheelbase of the drone, s To maintain a safe distance for flight; c To maintain a safe distance between machines, d This is the distance between the midpoints of the diagonal arms of the quadcopter drone; Step 4: Single-machine path planning, which allows for planning paths at any angle while introducing traffic flow. The congestion weight map, update method, and node cost are as follows: Congestion weight map update: Divide the raster map into m Each congestion detection zone forms a congestion weight map to obtain an obstacle avoidance environment map for multiple drones; each grid cell is a minimum congestion detection zone. Set the scaling factor The area of ​​the congestion detection zone is , W and H respectively grid The width and height of the image; each congestion detection region contains a number of grid cells. ; Each congestion detection zone is assigned a consecutive integer starting from 0 as its number; Node cost calculation: Calculate the arrival time at the current node, query the occupied time of the region where the current node is located, and if the current time is within the occupied time, then let... M It equals the congestion weight corresponding to the occupancy time; otherwise, let M =0; the evaluation function for node value is expressed as: in, f The cost of nodes; g For cumulative value; h To estimate the cost; w Weights for estimating cost values; Step 5: Multi-machine collaborative path planning: Step 5-1: Approximate calculation of the time corresponding to the path point No. i The set of x and y coordinates of a drone x i , y i Stored separately in the flight x and y coordinate set Assuming the speed is v = ( v x , v y ), with a time step of T , No. i A drone in k x and y coordinates of time , The calculation method is as follows: In the formula, θ The angle between the directions from the previous moment to the current state; Step 5-2: Path Conflict Detection: Based on the positions of the UAVs and target points, and the obstacle map of the simulation environment, path conflicts between multiple UAVs are determined using the corresponding time points of the path points. Conflict detection is performed by checking the positions of other UAVs at the corresponding time points during the previous iteration. pos and Φ UAV Used to store the location of the conflict point and the number of the drone involved in the conflict; calculates the priority of drones that have inter-drone collisions at the same point, and completes the update. The multi-indicator dynamic priority update strategy is divided into the following two parts: 1) When collision risks differ, aircraft with more collisions are assigned lower priority and implement avoidance strategies. 2) When the collision risk is the same, consider the collision risk and the total distance. S Waiting time T w distance ratio S r Construct a priority calculation model that integrates multiple indicators for the comprehensive indicators of UAVs. P i The calculation method is as follows: In the formula, α,β, γ To account for the impact of the corresponding indicators, , and The normalized values ​​for each indicator are calculated using the following formula: In the formula, S r The ratio of remaining distances, i.e., the remaining distances when a conflict occurs. S c Total distance S The ratio, its expression is: Sr （ 11） High flight rights are allocated to drones with long total distances, long waiting times, and large remaining distances. Step 6: Local inter-machine collision avoidance: Step 6-1: Traverse the set of conflicting drones. r_flag To replan the signage location, when θ ≥ 180° - ε hour, r_flag If True, the replanning strategy is executed; otherwise, the starting point wait strategy is executed. Step 6-2: Let the first of the consecutive conflict points be the location to be avoided. Treat it as an obstacle and perform path planning to generate a new path. new_path If a new path cannot be generated, i.e. new_path If empty, the starting point wait strategy is executed to avoid conflict; Step 6-3: Exec_wait Record the drones that have executed the starting point waiting strategy in a single iteration; determine whether the current drone has executed the starting point waiting strategy in this iteration. If not, wait at the starting point for one time step. T ; Step 6-4: Determine if there are still conflicts in the system. If there are no conflicts, exit the loop; otherwise, continue to have the drones with lower priority adopt the corresponding obstacle avoidance strategies. Step 7: Determine whether a flight path that satisfies all cooperative path planning constraints has been obtained. If yes, end; otherwise, proceed to step 5.

2. The UAV cooperative path planning method with multi-index dynamic priority as described in claim 1, characterized in that, Step 5 also includes a step of dynamically prioritizing and updating the strategy using multiple indicators, considering collision risk, total distance S, waiting time Tw, and distance ratio Sr; when the collision risk is the same, the influence weight of each indicator is determined as follows: (1) The constructed hierarchical model; (2) Construct the comparison discrimination matrix A; (3) Weight calculation: (4) Consistency check To determine whether there are logical contradictions in the pairwise comparison discriminant matrices, a consistency check is performed: In the formula, the average random consistency index RI is given according to the order of the comparison matrix. When the random consistency ratio CR < 0.1, the calculated weights are considered to be correct and reasonable.

3. The UAV cooperative path planning method with multi-index dynamic priority as described in claim 1, characterized in that, Conflict avoidance strategies are as follows: Considering local inter-machine collisions, a strategy selection formula is established based on the conflict situation: θ ≥180° - ε (0° ≤ ε ≤180°)（ 14） In the formula, θ Let the angle between the conflict vectors be the angle between them. θ ≥180° - ε If the path replanning strategy is executed, then the starting point waiting strategy is executed. ε This is an allowable value that can be manually assigned to balance coordination capability and solution speed; When the noses of two drones are facing each other, the collision planning strategy is selected.

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