A cooperative unmanned aerial vehicle cluster operation path planning method

Abstract

The application relates to a cooperative unmanned aerial vehicle cluster operation path planning method and relates to the unmanned aerial vehicle planning field. The application acquires starting positions and target positions of unmanned aerial vehicle clusters, constructs a minimum cuboid space covering the starting positions and the target positions, grids the minimum cuboid space into discrete space by using a cube; a static space cost topology graph representing reachability and movement cost between grid units in the discrete space is constructed, the static space cost topology graph contains nodes corresponding to the grid units and edges between the nodes, the edges are endowed with weights representing the movement cost; the static space cost topology graph is converted into a directed resource graph, the directed resource graph is configured with directed edges describing feasible movement processes of the unmanned aerial vehicles, the directed edges provide movement resources with a flow of 1 for the unmanned aerial vehicles; on the basis of the directed resource graph, a network maximum flow algorithm is used to find a collision-free path; the collision-free path is screened and post-processed considering the movement cost to obtain an executable path of the unmanned aerial vehicle.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) planning technology, and in particular to a method for planning the operational paths of a collaborative UAV swarm. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] With the rapid development of drone technology, drone swarms are increasingly being used in various fields such as logistics and delivery, agricultural plant protection, and aerial filming. Compared with single-drone operations, drone swarms can complete more complex and larger-scale tasks through collaborative cooperation, significantly improving operational efficiency and system robustness. However, as the swarm size increases and the mission environment becomes more complex, ensuring that multiple drones move efficiently and safely from their starting position to their target position within a shared space—that is, the multi-drone collision-free path planning problem—has become a core technical challenge that urgently needs to be solved in this field.

[0004] Existing UAV swarm path planning methods are mainly divided into two categories: centralized planning and distributed planning. Centralized planning treats the multi-drone problem as a whole and theoretically can obtain a globally optimal solution. However, as the number of UAVs increases, the state space explodes exponentially, resulting in extremely high computational complexity and making it difficult to meet real-time requirements. Distributed planning, while reducing the computational burden by allowing each UAV to make decisions based on local information, is prone to getting trapped in local optima and struggles to guarantee global collision-free safety, especially in high-density swarm scenarios where deadlocks or collisions are highly likely. Regarding specific path search algorithms, traditional graph search-based methods such as A* and Dijkstra's algorithm are widely used. However, these traditional methods have the following significant drawbacks when handling multi-drone collaboration: traditional methods typically separate spatial path planning from temporal scheduling or introduce a time dimension to construct a huge spatiotemporal search tree. This leads to a sharp increase in the search space dimension and low computational efficiency. Furthermore, time window-based methods have extremely high requirements for communication synchronization and clock precision. Most existing methods only determine collisions after path generation through simple geometric intersection detection, or avoid collisions by reserving time slices during the search process, often requiring subsequent complex spatiotemporal composite analysis to resolve collisions.

[0005] To address the aforementioned issues, a method is urgently needed to fundamentally resolve the spatiotemporal conflicts among multiple machines and achieve efficient parallel solution and optimization of multi-machine paths. Summary of the Invention

[0006] To solve the above-mentioned technical problems, or at least partially solve them, the present invention provides a method for planning the operation path of a collaborative unmanned aerial vehicle (UAV) swarm.

[0007] This invention provides a method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm, comprising: Obtain the starting and target positions of the drone swarm, construct a minimum cuboid space covering the starting and target positions, and then mesh the minimum cuboid space with cubes to form a discrete space. Construct a static spatial cost topology graph that represents the reachability and movement cost between grid cells in a discrete space. The static spatial cost topology graph includes nodes of corresponding grid cells and edges representing the reachability between nodes, with each edge assigned a weight representing the movement cost. The static spatial cost topology graph is converted into a directed resource graph. The directed resource graph is configured with directed edges that describe the feasible movement process of the UAV. The directed edges provide the UAV with movement resources with a throughput of 1. Based on the directed resource graph, the network maximum flow algorithm is used to find collision-free paths among multiple machines; Considering mobility costs, multi-drone collision-free paths are filtered and post-processed to obtain executable paths for drones.

[0008] Furthermore, constructing the minimum cuboid space covering the starting and target positions includes: during initialization, setting the minimum value to positive infinity and the maximum value to negative infinity to ensure that any actual coordinate can cover the initial value; traversing all starting and target position coordinates, updating the extreme value records of each dimension for each new point encountered, tracking the minimum and maximum values ​​in each dimension, and determining the minimum cuboid space.

[0009] Furthermore, each node of the static spatial cost topology graph corresponds to each grid cell in the discrete space. If two grid cells are adjacent and neither contains static obstacles, an undirected edge is created between the adjacent grid cells, and the weight of the undirected edge is configured according to the relative orientation and distance between the adjacent nodes with the undirected edge.

[0010] Furthermore, set corresponding horizontal adjacent cost coefficients and corresponding vertical cost coefficients, and use the weighted distance of the corresponding cost coefficients as the weight.

[0011] Furthermore, the step of converting the static spatial cost topology graph into a directed resource graph includes: For each node in the static spatial cost topology graph, create two corresponding vertices in the directed resource graph: a first vertex and a second vertex; set a directed edge between the first vertex and the second vertex, with the directed edge providing the drone with a mobility resource of 1; decompose any undirected edge between nodes in the static spatial cost topology graph into two directed edges in opposite directions, with the directed edges providing the drone with a mobility resource of 1, the two directed edges inheriting the weight of the original edge, one directed edge connecting the first vertex of one node to the second vertex of another node, and the other directed edge connecting the first vertex of another node to the second vertex of the first node; restrict the node corresponding to the starting position to allow one drone to depart, and restrict the node corresponding to the destination position to allow one drone to receive.

[0012] Furthermore, based on the directed resource graph, the network maximum flow algorithm is used to find collision-free paths among multiple machines, including: Configure augmenting start point and augmenting end point for the directed resource graph. The augmenting start point is pointed to the first vertex of all nodes corresponding to the start position by a directed edge that provides the drone with a mobility resource of 1. The second vertex of all nodes corresponding to the start position is pointed to the augmenting end point by a directed edge that provides the drone with a mobility resource of 1. Execution in a loop: Using a breadth-first search algorithm, find the shortest augmenting path from the augmenting start point to the augmenting end point based on the remaining resources. Along the augmenting path, find the minimum remaining resources of all directed edges as the bottleneck. Update the remaining resources based on the bottleneck: For directed edges in the same direction as the augmenting path, subtract the bottleneck of the augmenting path from the remaining resources; for directed edges in the opposite direction of the augmenting path, add the bottleneck of the augmenting path to the remaining resources. If the augmentation endpoint cannot be reached, the process ends. For all the shortest augmenting paths found, remove the augmenting start point and augmenting end point from the shortest augmenting paths to obtain the shortest path from the starting position to the target position.

[0013] Furthermore, considering the movement cost, the selection of the shortest path from the starting position to the target position includes: for cases where there are multiple candidate collision-free paths for the starting and target positions, obtaining the movement cost of the vertices and directed edges of the paths, calculating the total movement cost of the paths, and selecting the path with the minimum total movement cost.

[0014] Furthermore, path post-processing includes dynamic smoothing of the discrete coordinates of the path. Dynamic smoothing includes: selecting control points from the discrete coordinates of the path to form a smooth trajectory by minimizing the time integral of the square of the third-order derivative of the curve; performing B-spline interpolation on the control points to obtain the smooth trajectory; determining the curvature constraint threshold based on the minimum turning radius of the UAV; and correcting points in the smooth trajectory where the curvature violates the curvature constraint.

[0015] Furthermore, collision risk assessment is performed on the smoothed trajectory after dynamic smoothing, including: calculating the distance to the nearest obstacle during the drone's operation along the smoothed trajectory to obtain the distance field, and assessing the collision risk through the distance field.

[0016] Secondly, the present invention provides a working path planning device for a collaborative drone swarm, comprising: at least one processing unit, the processing unit being connected to a storage unit via a bus unit, the storage unit storing a computer program that can run on a processor, and the processing unit implementing the working path planning method for the collaborative drone swarm by running the computer program stored in the storage unit.

[0017] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed, implements the operation path planning method for the cooperative drone swarm.

[0018] The technical solutions provided in the embodiments of the present invention have the following advantages compared with the prior art: This invention de-meshs the space and constructs a spatial cost topology graph based on the reachability and movement costs between grids. Based on this topology graph, a directed resource graph is obtained. This graph is configured with directed edges describing feasible movement processes for UAVs, each providing a movement resource with a throughput of 1. Using this directed resource graph, a network maximum flow algorithm is employed to find collision-free paths for multiple UAVs. This invention eliminates the need for explicit collision checks based on UAV temporal relationships; instead, it directly eliminates the possibility of collisions by constraining the absence of shared movement resources in the space at any given time. This achieves collision avoidance without considering temporal relationships, reducing the computational load of UAV swarm path planning, and optimizing the total movement cost of the planned path as much as possible through movement costs. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart of a collaborative unmanned aerial vehicle (UAV) swarm operation path planning method provided in an embodiment of the present invention; Figure 2A flowchart for converting the static spatial cost topology graph into a directed resource graph is provided as an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the conversion of the static spatial cost topology graph into a directed resource graph, provided as an embodiment of the present invention. Figure 4 A schematic diagram of the planned path and obstacle space provided for an embodiment of the present invention; Figure 5 The projection diagram of the planned path in the XY plane provided in the embodiments of the present invention; Figure 6 This is a heatmap of safe distances in the XY plane for a scene formed by obstacles, provided in an embodiment of the present invention. Figure 7 This is a schematic diagram of a collaborative drone swarm operation path planning device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0024] Example 1 The present invention provides a method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm, such as... Figure 1 As shown, it includes: S100: Obtain the starting position and target position of the drone cluster, construct a minimum cuboid space covering the starting position and target position, and mesh the minimum cuboid space with cubes to form a discrete space.

[0025] The minimum rectangular space defines the scope for subsequent grid division and path planning.

[0026] Constructing the minimum cuboid space covering the starting and target positions includes: during initialization, setting the minimum value to positive infinity and the maximum value to negative infinity to ensure that any actual coordinate can cover the initial value; traversing all starting and target position coordinates, updating the extreme value records of each dimension for each new point encountered, tracking the minimum and maximum values ​​in each dimension, and determining the minimum cuboid space.

[0027] The process of meshing the minimum rectangular prism space into a discrete space using cubes includes: The side length of the cube unit is determined based on the positioning tolerance of the UAV, and the side length of the cube unit is set to 1-2 times the positioning tolerance.

[0028] The minimum cuboid space is divided equally according to the side length of the cube unit, and the minimum cuboid space is meshed to form a discrete space.

[0029] S200, construct a static spatial cost topology graph representing the reachability and movement costs between grid cells in discrete space.

[0030] Each node in the static spatial cost topology graph corresponds to a grid cell in the discrete space. If two grid cells are adjacent and neither contains static obstacles, an undirected edge is created between them. The undirected edge represents the reachability between nodes. The weight of the undirected edge is configured according to the relative orientation and distance between adjacent nodes with undirected edges. The weight represents the passage cost of the space between the nodes connected by the edge.

[0031] Specifically, each node in the static spatial cost topology graph is traversed. For a given current node, its neighboring nodes are searched to determine if there is an undirected edge. If not, the presence of an obstacle is checked. If there is no obstacle, an undirected edge is configured, and the weight of the undirected edge is configured based on the relative orientation and distance between the neighboring nodes with the undirected edge. The weight represents the passage cost of the space between the nodes connected by the edge. Specifically, the relative orientation between nodes can be either vertical or horizontal. Since the movement cost of a UAV is higher when flying vertically than horizontally, this application sets a horizontal cost coefficient for horizontal adjacency and a vertical cost coefficient for vertical adjacency, and uses the weighted distance of the corresponding cost coefficient as the weight.

[0032] S300, the static spatial cost topology graph is converted into a directed resource graph, the directed resource graph is configured with directed edges describing the feasible movement process of the UAV, and the directed edges provide the UAV throughput resources.

[0033] like Figure 2 As shown, the process of converting the static spatial cost topology graph into a directed resource graph includes: For each node in the static spatial cost topology graph, two corresponding vertices are created in the directed resource graph: a first vertex and a second vertex. The first vertex represents the gateway for drones to enter the node, and the second vertex represents the gateway for drones to leave the node. A directed edge is set between the first and second vertices, pointing from the first vertex to the second vertex, and the directed edge provides the drone with a movement resource of 1 throughput, ensuring that at most one drone is in the node at any given time. Figure 3 As shown, in the static spatial cost topology graph, there are four nodes ABCD. Node A corresponds to the first vertex A1 and the second vertex A2, node B corresponds to the first vertex B1 and the second vertex B2, node C corresponds to the first vertex C1 and the second vertex C2, and node D corresponds to the first vertex D1 and the second vertex D2. The undirected edges between any nodes in the static spatial cost topology graph are decomposed into two directed edges with opposite directions. Each directed edge provides the UAV with a mobility resource of 1. The two directed edges inherit the weights of the original undirected edges. One directed edge connects the first vertex of one node to the second vertex of another node, and the other directed edge connects the first vertex of another node to the second vertex of the first node. For example... Figure 3 As shown, the undirected edge between nodes A and B is divided into a directed edge from the second vertex A2 to the first vertex B1 and a directed edge from the second vertex B2 to the first vertex A1. All directed edges between nodes point from the portal of the outgoing node to the portal of the incoming node; that is, the first vertex of a node points to the second vertex of an adjacent reachable node, and the edges between any two vertices within a node are connected by the portal of the incoming node pointing to the portal of the outgoing node. This means that any drone leaving a node and entering an adjacent reachable node must first pass through the internal passage of the adjacent reachable node before it can continue flying from the adjacent reachable node to other nodes. The node corresponding to the starting position is restricted to allow a drone to depart, and the node corresponding to the destination position is restricted to allow a drone to be received.

[0034] By partitioning the grid with directed edges and configuring resources, it is ensured that the same grid cell cannot be occupied by two drones simultaneously (directed edges between vertices within a node provide drones with movement resources of 1 throughput). The same movement path cannot be used by two drones simultaneously (directed edges outside a node provide drones with movement resources of 1 throughput). The same starting or ending position can only be used by one drone. The drone state of each node can be independently controlled and measured, providing a foundation for collision-free planning.

[0035] S400, based on a directed resource graph, uses the network maximum flow algorithm to find collision-free paths among multiple machines; specifically, the process includes: During the construction of the directed resource graph, the adjacency relationship is recorded. The remaining resources of the initial directed edges are their movement resources. The flow of each directed edge is initially zero, that is, the total flow is zero. The path list is initialized. Configure augmenting start points and augmenting end points for the directed resource graph. The augmenting start point is pointed to the first vertex of all nodes corresponding to the start position by a directed edge that provides the drone with a mobility resource of 1. The second vertex of all nodes corresponding to the start position is pointed to the augmenting end point by a directed edge that provides the drone with a mobility resource of 1.

[0036] Execute in a loop: The breadth-first search algorithm is used to find the shortest augmenting path from the starting point to the ending point based on the remaining resources of directed edges. Each shortest augmenting path contains a path from the starting position to the target position. Specifically, for any vertex v and any adjacent reachable vertex u found by the breadth-first search, the directed edges between vertex v and u are obtained. It is checked whether the current remaining resources (the difference between the total movement resources and the current flow) are greater than zero. If they are greater than zero, the connection is considered to be connected, and the construction of the shortest augmenting path is allowed (vertices are recorded in the path list). This process is iterated until an augmenting path leading to the ending point is found.

[0037] Along the augmenting path, find the minimum value of the remaining resources of all directed edges as the bottleneck.

[0038] For directed edges related to augmenting paths, the remaining resources are updated based on the bottleneck. Specifically, for directed edges in the same direction as the augmenting path, the remaining resources are reduced by the augmenting path's bottleneck; for directed edges in the opposite direction, the remaining resources are increased by the augmenting path's bottleneck. This way, when searching for subsequent paths, if a better path can be found from the opposite directed edge...

[0039] If the augmentation endpoint cannot be reached, the process ends.

[0040] The bottleneck is used to update the total flow. Ultimately, since each node is limited to accommodating only one drone at a time, and directed edges allow only one drone to pass through, the collision-free multi-drone path obtained by the maximum flow algorithm actually provides the maximum total flow for a maximum number of drones to simultaneously fly from their respective starting positions to their respective target positions. Furthermore, since the collision-free multi-drone path does not share nodes (limiting movement resources to 1), no two drones occupy the same grid cell at any given time, thus collision avoidance can be achieved without considering temporal relationships.

[0041] For all the shortest augmenting paths found, the augmentation start and end points are removed from the shortest augmenting paths to obtain the shortest path from the starting position to the target position for each UAV, i.e., the multi-UAV collision-free path. For example... Figure 4 and Figure 5 As shown. Figure 4 The obstacle space described based on the XYZ axes is given, and the standardized size of the obstacle space is 30×30×8. Figure 4 and Figure 5The gray dots in the diagram represent obstacles, and the lines of different colors represent the planned trajectories of each drone in the drone swarm. Figure 5 for Figure 4 Projection in the XY plane. Figure 6 Scenes formed by obstacles Figure 4 Heat map of safe distances in the XY plane.

[0042] S500 considers movement costs to filter and post-process multi-drone collision-free paths to obtain executable paths for UAVs. In specific implementation, considering movement costs to select the shortest path from the starting position to the target position includes: when there are multiple candidate collision-free paths for the starting and target positions, obtaining the movement costs of the vertices and directed edges of the paths, calculating the total movement cost of the paths, and selecting the path with the minimum total movement cost.

[0043] Post-processing of the path includes dynamic smoothing of the discrete coordinates of the path. Dynamic smoothing involves: selecting control points from the discrete coordinates of the path to form a smooth trajectory by minimizing the time integral of the square of the third-order derivative of the curve; performing B-spline interpolation on the control points to obtain the smooth trajectory; determining the curvature constraint threshold based on the minimum turning radius of the UAV; and correcting points in the smooth trajectory where curvature violates the curvature constraint. Collision risk assessment is then performed on the smoothed trajectory after dynamic smoothing, including: calculating the distance to the nearest obstacle during the UAV's movement along the smooth trajectory to obtain the distance field; and evaluating the collision risk using the distance field.

[0044] Example 2 like Figure 7 As shown, this embodiment of the invention provides a working path planning device for a collaborative drone swarm, comprising: at least one processing unit, the processing unit being connected to a storage unit via a bus unit, the storage unit serving as a computer-readable storage medium, and capable of storing software programs, computer-executable programs, and modules, such as the software program, computer-executable program, and modules corresponding to the working path planning method for a collaborative drone swarm in this embodiment of the invention. The processing unit implements the aforementioned working path planning method for a collaborative drone swarm by running the software program, computer-executable program, and modules stored in the storage unit.

[0045] Of course, the computer program stored in the memory of the collaborative drone swarm operation path planning device provided in the embodiments of the present invention is not limited to the method operation described above, but can also execute related operations in the collaborative drone swarm operation path planning method provided in any embodiment of the present invention.

[0046] Example 3 This invention provides a computer-readable storage medium storing a computer program that, when executed, implements the collaborative drone swarm operation path planning method.

[0047] In the embodiments provided by this invention, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, structures, or units, and may be electrical, mechanical, or other forms.

[0048] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0049] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0050] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm, characterized in that, include: Obtain the starting and target positions of the drone swarm, construct a minimum cuboid space covering the starting and target positions, and then mesh the minimum cuboid space with cubes to form a discrete space. Construct a static spatial cost topology graph that represents the reachability and movement cost between grid cells in a discrete space. The static spatial cost topology graph includes nodes of corresponding grid cells and edges representing the reachability between nodes, with each edge assigned a weight representing the movement cost. The static spatial cost topology graph is converted into a directed resource graph. The directed resource graph is configured with directed edges that describe the feasible movement process of the UAV. The directed edges provide the UAV with movement resources with a throughput of 1. Based on the directed resource graph, the network maximum flow algorithm is used to find collision-free paths among multiple machines; Considering mobility costs, multi-drone collision-free paths are filtered and post-processed to obtain executable paths for drones.

2. The method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm according to claim 1, characterized in that, Constructing the minimum cuboid space covering the starting and target positions includes: during initialization, setting the minimum value to positive infinity and the maximum value to negative infinity to ensure that any actual coordinate can cover the initial value; traversing all starting and target position coordinates, updating the extreme value records of each dimension for each new point encountered, tracking the minimum and maximum values ​​in each dimension, and determining the minimum cuboid space.

3. The method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm according to claim 1, characterized in that, Each node in the static spatial cost topology graph corresponds to each grid cell in the discrete space. If two grid cells are adjacent and neither contains static obstacles, an undirected edge is created between the adjacent grid cells. The weight of the undirected edge is configured according to the relative orientation and distance between the adjacent nodes with the undirected edge.

4. The method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm according to claim 3, characterized in that, Set the corresponding horizontal adjacent cost coefficient and the corresponding vertical cost coefficient, and use the weighted distance of the corresponding cost coefficient as the weight.

5. The method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm according to claim 1, characterized in that, The step of converting the static spatial cost topology graph into a directed resource graph includes: For each node in the static spatial cost topology graph, create two corresponding vertices in the directed resource graph: a first vertex and a second vertex; set a directed edge between the first vertex and the second vertex, with the directed edge providing the drone with a mobility resource of 1; decompose any undirected edge between nodes in the static spatial cost topology graph into two directed edges in opposite directions, with the directed edges providing the drone with a mobility resource of 1, the two directed edges inheriting the weight of the original edge, one directed edge connecting the first vertex of one node to the second vertex of another node, and the other directed edge connecting the first vertex of another node to the second vertex of the first node; restrict the node corresponding to the starting position to allow one drone to depart, and restrict the node corresponding to the destination position to allow one drone to receive.

6. The method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm according to claim 1, characterized in that, Based on directed resource graphs, the network maximum flow algorithm is used to find collision-free paths among multiple machines, including: Configure augmenting start point and augmenting end point for the directed resource graph. The augmenting start point is pointed to the first vertex of all nodes corresponding to the start position by a directed edge that provides the drone with a mobility resource of 1. The second vertex of all nodes corresponding to the start position is pointed to the augmenting end point by a directed edge that provides the drone with a mobility resource of 1. Execution in a loop: Using a breadth-first search algorithm, find the shortest augmenting path from the augmenting start point to the augmenting end point based on the remaining resources. Along the augmenting path, find the minimum remaining resources of all directed edges as the bottleneck. Update the remaining resources based on the bottleneck: For directed edges in the same direction as the augmenting path, subtract the bottleneck of the augmenting path from the remaining resources; for directed edges in the opposite direction of the augmenting path, add the bottleneck of the augmenting path to the remaining resources. If the augmentation endpoint cannot be reached, the process ends. For all the shortest augmenting paths found, remove the augmenting start point and augmenting end point from the shortest augmenting paths to obtain the shortest path from the starting position to the target position.

7. The method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm according to claim 1, characterized in that, The process of selecting the shortest path from the starting position to the target position while considering movement costs includes: when there are multiple candidate collision-free paths for the starting and target positions, obtaining the movement costs of the vertices and directed edges of the vertices, calculating the total movement cost of the path, and selecting the path with the minimum total movement cost.

8. The method for planning the operational path of a collaborative unmanned aerial vehicle (UAV) swarm according to claim 1, characterized in that, Post-processing of the path includes dynamic smoothing of the discrete coordinates of the path. Dynamic smoothing includes: selecting control points from the discrete coordinates of the path to form a smooth trajectory by minimizing the time integral of the square of the third-order derivative of the curve; performing B-spline interpolation on the control points to obtain the smooth trajectory; determining the curvature constraint threshold based on the minimum turning radius of the UAV; and correcting points in the smooth trajectory where the curvature violates the curvature constraint.

9. The method for planning the operation path of a collaborative unmanned aerial vehicle (UAV) swarm according to claim 8, characterized in that, Collision risk assessment of a smoothed trajectory after dynamic smoothing includes: calculating the distance to the nearest obstacle during the drone's operation along the smoothed trajectory to obtain the distance field, and then assessing the collision risk using the distance field.

10. A collaborative unmanned aerial vehicle (UAV) swarm operation path planning device, comprising: At least one processing unit, the processing unit being connected to a storage unit via a bus unit, the storage unit storing a computer program that can run on a processor, characterized in that the processing unit implements the operation path planning method for a collaborative UAV swarm as described in any one of claims 1-9 by running the computer program stored in the storage unit.

Images