A method and system for full-process path planning of multi-UAV high-voltage line inspection

Abstract

This invention discloses a method and system for path planning throughout the entire process of multi-UAV high-voltage line inspection, comprising the following steps: constructing a three-dimensional environment model including obstacles such as poles, trees, and terrain; within the three-dimensional environment model, determining the first and second paths of the UAVs based on the Hybrid A* algorithm; calculating the lengths of the two paths and determining whether they meet the UAV's cruising distance requirements; determining the coverage feasibility of the line to be inspected based on the UAV's inspection radius and the span of the high-voltage line; planning the third, fourth, and fifth paths, and combining the three path segments to obtain the complete inspection path for each UAV. According to this invention, by adapting the linear layout of high-voltage lines and the motion constraints of UAVs using the Hybrid A* algorithm, multi-UAV collaborative conflict-free inspection is achieved, improving inspection efficiency and flight safety, reducing labor costs, and is applicable to various fully automated inspection scenarios for high-voltage transmission lines.

Description

Technical Field

[0001] This invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a method for path planning throughout the entire process of multi-UAV high-voltage line inspection. Background Technology

[0002] Due to the expansion of my country's power system, high-voltage lines are mostly distributed in complex terrain, making manual inspections difficult and leading to the adoption of drones as the mainstream inspection method.

[0003] Path planning is the core of autonomous UAV inspection. Existing research mostly focuses on single UAVs, but it is difficult to meet the needs of long-distance and large-scale inspection. Multi-UAV path planning technology is adapted to general scenarios but does not match the special needs of high-voltage lines. Traditional algorithms and collaborative methods have defects and cannot meet the high safety and high reliability requirements of high-voltage inspection.

[0004] Therefore, there is an urgent need to propose a multi-UAV full-process path planning technology adapted to high-voltage line scenarios, optimize collaborative strategies and algorithms, achieve efficient and safe inspection, and promote the intelligent upgrade of power inspection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method for full-process path planning for multi-UAV high-voltage line inspection. To achieve the above-mentioned objective and other advantages of the present invention, a method for full-process path planning for multi-UAV high-voltage line inspection is provided, comprising: Construct a 3D environment model including towers, trees, and terrain; within the 3D environment model, determine the first and second paths of the UAV using an improved Hybrid A* algorithm; calculate the lengths of the first and second paths respectively, and determine the takeoff point of the UAV, specifically including: Generate random numbers η ϵ [0,1] , ϵ [0,1] , ϵ [0,1] , ϵ [0,1] ; Indicates the first random number at the horizontal level. This represents the second random number at the horizontal level. This represents a vertical random number. The function used to generate the drone's takeoff point is: ; ; Where (x, y, z) represents the takeoff point of a drone, and (a, b, c) represents the center coordinates of the drone's takeoff range. The radius representing the horizontal takeoff range of the drone. This represents the vertical takeoff range height, and P represents the set of forbidden points. A third path is determined, which is a fine-grained path for each UAV from its takeoff point to the inspection start point of the high-voltage line to be inspected; a fourth path is determined, which is a fine-grained path for each UAV from the inspection start point of the high-voltage line to the inspection end; a fifth path is determined, which is the return path for each UAV from the midpoint of the inspection end to the start point; the third, fourth, and fifth paths of each UAV are combined to obtain the complete path of each UAV.

[0006] Preferably, the first path refers to a rough path from the drone's starting point to the center point of the starting end of the high-voltage line to be inspected; the second path refers to a rough path from the center point of the starting end of the high-voltage line to be inspected to the center point of the ending end of the high-voltage line to be inspected; the three-dimensional environment model is obtained by three-dimensional modeling of static obstacles in three regions, and the three-dimensional coordinates of the static obstacles in the three regions are used as a set of prohibited points for static obstacles; the three regions refer to the region from the drone's takeoff point to the center point of the starting end of the high-voltage line to be inspected, the region from the center point of the starting end of the high-voltage line to be inspected to the center point of the ending end of the high-voltage line to be inspected, and the region from the center point of the ending end of the high-voltage line to the drone's starting point; the distance from the ending end of the high-voltage line to be inspected to the starting point is greater than the distance from the starting end of the high-voltage line to the starting point.

[0007] Preferably, the core cost function of the improved Hybrid A* algorithm ; in, It is the actual flight cost from the starting point to the current node n. ; in, Distance weights This is the actual flight distance. As energy consumption weight, For flight energy consumption; Let n be the heuristic cost from the current node n to the target node; ; in, The three-dimensional coordinates of the current node. The three-dimensional coordinates of the target point; The cost of safety constraints; ; in, For safety distance weighting, To minimize the safe distance between conductors, Let n be the distance from node n to the wire. As the turning radius weight, Minimum turning radius, Let n be the turning radius at node n. To take non-negative values; , , This is the weighting coefficient, with a value range of [0.9, 1.3]. Preferably, based on the first path length and the second path length, it is determined whether the total path length of the inspection process meets the drone's cruising distance requirements, specifically including: The lengths of the first path L1 and the second path L2 are calculated using Dubins curves. The formula for calculating the length of a Dubins curve is as follows: ; Where r is the minimum turning radius of the drone. Let d be the turning angle and d be the length of the straight segment; + +2 Where L redundancy is the emergency continuation redundancy distance, with a value of 500~1000, which is the same as the drone's maximum cruising distance. In comparison.

[0008] Preferably, the third path is a fine-grained path for each UAV from its takeoff point to the starting point of the inspection of the high-voltage line to be inspected, specifically including: Step (1-1): Calculate fitness. The top three fitness values, from smallest to largest, are as follows: , , The fitness function can be represented as follows: ; in, This represents the fitness of the i-th drone. This represents the distance between the current position of the i-th drone and the inspection starting point. This represents the number of static obstacles between the current position of the i-th UAV and the inspection starting point. This represents the dynamic obstacle risk value between the current position of the i-th UAV and the line connecting the inspection starting point. This represents the sum of static obstacles along the line connecting all drones to the starting position of the inspection. This represents the sum of dynamic obstacle risk values ​​along the line connecting all drones to the inspection start position, where k is the dynamic risk weight, with a value range of [1.2, 1.5]. Step (1-2): Update the positions of the remaining points: ; ; ; ; in, As the number of iterations decreases from 2 to 0, and Let be a random vector on [0, 1]. This represents the updated coordinates of the remaining points. This represents the search range correction factor. This represents the search divergence correction coefficient; Steps (1-3): Perform a local search based on the Gaussian distribution: ; in, for Random numbers within a range and As a dynamic factor, Represents a unit vector. This represents the coordinate vector at time t. Let be the coordinate vector after the local search at time t+1; Steps (1-4): Update , , The location is determined, and the third path is output after iterating to the maximum number of iterations.

[0009] Preferably, a fourth path is determined, which is a fine-grained path for each UAV from the inspection start point at the beginning of the high-voltage line to the inspection end, specifically including: Step (2-1): Based on the drone inspection radius and the spacing between high-voltage line conductors, divide the line to be inspected into several drone inspection areas in a direction parallel to the direction of the high-voltage line. Step (2-2): Establish a total set of forbidden points for each region. The total set of forbidden points includes: a set of forbidden points for static obstacles and a set of forbidden points for dynamic obstacles. Step (2-3): Determine if there is a vertical section in each inspection area that the drone cannot pass through. If not, proceed to step (2-4); otherwise, proceed to step (2-5). Steps (2-4): Establish a 3D grid map within each inspection area. Using the starting point of the area to be inspected as the starting point and the ending point as the ending point, plan an inspection path combining "straight line + circular arc" based on the improved Hybrid A* algorithm. Plan a surrounding trajectory around the tower, with a surrounding radius of... ; Steps (2-5): Integrate the inspection areas with impassable sections with adjacent areas, adopt multi-machine collaborative path planning, and replan the path based on the improved Hybrid A* algorithm.

[0010] Preferably, the method further includes a multi-machine cooperative obstacle avoidance step: Step (3-1): Calculate the radius of the drone's safety sphere ; in, The radius of the drone's fuselage. Minimum safe distance between drones; Step (3-2): Calculate the time when the conflict drone arrives at the conflict point. , ; in, , This represents the path length from the drone to the point of conflict. , For flight speed; Step (3-3): Perform local replanning based on the improved Hybrid A* algorithm, and add a conflict penalty term to the cost function of the new path. ; in, As a penalty weight, Given the distance from node n to the conflict point, generate a conflict-free path.

[0011] Preferably, the method further includes a dynamic obstacle handling step: The drone uses visual sensors and lidar to detect dynamic obstacles and calculate their speed and direction of movement. ;

[0012] The predicted intersection time between the dynamic obstacle and the current planned path is: ; in, The initial distance, The angle of relative motion. A multi-UAV high-voltage line inspection full-process path planning system includes: a map building module, a judgment module, a determination module, a planning module, a multi-UAV collaboration module, and a dynamic update module. The map construction module is used to build a 3D environment model including towers, trees, and terrain. Within the 3D environment model, the first and second paths of the UAV are determined according to the improved Hybrid A* algorithm. The core cost function of the improved Hybrid A* algorithm is... ; The judgment module is used to calculate the path length using the Dubins curve length calculation formula based on the first path length and the second path length, and to determine whether the path length of the entire inspection process meets the drone's cruising distance requirements; if it does, it obtains the number of drones input by the user; if it does not, it provides a prompt. The determination module is used to determine whether the high-voltage line to be inspected can be covered for inspection based on the drone's inspection radius and the span and tower spacing of the line to be inspected. If it cannot be covered for inspection, the number of drones input by the user is retrieved again. If it can be covered for inspection, the takeoff point calculation function described in the invention is used to determine the takeoff point of the drone. The planning module is used to determine the third, fourth, and fifth paths, and combines the third, fourth, and fifth paths of each UAV to obtain the complete path for each UAV. The multi-drone collaboration module is used to coordinate the flight trajectories of multiple UAVs to avoid collisions by employing conflict judgment formulas and replanning methods. The dynamic update module is used to detect dynamic obstacles and trigger path replanning by employing dynamic obstacle speed and direction calculation methods.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: This system comprehensively considers path planning tasks at different stages of the drone inspection process, providing a complete path from departure to inspection and return. It also uses cameras to detect dynamic obstacles during flight, meeting multiple requirements such as inspection coverage, fuel constraints, and obstacle avoidance. Furthermore, real-time detection of dynamic obstacles avoids risks caused by sudden changes in obstacle movement during mission execution, providing a more comprehensive and reasonable path planning solution for multi-drone systems. Attached Figure Description

[0014] Figure 1 A flowchart of the multi-UAV high-voltage line inspection full-process path planning method and system according to the present invention; Figure 2 The present invention provides a system diagram of a multi-UAV high-voltage line inspection full-process path planning method and system. Detailed Implementation

[0015] 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, and 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.

[0016] Reference Figure 1 A method for path planning throughout the entire process of multi-UAV high-voltage line inspection, comprising: S101: Construct a one-dimensional environment model: Within the one-dimensional environment model, based on the improved Hybrid A* algorithm, determine the first and second paths of the UAV respectively.

[0017] Based on the first and second paths, calculate the length of the first path and the length of the second path, and determine whether the total path length of the inspection process meets the cruise distance requirement of this drone. If it does, it means that it meets the cruise distance requirement of this drone; otherwise, it means that it does not meet the cruise distance requirement of this drone, and a prompt is given.

[0018] S103: Obtain the number of drones input by the user.

[0019] S104: Determine whether the high-voltage line to be inspected can be covered for inspection based on the drone's inspection radius and the span and tower spacing of the high-voltage line to be inspected. If the drone cannot be covered during inspection, the number of drones entered by the user is retrieved again. If the drone can be covered during inspection, the takeoff point of the drone is determined.

[0020] S105: Determine the third path, which is the fine path between the take-off point of each UAV and the inspection starting point of the high-voltage line to be inspected. Determine the fourth path, which is the fine path for each drone from the starting point of the inspection of the high-voltage line to the ending point of the high-voltage line to be inspected: The fifth path is determined, which is the return path of each drone from the midpoint of the end of the high-voltage line to be inspected to the starting point. The third, fourth and fifth paths of each drone are combined to obtain the complete path of each drone. S106: Determine whether there is a path conflict between the complete paths of the drones. If not, output the path directly. If so, make minor adjustments to the path.

[0021] S107: Detect dynamic obstacles using the highway gantry camera, determine if path replanning is needed, and if so, return to the third path determination step.

[0022] S101: Within the three-dimensional environment model, based on the improved adaptive extended neighborhood Hybrid A* algorithm; Specifically, this includes S1011: Determine the traversal range of the node based on the coordinates of the starting point and the center point of the starting end of the road segment to be inspected; S1012: Calculate the cost of each target node within the traversal range of the current node; S1013: Sort the costs of the target nodes in descending order to determine the next target node; S1014: If the next target is defined as the coordinates of the center point of the starting end of the road segment to be inspected, then the process ends and the first path is obtained.

[0023] The steps for determining the second path are the same as those for the first path. The difference is that the starting point coordinates of the second path in S1011 are replaced with the center point coordinates of the starting end of the road segment to be inspected, and the center point coordinates of the starting end of the road segment to be inspected are replaced with the center point coordinates of the ending end of the road segment to be inspected.

[0024] For example, determining the traversal range of nodes based on the grid coordinates of the starting point and the center point coordinates of the starting end of the road segment to be inspected means selecting only nodes whose horizontal and vertical coordinates are within the range of the horizontal and vertical coordinates of the starting point and the center point of the starting end of the road segment to be inspected. For example, the coordinates of the starting node are ( The coordinates of the center point of the starting end of the road section to be inspected are ( If the node's traversal range is within the range of [...], then the traversal range of the node is within the range of [...]. Within the range of ], the ordinate is in [ Nodes within the range.

[0025] S1012: Calculate the cost of each target node within the traversal range of the current node. S1013: Sort the cost of the target points in descending order and determine the expansion nodes. Specifically, the expansion nodes are the nodes with the lowest cost among all search target nodes, that is, the nodes that are included in the path among all nodes.

[0026] S102: Based on the first path and the second path, the lengths of the first path and the second path are calculated respectively. The specific calculation process includes: For the first path, the path length is calculated based on the Dubins curve to obtain the first path length; For the second path, the path length is calculated based on the Dubins curve to obtain the second path length.

[0027] S102: Based on the first path length and the second path length, determine whether the total path length of the inspection process meets the drone's cruising distance requirements, specifically including: The lengths of the first and second paths are summed, and the sum is compared with the drone's maximum cruising radius. If the sum is less than the drone's maximum cruising radius, it means that the drone's cruising distance requirement is met; otherwise, it means that the drone's cruising distance requirement is not met.

[0028] S103: Obtain the number of drones determined by the user; wherein the number of drones is set according to the actual number of drones. If the number of drones is less than four, the number of drones is set to four, and finally only the path matching the actual number of drones is output.

[0029] S104: Based on the drone's travel radius and the width of the road segment to be inspected, determine whether the road segment to be inspected can be covered for inspection, specifically including: Determine if the sum of the actual travel radii of the inspection drones is less than the width of the inspection section. If it is less, it means the section cannot be covered; if it is greater, it means the section can be covered.

[0030] S104: If the inspection can be covered, then determine the takeoff point of the drone. S105: Determine a third path, which is a fine path for each UAV from its takeoff point to the starting point of the inspection section to be inspected.

[0031] S10511: Calculate fitness, with the first three fitness values ​​(from smallest to largest) denoted as follows: , , , S10512: Update the positions of the remaining points; S10513: Local search based on Gaussian distribution.

[0032] S105-14: Update , , The location is determined, and the third path is output after iterating to the maximum number of iterations.

[0033] S105: Determine the fourth path, which is a fine path for each UAV from the starting point of the inspection section to the ending point of the inspection section.

[0034] S10521: Based on the drone inspection radius and inspection route width, the line is evenly divided into several drone inspection areas along a direction parallel to the longitudinal centerline of the power line.

[0035] S10522: Based on the inspection areas divided in S10521, establish a total set of taboo points for each area. The total set of taboo points includes: a set of static obstacle taboo points and a set of dynamic obstacle taboo points; the dynamic obstacles are dynamic obstacles detected by the camera.

[0036] S10523: Determine whether there is a vertical section in each inspection area that the drone cannot pass through. If not, proceed to step S10524; if so, proceed to step S10525. A vertical section refers to a plane that is perpendicular to the road surface in each inspection area.

[0037] S10524: Treat the centerline of the route within each inspection area as the length of the 3D map and the flight altitude of the UAV as the height of the 3D map. Establish a 3D map within the rectangular area formed by the length and height. Take the center point of the inspection area corresponding to the starting end of the road segment to be inspected as the starting point and the center point of the inspection area corresponding to the ending end of the road segment as the ending point. Perform path planning based on the adaptive expansion domain algorithm.

[0038] S10525: The inspection area with impassable sections obtained in S10523 is integrated with one or two adjacent inspection areas into one inspection area. The inspection drone is the drone corresponding to each area before the merger. The remaining areas are searched according to step S10524.

[0039] S105: Determine the fifth path, which is the return path of each UAV from the midpoint of the end of the road segment to be inspected to the starting point. Specifically, it includes setting the midpoint of the end of the road segment to be inspected as the starting point and the starting point as the destination, and using the method of determining the third path to perform path planning to obtain the fifth path.

[0040] S105: Combine the third, fourth, and fifth paths of each UAV to obtain the complete path of each UAV, specifically including: connecting the third, fourth, and fifth paths of each UAV in sequence to obtain the complete path of the current UAV.

[0041] By following the above method, the travel distance of each drone can be made relatively average, thus avoiding the risk of being unable to return.

[0042] S106: Determine whether there are path conflicts between the complete paths of the drones. If not, output the path directly; if so, fine-tune the path, specifically including: S106-1: Calculate the radius of the drone safety sphere. If the distance between the centers of the two drone safety spheres is D < 2... If so, it is determined to be a path conflict, and proceed to S106-2; S106-2: Calculate the time when the conflict drone arrives at the conflict point, if The path does not need to be adjusted; otherwise, proceed to step S106-3. S106-3: Local replanning is performed based on the improved Hybrid A* algorithm, and a conflict penalty term is added to the cost function of the new path.

[0043] S106-1: Calculate the radius of the drone's safety sphere; S106-2: , ; in, , This represents the path length from the drone to the point of conflict. , For flight speed, if No adjustment is needed; otherwise, proceed to step S106-3. S106-3: Local replanning based on the improved Hybrid A* algorithm, with a new conflict penalty term added to the cost function of the new path; S107: Detect dynamic obstacles by using the visual sensors and lidar mounted on the UAV to detect dynamic obstacles and calculate the speed and direction of movement of the dynamic obstacles. Calculate the predicted intersection time between dynamic obstacles and the current planned path; If it is less than the safety warning time If the conditions are met, return to S101 for replanning; otherwise, replanning is not required.

[0044] This invention first improves the Hybrid A* algorithm, requiring only the traversal of adjacent nodes between the current node and the target node for expansion selection. The improved algorithm then performs initial path calculation considering only static obstacles, detecting drones unable to return due to energy constraints. Furthermore, it generates initial takeoff points by creating random points, planning the path from the starting point to the inspection section using swarm intelligence. Simultaneously, it performs local search using a high-speed distribution, thus combining the improved algorithm with a swarm intelligence method using Gaussian distribution for region search. This allows for partitioned planning of inspection paths and return paths, achieving full-process path planning for multi-inspection drones on high-voltage power lines.

[0045] This invention comprehensively considers path planning tasks at different stages of the UAV inspection process, providing a complete path from departure to inspection and return. It also uses cameras to detect dynamic obstacles during flight, meeting multiple requirements such as inspection coverage, fuel constraints, and obstacle avoidance. Furthermore, real-time detection of dynamic obstacles avoids risks caused by sudden changes in dynamic obstacles during mission execution, providing a more comprehensive and reasonable path planning solution for multi-UAVs.

[0046] like Figure 2 As shown in Embodiment 2: A multi-UAV high-voltage line inspection full-process path planning system is configured to include: a map building module, a judgment module, a determination module, a planning module, a multi-UAV collaboration module, and a dynamic update module; The map building module is configured to: construct a 3D environment model including towers, trees, and terrain; and within the 3D environment model, determine the first and second paths of the UAV according to the improved Hybrid A* algorithm. The judgment module is configured to: calculate the path length using the Dubins curve length calculation formula based on the first path length and the second path length, and determine whether the path length of the entire inspection process meets the drone's cruising distance requirements; if it meets the requirements, obtain the number of drones input by the user; if it does not meet the requirements, provide a prompt. The determination module is configured to: determine whether the line to be inspected can be covered for inspection based on the inspection radius of the drone and the span and tower spacing of the high-voltage line to be inspected; if it cannot be covered for inspection, then re-acquire the number of drones input by the user; if it can be covered for inspection, then use the take-off point calculation function described in the claims to determine the take-off point of the drone when it departs. The planning module is configured to: determine the third, fourth, and fifth paths, and combine the third, fourth, and fifth paths of each UAV to obtain the complete path of each UAV. The multi-drone collaboration module is configured to use a conflict judgment formula and replanning method to coordinate the flight trajectories of multiple UAVs to avoid collisions. The dynamic update module is configured to use a dynamic obstacle speed and direction calculation method to detect dynamic obstacles and trigger path replanning.

[0047] The number of devices and processing scale described herein are for simplification purposes. Applications, modifications, and variations of this invention will be readily apparent to those skilled in the art. Although embodiments of the invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for this invention, and further modifications can be readily implemented by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, this invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for path planning throughout the entire process of multi-UAV high-voltage line inspection, characterized in that, Includes the following steps: S1. Construct a three-dimensional environment model containing towers, trees, and terrain, and within the three-dimensional environment model, determine the first path and the second path of the UAV according to the improved Hybrid A* algorithm. S2. Based on the first path and the second path, calculate the length of the first path and the length of the second path respectively, determine the take-off point of the drone, and then determine whether the path length of the entire inspection process meets the zero cruise distance requirement of the drone. S3. Obtain the number of drones input by the user; S4. Determine whether the high-voltage line to be inspected can be covered for inspection based on the drone inspection radius and the span and tower spacing of the high-voltage line to be inspected. S5. Determine the third, fourth, and fifth paths, and combine the third, fourth, and fifth paths of each UAV to obtain the complete path of each UAV.

2. The method for path planning throughout the entire process of multi-UAV high-voltage line inspection as described in claim 1, characterized in that, Step S5 specifically includes: The third path is a fine-grained path for each drone from its takeoff point to the starting point of the inspection of the high-voltage line to be inspected. The fifth path is the return path for each drone from the midpoint of the high-voltage line to be inspected to the starting point.

3. The method for path planning throughout the entire process of multi-UAV high-voltage line inspection as described in claim 1, characterized in that, The first path is a rough path taken by the drone from its starting point to the center point of the starting end of the high-voltage line to be inspected.

4. The method for full-process path planning of multi-UAV high-voltage line inspection as described in claim 3, characterized in that, The second path is a rough path from the center point of the starting end of the high-voltage line to be inspected to the center point of the ending end of the high-voltage line to be inspected.

5. The method for path planning throughout the entire process of multi-UAV high-voltage line inspection as described in claim 1, characterized in that, The three-dimensional environment model is obtained by three-dimensional modeling of static obstacles in three regions, and the three-dimensional coordinates of the static obstacles in the three regions are used as the set of static obstacle taboo points; the three regions refer to: the region between the drone take-off point and the center point of the starting end of the high-voltage line to be inspected, the region between the center point of the starting end of the high-voltage line to be inspected and the center point of the ending end of the high-voltage line to be inspected, and the region between the center point of the ending end of the high-voltage line to be inspected and the drone take-off point. The distance from the end of the high-voltage line to be inspected to the starting point is greater than the distance from the beginning of the high-voltage line to the starting point.

6. The method for path planning throughout the entire process of multi-UAV high-voltage line inspection as described in claim 1, characterized in that, Based on the first path length and the second path length, determine whether the total path length of the inspection process meets the drone's cruising distance requirements, specifically including: The lengths of the first path L1 and the second path L2 are calculated using Dubins curves. The formula for calculating the length of a Dubins curve is as follows: ； Where r is the minimum turning radius of the drone. Let d be the turning angle and d be the length of the straight segment; + +2 Where L redundancy is the emergency continuation redundancy distance, with a value ranging from 500 to 1000, which is the same as the drone's maximum cruising distance. In comparison.

7. The method for path planning throughout the entire process of multi-UAV high-voltage line inspection as described in claim 1, characterized in that, The third path is a detailed path for each drone from its takeoff point to the starting point of the inspection of the high-voltage line to be inspected, specifically including: Calculate fitness, with the top three fitness values ​​from smallest to largest as follows: , , ; Update the positions of all other points; Local search based on Gaussian distribution; renew , , The third path is output after iterating to the maximum number of iterations.

8. The method for path planning throughout the entire process of multi-UAV high-voltage line inspection as described in claim 1, characterized in that, The fourth path is a detailed path for each drone from the starting point of the inspection of the high-voltage line to the ending point of the inspection, specifically including: 2-1. Based on the drone inspection radius and the spacing between high-voltage line conductors, divide the line to be inspected into several drone inspection areas evenly along the direction parallel to the high-voltage line. 2-2. Establish a total set of prohibited points for each region. The total set of prohibited points includes: a set of prohibited points for static obstacles and a set of prohibited points for dynamic obstacles. 2-3. Determine if there is a vertical section in each inspection area that cannot be passed by the drone. If not, proceed to step (2-4); if yes, proceed to step (2-5). 2-4. Establish a three-dimensional grid map in each inspection area. With the starting point of the area to be inspected as the starting point and the ending point as the ending point, plan the inspection path of "straight line + arc" combination based on the improved Hybrid A* algorithm, and plan the surrounding trajectory around the tower. 2-5. Integrate the inspection areas with impassable sections with adjacent areas, adopt multi-machine collaborative path planning, and replan the path based on the improved Hybrid A* algorithm.

9. The method for full-process path planning of multi-UAV high-voltage line inspection as described in claim 1, characterized in that, It also includes multi-machine cooperative obstacle avoidance and dynamic obstacle handling, wherein the multi-machine cooperative obstacle avoidance specifically includes: Calculate the radius of the drone's safety sphere; Calculate the time when the conflict drones arrive at the conflict point; Local replanning is performed based on the improved Hybrid A* algorithm, and a conflict penalty term is added to the cost function of the new path; The dynamic obstacle processing specifically includes: detecting dynamic obstacles using visual sensors and lidar mounted on the UAV, calculating the speed and direction of movement of the dynamic obstacles, and calculating the predicted intersection time between the dynamic obstacles and the currently planned path.

10. A multi-UAV high-voltage line inspection full-process path planning system, characterized in that, The map building module is used to construct a 3D environment model containing towers, trees, and terrain; within the 3D environment model, the first and second paths of the UAV are determined according to the improved Hybrid A* algorithm. The judgment module is used to calculate the path length based on the first path length and the second path length using the Dubins curve length calculation formula; The determination module is used to determine whether the line to be inspected can be covered by the inspection based on the inspection radius of the drone and the span and tower spacing of the high-voltage line to be inspected. The planning module is used to determine the third, fourth, and fifth paths, and combines the third, fourth, and fifth paths of each UAV to obtain the complete path for each UAV. The multi-drone collaboration module is used to coordinate the flight trajectories of multiple UAVs to avoid collisions by employing conflict judgment formulas and replanning methods. The dynamic update module is used to detect dynamic obstacles and trigger path replanning by employing dynamic obstacle speed and direction calculation methods.

Images