Unmanned aerial vehicle local dynamic path planning method and device for power distribution network line inspection

Abstract

The application discloses a kind of unmanned aerial vehicle local dynamic path planning method and equipment for distribution network line inspection, and the method steps include: step S01.Obtain the start, target position of controlled unmanned aerial vehicle and the information of obstacle;Step S02.Calculate the attraction and repulsion of unmanned aerial vehicle respectively, and obtain total potential;Step S03.Judge whether unmanned aerial vehicle is trapped in local minimum value, if yes, enter step S04, otherwise enter step S05;Step S04.Recalculate the total potential currently received, obtain the direction of current lateral force, adjust the size of lateral force to make exit local minimum value, add lateral force to total potential;Step S05.Determine the moving direction and distance of unmanned aerial vehicle next time and update, return to step S02 until unmanned aerial vehicle reaches target position or meets the condition.The application can accurately avoid obstacles during the flight of inspection unmanned aerial vehicle, while avoiding being trapped in local minimum value.

Description

Technical Field

[0001] This invention relates to the field of power distribution line inspection technology, and in particular to a method and equipment for local dynamic flight path planning of unmanned aerial vehicles (UAVs) for power distribution line inspection. Background Technology

[0002] The application of drones in the inspection of overhead power lines eliminates the need for manual labor to traverse complex terrain and long-span power lines, improving inspection efficiency and reducing accident risks. However, since power distribution networks are mainly located in rural or mountainous areas, the height of overhead line corridors is generally between 10 and 15 meters, making them prone to trees and bamboo encroaching on the line passages. This seriously affects the flight safety and inspection efficiency of drones, thus limiting the application of autonomous drone flight.

[0003] In existing technologies, inspection drones typically utilize various sensors to accurately detect obstacles and employ traditional path planning algorithms such as Dijkstra's algorithm or APF (Artificial Potential Field) algorithm for path planning. However, Dijkstra's algorithm is poorly adaptable to complex environments. When inspection robots fly over complex environments such as mountainous areas, they struggle to accurately plan detours due to issues like trees and bamboo obstructing the path or unexpected obstacles. APF algorithms, on the other hand, are prone to getting stuck in local minima, causing the drone to stagnate or wander within a small area. These issues result in poor adaptability for autonomous flight and pose flight safety risks. Summary of the Invention

[0004] The technical problem to be solved by this invention is: In view of the technical problems existing in the prior art, this invention provides a local dynamic trajectory planning method and equipment for UAVs used for power distribution line inspection that is simple to implement, highly efficient, and highly adaptable and flexible. It can accurately avoid obstacles during the flight of the inspection UAV and avoid getting trapped in local minima.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0006] A local dynamic trajectory planning method for UAVs used in power distribution network line inspection, which involves UAVs searching and planning trajectories on a 3D map constructed from a 3D environment, includes the following steps:

[0007] Step S01. Obtain the starting position and target position of the controlled drone, as well as information on obstacles in the environment;

[0008] Step S02. Calculate the attractive force of the target position on the drone and the repulsive force of the obstacle on the drone based on the distance between the controlled drone and the target position and the obstacle, and combine the calculated attractive and repulsive forces to obtain the total force.

[0009] Step S03. Determine whether the controlled drone is trapped in a local minimum based on its flight status. If so, proceed to step S04; otherwise, proceed to step S05.

[0010] Step S04. Recalculate the total force on the current position of the controlled drone, obtain the current lateral force direction based on the current total force vector, adjust the magnitude of the current lateral force so that the controlled drone exits the local minimum, and add the current lateral force to the total force.

[0011] Step S05. Based on the current total power, determine the direction and distance of movement of the controlled drone in the next moment, update the drone's position, and return to step S02 until the controlled drone reaches the target position or meets the preset termination condition.

[0012] Furthermore, the attractive force exerted by the target location on the drone is calculated according to the following formula:

[0013]

[0014] in, Current location q The attraction of the place Current location q The gravitational potential energy at that location The direction is the drone pointing to the target point, and the size is the current position. q With the target point Distance between | q- | is a vector.

[0015] Furthermore, the repulsive force exerted by the obstacle on the drone is calculated according to the following formula:

[0016]

[0017] in Indicates from Pointing to from unit vector, Represented as:

[0018]

[0019] in, k The proportionality constant representing the repulsive force. This represents the maximum distance threshold at which obstacles can affect the drone.

[0020] Further, in step S02, the calculation expression for the total force obtained by combining the calculated attractive and repulsive forces is as follows:

[0021] .

[0022] Furthermore, step S01 also includes configuring a proportional constant between the attractive and repulsive forces. and This allows for the separate control of the strength of attractive and repulsive forces.

[0023] Furthermore, in step S03, if it is determined that the drone is stagnant or flying back and forth within an area smaller than a preset size, then it is determined that the drone is trapped in a local minimum.

[0024] Further, in step S04, obtaining the current lateral force direction based on the current total force vector, and adjusting the magnitude and direction of the current lateral force to cause the controlled drone to exit the local minimum includes:

[0025] Rotate the current total force vector by 90 degrees to obtain the lateral force direction;

[0026] Adjust the magnitude of the current lateral force based on the current attractive force until the controlled drone exits the local minimum.

[0027] Furthermore, adjust the magnitude of the current lateral force according to the current magnitude of the attractive force using the following formula:

[0028]

[0029] in, It is a coefficient that adjusts the magnitude of the lateral force. This represents the adjusted lateral force vector. This represents the direction vector of the lateral force. By attraction vector Rotate 90 degrees to obtain.

[0030] An electronic device includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method described above.

[0031] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0032] Compared with existing technologies, the advantages of this invention are as follows: This invention realizes local dynamic trajectory planning for power distribution line inspection drones based on the APF algorithm. At the same time, by introducing lateral force, when the drone detects that it may be in a local minimum, it can use the lateral force to help the drone jump out of the local minimum point, ensuring that the drone can effectively avoid obstacles without getting trapped in a local minimum or deviating too far from the trajectory. This effectively completes the dynamic planning task and solves the problems of insufficient ability of traditional APF algorithms to handle complex environments and unexpected obstacle threats in mountainous inspection flights. It also helps to reduce the dependence on high-end drone equipment and flight operators, and can reduce the labor intensity of operators, improve the efficiency of power distribution line inspection, and enhance the level of automation and intelligence. Attached Figure Description

[0033] Figure 1 This is a schematic diagram illustrating the implementation process of the UAV local dynamic trajectory planning method for power distribution network line inspection in this embodiment.

[0034] Figure 2 This is the main view (corresponding to) when the traditional APF algorithm performs trajectory planning. Figure 2 (a) and side view (corresponding) Figure 2 (b)

[0035] Figure 3 This is a top-down view of the traditional APF algorithm during trajectory planning (corresponding to...) Figure 3 (a) and the global view (corresponding to) Figure 3 (b)

[0036] Figure 4 This is the main view (corresponding to) used in this embodiment for trajectory planning with the improved APF algorithm. Figure 4 (a) and the side view (corresponding) Figure 4 (b)

[0037] Figure 5 This is a top view of the improved APF algorithm used in this embodiment for trajectory planning (corresponding to...). Figure 5 (a) Middle view, global view (corresponding) Figure 5 (b) Detailed Implementation

[0038] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0039] This invention relates to three-dimensional local dynamic trajectory planning for UAVs in power distribution line inspection applications. In a three-dimensional environment, the UAV first explores obstacles and constructs a three-dimensional map. After the three-dimensional map is established, three-dimensional local dynamic trajectory planning is performed on the map to avoid obstacles and autonomously fly from the starting position to the target position. During the flight, the inspection robot completes the inspection of the power distribution line.

[0040] like Figure 1 As shown, the steps of the UAV local dynamic trajectory planning method for power distribution network line inspection in this embodiment include:

[0041] Step S01. Obtain the starting position and target position of the controlled drone, as well as information on obstacles in the environment.

[0042] This embodiment first performs initialization settings, obtains the starting position and target position to determine the start and end positions of the drone, and at the same time obtains the position and size of each obstacle in the environment for subsequent path planning to avoid each obstacle.

[0043] In the initial configuration phase, this embodiment also includes configuring the proportional constants of attractive and repulsive forces. and The strength of attractive and repulsive forces can be controlled separately, and a distance threshold for the repulsive force can be set. This is used for subsequent calculations of repulsive force. Understandably, other types of environmental parameters can also be initialized according to actual needs.

[0044] Step S02. Calculate the attractive force of the target position on the drone and the repulsive force of the obstacle on the drone based on the distance between the controlled drone and the target position and the obstacle, and combine the calculated attractive and repulsive forces to obtain the total force.

[0045] During the flight of the controlled drone from its starting position to its target position, the attractive force of the target position on the drone and the repulsive force of the obstacle on the drone are calculated in real time based on the APF algorithm. The total force on the drone is obtained by combining the attractive force and the repulsive force. Then, based on the total force, the drone's next movement direction and movement distance can be planned according to the APF algorithm.

[0046] The gravitational pull exerted by the target location on the drone is a vector pointing towards the target, and its magnitude is directly proportional to the distance between the drone and the target. The magnitude of the gravitational pull is affected by the distance between the drone and the target; as the distance increases, the gravitational pull on the drone increases, and vice versa. For example, gravitational potential energy... Size can be represented as:

[0047] (1)

[0048] Gravitational potential energy The magnitude depends on the gravitational field coefficient. and the distance between the drone and the target point, of which The direction is the drone pointing to the target point, and the size is the current position. q With the target point Distance between | q- | vector, i.e. It is a vector whose direction is from the drone to the target point, and whose magnitude is equal to the drone's current position. q With the target point Euclidean distance between them | q- | Confirmed. The negative gradient of the gravitational field is the attractive force. This refers to the force, or attraction, that guides the drone toward the target point. It can be represented as:

[0049] (2)

[0050] Real-time based on the drone's current location q With the target point Distance between | q- The attractive force can be calculated according to the above formula (2). .

[0051] Further calculations are needed to determine the repulsive force generated by obstacles in the environment. This reflects the dynamic characteristics of the interaction between the UAV and the obstacle and is crucial for achieving dynamic trajectory planning and obstacle avoidance mechanisms. The direction of the repulsive force is opposite to the direction in which the UAV moves towards the obstacle, and its magnitude is inversely proportional to the distance between the UAV and the obstacle. Furthermore, the repulsive force only works when the distance between the UAV and the obstacle is less than a certain threshold. The repulsive force only comes into play when the distance between the drone and the obstacle is inversely proportional to the magnitude of the repulsive force.

[0052] Specifically, in a repulsive potential field, the distance between the drone and the obstacle plays a decisive role. Before entering the obstacle's influence zone, the repulsive potential energy is zero. Once inside the obstacle's influence zone, the repulsive potential energy changes significantly. The distance between the two is the main factor determining the magnitude of the repulsive force: decreasing distance increases the repulsive force, and vice versa. (Repulsive potential field) It can be represented as:

[0053] (3)

[0054] in, kThe repulsive potential field coefficient is a proportionality constant representing the repulsive force. It is a vector, with its direction pointing from the drone's current position to the target point, and its magnitude determined by the drone's current position. q With the target point Euclidean distance between |q- Confirmed. This represents the maximum distance threshold at which an obstacle affects the drone. The repulsive force is the negative gradient of the repulsive potential field, which can be calculated using the following formula:

[0055] (4)

[0056] in, Indicates from Pointing to from unit vector, It can be represented as:

[0057] (5)

[0058] The total force, which is the sum of the attractive and repulsive forces, is obtained by combining the forces of attraction and repulsion, i.e., the force of the drone. q The net force on the point It can be represented as:

[0059] (6)

[0060] This embodiment, by employing the above method, can synthesize the attractive force and all calculated repulsive forces to obtain the resultant force acting on the drone. This resultant force determines the drone's direction and distance of movement in each step. 。

[0061] Step S03. Determine whether the controlled drone is trapped in a local minimum based on its flight status. If so, proceed to step S04; otherwise, proceed to step S05.

[0062] In this embodiment, if it is determined that the drone is stationary or flying back and forth within an area smaller than a preset size, it is determined that the drone is trapped in a local minimum. If it is determined that the drone is trapped in a local minimum, then proceed to step S04; otherwise, jump directly to step S05.

[0063] Traditional APF algorithms are prone to getting trapped in local minima during UAV path planning. This embodiment monitors local minima during UAV flight. When a local minima is detected—that is, when the UAV stops moving or hovers within a small area—the system promptly proceeds to step S04 to adjust the lateral force. This helps the UAV quickly escape the local minima, thus solving the problem of traditional APF algorithms easily getting trapped in local minima.

[0064] Step S04. If the controlled drone gets stuck in a local minimum, recalculate the total force on the controlled drone at its current position, and obtain the current lateral force direction based on the current total force vector. By adjusting the magnitude and direction of the current lateral force, the controlled drone can exit the local minimum.

[0065] When the drone is identified as being trapped in a local minimum, the lateral force adjustment is initiated. By first recalculating the total force on the controlled drone at its current position, the direction of the current lateral force is obtained based on the current total force vector. Then, by adjusting the magnitude and direction of the current lateral force, the drone can effectively escape the dead zone when it encounters a local minimum, thus completing the dynamic programming task.

[0066] In this embodiment, the steps of obtaining the current lateral force direction based on the current total force vector and adjusting the magnitude and direction of the current lateral force to make the controlled drone exit the local minimum include:

[0067] Rotate the current total force vector by 90 degrees to obtain the lateral force direction;

[0068] Adjust the magnitude of the current lateral force based on the current attractive force until the controlled drone exits the local minimum.

[0069] Specifically, in order to deal with the local minimum situation, when the UAV is identified to be trapped in the local minimum situation, all forces it is subjected to are recalculated. The lateral force is perpendicular to the direction of the total force. The direction of the lateral force is obtained by rotating the total force vector by 90 degrees. The magnitude of the lateral force is adjusted according to the current situation according to formula (7) to help the UAV escape from the local minimum. Then the lateral force is added to the total force, and then the process jumps to step S05.

[0070] As an alternative implementation, the magnitude of the current lateral force can be adjusted according to the current attractive force using the following formula:

[0071] (7)

[0072] in, It is a coefficient that adjusts the magnitude of the lateral force. This represents the adjusted lateral force vector. This represents the direction vector of the lateral force. By attraction vector Rotate 90 degrees to obtain.

[0073] The following describes the method of the present invention in further detail by taking the example of controlling a drone to escape a local minimum in a specific application embodiment. The detailed steps are as follows:

[0074] Step S401. Calculate the total force: When it is detected that the current drone is trapped in a local minimum, recalculate the total force (resultant force) on the drone, that is, its magnitude can be expressed as:

[0075]

[0076] Step S402. Determine the lateral force direction: The lateral force is perpendicular to the total force direction. Rotate the total force vector by 90 degrees to obtain the lateral force direction, so as to ensure that the UAV VU will not fall into a local minimum further.

[0077] Direction of lateral force: Horizontal force direction vector Depend on Rotate 90 degrees to obtain, for example, rotate clockwise: .

[0078] Step S403. Calculate the lateral force: Select the magnitude and direction of the lateral force based on the current situation. and The sizes are proportional, as shown in Equation (7), to help the drone escape from the local minimum.

[0079] Step S05. Based on the current total power, determine the direction and distance of movement of the controlled drone in the next moment, update the drone's position, and return to step S02 until the controlled drone reaches the target position or meets the preset termination condition.

[0080] In specific application embodiments, local dynamic trajectory planning is performed using both the traditional APF algorithm and the APF algorithm improved by introducing lateral force in this invention, and the results are as follows: Figures 2-5 As shown, where Figure 2 , 3 These correspond to the front view, side view, top view, and global view obtained by the traditional APF algorithm, respectively. As can be seen from the figure, in high obstacle density and complex environment, the traditional APF algorithm faces a significant local minimum problem when performing local trajectory planning. This can cause the UAV to get stuck in the dead zone and thus fail to generate a trajectory. Figure 4 , 5 These correspond to the main view, side view, top view, and global view of the trajectory planning obtained by introducing the lateral force-improved APF algorithm in this invention, respectively. As can be seen from the results in the figures, compared with… Figure 2 , 3 Compared to traditional APF algorithms that fail to generate tracks, the improved APF algorithm introduced in this invention, by introducing a lateral force, successfully escapes the local minimum trap when encountering it. This is achieved by adding a lateral force vector perpendicular to the gravity vector and with a magnitude 0.3 times that of the gravity vector, and effectively generates a valid track. In other words, the APF algorithm improved by introducing a lateral force in this invention can effectively prevent the UAV from getting trapped in local minima, ultimately enabling it to effectively complete dynamic programming and reach the target position.

[0081] This embodiment uses the above method to realize local dynamic trajectory planning of power grid line inspection drones based on the APF algorithm, accurately avoiding obstacles in the environment. At the same time, by introducing lateral force, when the drone detects that it may be in a local minimum, the lateral force can be used to help the drone jump out of the local minimum point, ensuring that the drone can effectively avoid obstacles, rather than getting stuck in other local minima or deviating too far from the trajectory.

[0082] This invention addresses the three-dimensional local dynamic trajectory planning of UAVs used for power distribution line inspection. By introducing lateral force to form an improved APF algorithm based on the APF algorithm, the UAV achieves local dynamic trajectory planning. This effectively enables the UAV to escape dead zones when encountering local minima by using lateral force, thus completing the dynamic planning task. It solves the problem that the traditional APF algorithm is insufficient in handling complex environments and unexpected obstacles in mountainous inspection flights. At the same time, it helps reduce the dependence on high-end UAV equipment and flight operators, and can also reduce the labor intensity of operators, improve the efficiency of power distribution line inspection, and enhance the level of automation and intelligence.

[0083] This embodiment further provides an electronic device, including a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the method as described above.

[0084] It is understood that the method described in this embodiment can be executed by a single device, such as a computer or server, or it can be applied to a distributed scenario where multiple devices cooperate to complete the task. In a distributed scenario, one of the multiple devices may execute only one or more steps of the method described in this embodiment, and the multiple devices interact to complete the method. The processor can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the method described in this embodiment. The memory can be implemented using read-only memory (ROM), random access memory (RAM), static storage devices, and dynamic storage devices. The memory can store the operating system and other applications. When the method described in this embodiment is implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0085] This embodiment further provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0086] Those skilled in the art will understand that the above embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for local dynamic trajectory planning of unmanned aerial vehicles (UAVs) for power distribution network line inspection, used for UAV trajectory search and planning on a 3D map constructed from a 3D environment, characterized by the following steps: include: Step S01. Obtain the starting position and target position of the controlled drone, as well as information on obstacles in the environment; Step S02. Calculate the attractive force of the target position on the drone and the repulsive force of the obstacle on the drone based on the distance between the controlled drone and the target position and the obstacle, and combine the calculated attractive and repulsive forces to obtain the total force. Step S03. Determine whether the controlled drone is trapped in a local minimum based on its flight status. If so, proceed to step S04; otherwise, proceed to step S05. Step S04. Recalculate the total force on the current position of the controlled drone, obtain the current lateral force direction based on the current total force vector, adjust the magnitude of the current lateral force so that the controlled drone exits the local minimum, and add the current lateral force to the total force. Step S05. Based on the current total power, determine the direction and distance of movement of the controlled drone in the next moment, update the position of the drone, and return to step S02 until the controlled drone reaches the target position or meets the preset termination condition. In step S04, the current lateral force direction is obtained based on the current total force vector. Adjusting the magnitude and direction of the current lateral force to allow the controlled drone to exit the local minimum includes: Rotate the current total force vector by 90 degrees to obtain the lateral force direction; Adjust the current lateral force based on the current attractive force until the controlled drone exits the local minimum. Adjust the current lateral force according to the current attractive force using the following formula: in, It is a coefficient that adjusts the magnitude of the lateral force. This represents the adjusted lateral force vector. This represents the direction vector of the lateral force. By attraction vector Rotate 90 degrees to obtain.

2. The UAV local dynamic trajectory planning method for power distribution network line inspection according to claim 1, characterized in that, The attractive force exerted by the target location on the drone is calculated using the following formula: in, Current location q The attraction of the place Current location q The gravitational potential energy at that location The direction is the drone pointing to the target point, and the size is the current position. q With the target point Distance between | q- | is a vector.

3. The UAV local dynamic trajectory planning method for power distribution network line inspection according to claim 2, characterized in that, The repulsive force exerted by the obstacle on the drone is calculated using the following formula: in Indicates from Pointing to from unit vector, Represented as: in, k The proportionality constant representing the repulsive force. This represents the maximum distance threshold at which obstacles can affect the drone.

4. The UAV local dynamic trajectory planning method for power distribution network line inspection according to claim 3, characterized in that, In step S02, the calculation expression for the total force obtained by combining the calculated attractive and repulsive forces is as follows: 。 5. The UAV local dynamic trajectory planning method for power distribution line inspection according to claim 1, characterized in that, Step S01 further includes configuring a proportional constant for the attractive and repulsive forces. and This allows for the separate control of the strength of attractive and repulsive forces.

6. The UAV local dynamic trajectory planning method for power distribution line inspection according to any one of claims 1 to 5, characterized in that, In step S03, if it is determined that the drone is stagnant or flying back and forth within an area smaller than a preset size, then the drone is determined to be trapped in a local minimum.

7. An electronic device comprising a processor and a memory, the memory being used to store a computer program, characterized in that, The processor is used to execute the computer program to perform the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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