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

Abstract

The invention discloses an unmanned aerial vehicle local dynamic flight path planning method and equipment for power distribution network line inspection. The method comprises the following steps: step S01, acquiring starting and target positions of a controlled unmanned aerial vehicle as well as information of an obstacle; s02, respectively calculating the attraction force and the repulsive force suffered by the unmanned aerial vehicle, and synthesizing the attraction force and the repulsive force to obtain total force; s03, judging whether the unmanned aerial vehicle falls into a local minimum value or not, if so, turning to execute the step S04, and otherwise, turning to execute the step S05; step S04, recalculating the current total force to obtain the current transverse force direction, adjusting the transverse force so as to quit the local minimum value, and adding the transverse force to the total force; and S05, determining and updating the moving direction and distance of the unmanned aerial vehicle at the next moment, and returning to the step S02 until the unmanned aerial vehicle reaches the target position or meets the conditions. According to the invention, obstacles can be accurately avoided in the flight process of the inspection unmanned aerial vehicle, and falling into a local minimum value is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network line inspection, and in particular to a method and device for local dynamic trajectory planning of an unmanned aerial vehicle for power distribution network line inspection. Background Art

[0002] The application of drones in power overhead line inspections makes it possible to no longer rely on manual labor to travel through complex terrain and large-span power lines, which can not only improve inspection efficiency but also reduce accident risks. However, since the distribution network is mainly distributed in rural or mountainous areas, the height of the overhead line corridor is generally between 10 and 15 meters, and it is easy for trees and bamboo to occupy the line channel, which will seriously affect the flight safety and inspection efficiency of drones. Therefore, the application of autonomous drone flight will be subject to many restrictions.

[0003] In the prior art, inspection drones are usually equipped with various types of sensors to accurately sense the presence of obstacles, and use traditional path planning algorithms such as the Dijkstra algorithm or the APF (artificial potential field) algorithm for path planning. However, the above-mentioned Dijkstra algorithm has poor adaptability to complex environments. When the inspection robot is flying in complex environments such as mountainous areas, it is difficult to accurately plan the path to bypass obstacles because trees and bamboos are prone to occupying the line channel and there are unexpected obstacles. The use of the APF algorithm is prone to falling into the local minimum, causing the drone to stagnate or wander in a small range, resulting in the drone's poor adaptability to autonomous flight and there will be flight safety risks. Summary of the invention

[0004] The technical problem to be solved by the present invention is: in response to the technical problems existing in the prior art, the present invention provides a method and device for local dynamic trajectory planning of unmanned aerial vehicles for distribution network line inspection, which has a simple implementation method, high efficiency, strong adaptability and flexibility, and can accurately avoid obstacles during the flight of the inspection unmanned aerial vehicle and avoid falling into local minimum values.

[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is: A method for local dynamic trajectory planning of an unmanned aerial vehicle for power distribution network line inspection is used for the unmanned aerial vehicle to perform trajectory search planning in a three-dimensional map constructed for a three-dimensional environment, and the steps include: Step S01. Obtain the starting position and target position of the controlled UAV and obtain information about obstacles in the environment; Step S02. Calculate the attraction force of the target position on the drone and the repulsion force of the obstacle on the drone according to the distance between the controlled drone and the target position and the obstacle, and synthesize the calculated attraction force and repulsion force to obtain the total force; Step S03. Determine whether the currently controlled drone is trapped in a local minimum value according to the flight state of the controlled drone. If so, proceed to step S04; otherwise, proceed to step S05. Step S04. Recalculate the total force on the current position of the controlled UAV, and obtain the current lateral force direction according to the current total force vector, adjust the magnitude of the current lateral force so that the controlled UAV exits the local minimum, and add the current lateral force to the total force; Step S05. Determine the moving direction and moving distance of the controlled UAV at the next moment according to the currently obtained total force, and update the position of the UAV, and return to step S02 until the controlled UAV reaches the target position or meets the preset termination condition.

[0006] Furthermore, the attraction of the target position to the drone is calculated according to the following formula:

[0007] in, For current location q The attraction of the place, For current location q The gravitational potential energy at A direction is for the drone to point to the target point, and the size is the current position q With the target point Distance between | q- |Vector.

[0008] Furthermore, the repulsive force generated by the obstacle on the drone is calculated according to the following formula:

[0009] in Indicates from Pointing from The unit vector of It is expressed as:

[0010] in, k represents the proportionality constant of the repulsive force, Indicates the maximum distance threshold at which obstacles affect the drone.

[0011] Furthermore, in step S02, the calculation expression of the total force obtained by synthesizing the calculated attractive force and repulsive force is: .

[0012] Furthermore, the step S01 also includes configuring the proportional constants of the attractive force and the repulsive force. and to control the strength of the attractive and repulsive forces respectively.

[0013] Furthermore, in step S03, if it is determined that the drone is stagnant or flying back and forth in an area smaller than a preset size range, it is determined that the drone is trapped in a local minimum.

[0014] Furthermore, in step S04, the current lateral force direction is obtained according to the current total force vector, and the magnitude and direction of the current lateral force are adjusted so that the controlled UAV exits the local minimum value, including: Rotate the current total force vector 90 degrees to get the lateral force direction; The current lateral force is adjusted according to the current attractive force until the controlled drone exits the local minimum.

[0015] Furthermore, the current lateral force is adjusted according to the current attractive force as follows:

[0016] in, is the coefficient that adjusts the magnitude of the lateral force, represents the adjusted lateral force vector, represents the direction vector of the lateral force, By attraction vector Rotate 90 degrees to get.

[0017] An electronic device comprises 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 above method.

[0018] A computer-readable storage medium storing a computer program, wherein the computer program implements the above method when executed by a processor.

[0019] Compared with the prior art, the advantages of the present invention are: the present invention realizes the local dynamic trajectory planning of the distribution network line inspection UAV based on the APF algorithm, and at the same time, by introducing lateral force, when the UAV detects that it may be in a local minimum situation, the lateral force can be used to help the UAV jump out of the local minimum point, ensuring that the UAV can effectively avoid obstacles without falling into the local minimum or deviating too far from the track, effectively completing the dynamic planning task, and solving the problem that the traditional APF algorithm is insufficient in dealing with complex environments and unexpected obstacle threats during mountain inspection flights. At the same time, it helps to reduce dependence on high-end UAV equipment and flight operators, and can also reduce the labor intensity of operators, improve the efficiency of distribution line inspections and the level of automation and intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1It is a schematic diagram of the implementation process of the method for local dynamic trajectory planning of unmanned aerial vehicles for distribution network line inspection in this embodiment.

[0021] Figure 2 This is the main view when the traditional APF algorithm is used for trajectory planning (corresponding to Figure 2 Middle (a)) and side view (corresponding to Figure 2 (b)

[0022] Figure 3 This is the top view when the traditional APF algorithm is used for trajectory planning (corresponding to Figure 3 (a)) and the global view (corresponding to Figure 3 (b)

[0023] Figure 4 This is the main view (corresponding to the improved APF algorithm used in this embodiment for track planning) Figure 4 (a)), and side view (corresponding to Figure 4 (b)

[0024] Figure 5 This is a top view (corresponding to the improved APF algorithm used in this embodiment) when performing track planning Figure 5 Middle (a)), global view (corresponding to Figure 5 (b) DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0026] The present invention is aimed at the three-dimensional local dynamic trajectory planning of UAVs in the application of distribution network line inspection. In a three-dimensional environment, the UAV is first used to explore obstacles and build a three-dimensional map. After the three-dimensional map is established, three-dimensional local dynamic trajectory planning is performed in the map to avoid obstacles and fly autonomously from the starting position to the target position. During the flight, the inspection robot completes the inspection of the distribution network line.

[0027] like Figure 1 As shown, the steps of the method for local dynamic trajectory planning of a drone for power distribution network line inspection in this embodiment include: Step S01: Obtain the starting position and target position of the controlled UAV and obtain information about obstacles in the environment.

[0028] This embodiment first performs initialization settings, obtains the starting position and target position to determine the starting and ending positions of the drone, and simultaneously obtains the position and size of each obstacle in the environment for use in subsequent path planning to avoid each obstacle.

[0029] In the initialization configuration stage, this embodiment also includes configuring the proportional constants of the attractive force and the repulsive force. and To control the strength of attraction and repulsion respectively, and set the distance threshold for repulsion It is understood that, of course, other types of environmental parameters can be initialized and configured according to actual needs.

[0030] Step S02: Calculate the attraction force of the target position on the drone and the repulsion force of the obstacle on the drone according to the distance between the controlled drone and the target position and the obstacle, and synthesize the calculated attraction force and repulsion force to obtain the total force.

[0031] During the flight of the controlled drone from the starting position to the target position, the APF algorithm is used to calculate in real time the attraction of the target position on the drone and the repulsion of obstacles on the drone. The total force exerted on the drone is obtained by combining the attraction and repulsion. Based on the total force, the APF algorithm can be used to plan the next moving direction and distance of the drone.

[0032] The attraction (gravitational force) generated by the target position on the drone is a vector pointing to the target, and its magnitude is proportional to the distance between the drone and the target. The magnitude of the attraction is affected by the distance between the drone and the target point. When the distance between the two increases, the value of the attraction on the drone will increase, and vice versa, it will gradually decrease. For example, gravitational potential energy The size can be expressed as: (1) Gravitational potential energy The size depends on the gravitational field coefficient and the distance between the drone and the target point, where A direction is for the drone to point to the target point, and the size is the current position q With target point Distance between | q- |, that is, is a vector whose direction is from the drone to the target point and whose magnitude is the current position of the drone. q With the target point Euclidean distance between | q- | OK. The negative gradient of the gravitational field is the attraction , which is the force that guides the drone to move toward the target point, the attractive force It can be expressed as: (2) Real-time based on the current location of the droneq With the target point Distance between | q- |, according to the above formula (2), the attraction can be calculated .

[0033] Furthermore, it is necessary to calculate the repulsive force (repulsion) generated by obstacles in the environment, which reflects the dynamic characteristics of the interaction between the drone and the obstacle and is the key to realizing dynamic trajectory planning and obstacle avoidance mechanism. The direction of the repulsive force is opposite to the direction of the drone moving towards the obstacle, and its magnitude is inversely proportional to the distance between the drone and the obstacle. And it is only when the distance between the drone and the obstacle is less than a certain threshold that the repulsive force will be generated. The repulsive force will only take effect when the drone is close to the obstacle, that is, the size of the repulsive force is inversely proportional to the distance from the drone to the obstacle.

[0034] Specifically, in the repulsive potential field, the distance between the drone and the obstacle plays a decisive role. When the drone has not yet entered the obstacle's influence area, the repulsive potential energy is zero. Once it enters the obstacle's influence range, the repulsive potential energy will change significantly. The distance between the two is the main factor that determines the magnitude of the repulsive force: as the distance decreases, the repulsive force increases. On the contrary, as the distance increases, the repulsive force decreases. It can be expressed as: (3) in, k is the proportional constant representing the repulsive force, and the repulsive potential field coefficient It is a vector whose direction is from the current position of the drone to the target point and whose magnitude is from the current position of the drone q With the target point The Euclidean distance between |q- |OK. Indicates the maximum distance threshold at which obstacles affect the drone. The repulsive force is the negative gradient of the repulsive potential field and can be calculated by the following formula: (4) in, Indicates from Pointing from The unit vector of It can be expressed as: (5) The total force can be obtained by combining the attractive force and the repulsive force, that is, the UAV q The total force on the point It can be expressed as: (6) By adopting the above method, this embodiment can synthesize the attraction and all the calculated repulsion forces to obtain the resultant force acting on the drone, and the resultant force determines the movement direction and movement distance of the drone in each step. 。

[0035] Step S03. Determine whether the currently controlled UAV is trapped in a local minimum value according to the flight status of the controlled UAV. If so, proceed to step S04; otherwise, proceed to step S05.

[0036] In this embodiment, if it is determined that the drone is stagnant or flying back and forth in an area smaller than a preset size range, the drone is determined to be trapped in a local minimum. If it is determined to be trapped in a local minimum, step S04 is entered, otherwise it directly jumps to step S05.

[0037] The traditional APF algorithm is prone to falling into the local minimum during the UAV path planning process. This embodiment monitors the local minimum during the flight of the UAV. When the UAV is identified to be trapped in the local minimum, that is, the UAV is checked to be stagnant or wandering in a small area, step S04 is promptly entered to adjust the lateral force to help the UAV quickly escape from the local minimum, thereby solving the problem that the traditional APF algorithm is prone to falling into the local minimum.

[0038] Step S04. If the controlled drone falls into a local minimum, recalculate the total force acting on the current position of the controlled drone, 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.

[0039] When it is identified that the UAV is trapped in a local minimum, the lateral force adjustment is initiated. The total force acting on the current position of the controlled UAV is recalculated first, and the current lateral force direction is obtained according to the current total force vector. Then, the magnitude and direction of the current lateral force are adjusted. By introducing lateral force, the UAV can effectively jump out of the dead zone when encountering a local minimum and complete the dynamic planning task.

[0040] In this embodiment, the steps of obtaining the current lateral force direction according to the current total force vector and adjusting the magnitude and direction of the current lateral force so that the controlled drone exits the local minimum value include: Rotate the current total force vector 90 degrees to get the lateral force direction; The current lateral force is adjusted according to the current attractive force until the controlled drone exits the local minimum.

[0041] Specifically, in order to deal with the local minimum situation, when it is identified that the drone is trapped in the local minimum situation, the sum of all the forces it is subjected to is 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 90 degrees. The magnitude of the lateral force is adjusted according to formula (7) according to the current situation to assist the drone to escape from the local minimum. Then the lateral force is added to the total force, and then the process jumps to step S05.

[0042] As an optional implementation, the current lateral force may be adjusted according to the current attractive force as follows: (7) in, is the coefficient that adjusts the magnitude of the lateral force, represents the adjusted lateral force vector, represents the direction vector of the lateral force, By attraction vector Rotate 90 degrees to get.

[0043] The method of the present invention is further described below by taking the example of controlling a drone to escape from a local minimum in a specific application embodiment, and the detailed steps are as follows: Step S401. Calculate the total force: When it is identified that the current UAV is trapped in a local minimum, recalculate the total force (combined force) on the UAV, that is, its magnitude can be expressed as:

[0044] Step S402. Determine the direction of the lateral force: The lateral force is perpendicular to the direction of the total force vector. The total force vector is rotated 90 degrees to obtain the direction of the lateral force to ensure that the drone vu will not fall further into the local minimum.

[0045] Lateral force direction: , the direction vector of the lateral force Depend on Rotate 90 degrees, for example clockwise: .

[0046] Step S403. Calculate the lateral force: select the magnitude and direction of the lateral force according to the current situation. and The size is proportional to each other, as shown in Equation (7), to help the UAV escape from the local minimum.

[0047] Step S05. Determine the moving direction and moving distance of the controlled UAV at the next moment according to the total force currently obtained, and update the position of the UAV, and return to step S02 until the controlled UAV reaches the target position or meets the preset termination condition.

[0048] In a specific application embodiment, the traditional APF algorithm and the improved APF algorithm after introducing the lateral force of the present invention are respectively used to perform local dynamic trajectory planning, and the results obtained are as follows: Figures 2~5 As shown, Figure 2 , 3 They correspond to the front view, side view, top view and global view obtained by the traditional APF algorithm. It can be seen from the figure that in high obstacle density and complex environment, the traditional APF algorithm faces a significant local minimum problem when performing local track planning, which will cause the drone to fall into a dead zone and fail to successfully generate a track. Figure 4 , 5 They correspond to the front view, side view, top view and global view of the track planning obtained by introducing the lateral force to improve the APF algorithm of the present invention. It can be seen from the results in the figure that Figure 2 , 3 Compared with the situation where the traditional APF algorithm cannot generate a track, the improved APF algorithm of the present invention that introduces lateral force successfully escapes the trap of the local minimum by adding a lateral force vector perpendicular to the gravity vector and 0.3 times the gravity when encountering a local minimum, and effectively generates a qualified track. That is, the improved APF algorithm of the present invention that introduces lateral force can effectively prevent the drone from falling into the local minimum situation, and finally can effectively complete dynamic planning to reach the target position.

[0049] Through the above method, this embodiment can realize the local dynamic trajectory planning of the distribution network line inspection drone based on the APF algorithm, accurately avoid obstacles in the environment, and at the same time, by introducing lateral force, when the drone detects that it may be in a local minimum situation, 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 instead of falling into other local minima or deviating too far from the track.

[0050] The present invention aims at the three-dimensional local dynamic trajectory planning of distribution network line inspection UAVs. By introducing lateral force on the basis of APF algorithm to form an improved APF algorithm, the local dynamic trajectory planning of the UAV is realized based on the improved APF algorithm. The UAV can effectively jump out of the dead zone through lateral force when encountering a local minimum and complete the dynamic planning task. The problem of the traditional APF algorithm's insufficient ability to handle complex environments and unexpected obstacle threats during mountain inspection flights is solved. At the same time, it helps to reduce the dependence on high-end UAV equipment and flight operators, and can also reduce the labor intensity of operators, and improve the efficiency of distribution line inspection and the level of automation and intelligence.

[0051] 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 above method.

[0052] It is understandable that the above method of this embodiment can be executed by a single device, such as a computer or server, etc., and can also be applied to a distributed scenario and completed by multiple devices in cooperation with each other. In the case of a distributed scenario, one of the multiple devices can only execute one or more steps in the above method of this embodiment, and multiple devices interact to complete the above method. The processor can be implemented in the form of a general-purpose CPU, a microprocessor, an application-specific integrated circuit, or one or more integrated circuits, etc., for executing related programs to implement the above method of this embodiment. The memory can be implemented in the form of a read-only memory ROM, a random access memory RAM, a static storage device, and a dynamic storage device. The memory can store an operating system and other applications. When the above method of this embodiment is implemented by software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0053] This embodiment further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above method is implemented.

[0054] Those skilled in the art should understand that the above-mentioned embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented 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. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0055] The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment, it is not intended to limit the present invention. Therefore, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for local dynamic trajectory planning of an unmanned aerial vehicle for power distribution network line inspection, which is used for the unmanned aerial vehicle to perform trajectory search planning in a three-dimensional map constructed for a three-dimensional environment, characterized in that the steps include: Step S01. Obtain the starting position and target position of the controlled UAV and obtain information about obstacles in the environment; Step S02. Calculate the attraction force of the target position on the drone and the repulsion force of the obstacle on the drone according to the distance between the controlled drone and the target position and the obstacle, and synthesize the calculated attraction force and repulsion force to obtain the total force; Step S03. Determine whether the currently controlled drone is trapped in a local minimum value according to the flight state of the controlled drone. If so, proceed to step S04; otherwise, proceed to step S05. Step S04. Recalculate the total force on the current position of the controlled UAV, and obtain the current lateral force direction according to the current total force vector, adjust the magnitude of the current lateral force so that the controlled UAV exits the local minimum, and add the current lateral force to the total force; Step S05. Determine the moving direction and moving distance of the controlled UAV at the next moment according to the currently obtained total force, and update the position of the UAV, and return to step S02 until the controlled UAV reaches the target position or meets the preset termination condition.

2. The method for local dynamic trajectory planning of unmanned aerial vehicles for power distribution network line inspection according to claim 1 is characterized in that: The attraction of the target position to the drone is calculated according to the following formula: in, For current location q The attraction of the place, For current location q The gravitational potential energy at A direction is for the drone to point to the target point, and the size is the current position q With the target point Distance between | q- |Vector.

3. The method for local dynamic trajectory planning of unmanned aerial vehicles for distribution network line inspection according to claim 2 is characterized in that: The repulsive force generated by the obstacle on the drone is calculated according to the following formula: in Indicates from Pointing from The unit vector of It is expressed as: in, k represents the proportionality constant of the repulsive force, Indicates the maximum distance threshold at which obstacles affect the drone.

4. The method for local dynamic trajectory planning of unmanned aerial vehicles for distribution network line inspection according to claim 3 is characterized in that: In step S02, the total force is obtained by synthesizing the calculated attractive force and repulsive force as follows: .

5. The method for local dynamic trajectory planning of unmanned aerial vehicles for distribution network line inspection according to claim 1 is characterized in that: The step S01 also includes configuring the proportional constants of the attractive force and the repulsive force. and to control the strength of the attractive and repulsive forces respectively.

6. The method for local dynamic trajectory planning of an unmanned aerial vehicle for distribution network 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 in an area smaller than a preset size range, it is determined that the drone is trapped in a local minimum.

7. The method for local dynamic trajectory planning of an unmanned aerial vehicle for distribution network line inspection according to any one of claims 1 to 5, characterized in that: In step S04, the current lateral force direction is obtained according to the current total force vector, and the magnitude and direction of the current lateral force are adjusted so that the controlled UAV exits the local minimum value, including: Rotate the current total force vector 90 degrees to get the lateral force direction; The current lateral force is adjusted according to the current attractive force until the controlled drone exits the local minimum.

8. The method for local dynamic trajectory planning of unmanned aerial vehicles for power distribution network line inspection according to claim 7 is characterized in that: According to the current attractive force, adjust the current lateral force as follows: in, is the coefficient that adjusts the magnitude of the lateral force, represents the adjusted lateral force vector, represents the direction vector of the lateral force, By attraction vector Rotate 90 degrees to get.

9. An electronic device comprising a processor and a memory, wherein the memory is used to store a computer program, wherein: The processor is configured to execute the computer program to perform the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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