Method and system for automatic return of a photovoltaic panel detection robot to a breakpoint

Abstract

This invention discloses an automatic return method and system for a photovoltaic panel inspection robot from an interruption point, relating to the field of photovoltaic inspection. The method includes the following steps: at the interruption point A, recording the robot's current real-time dynamic positioning (RTK) coordinates, including X and Y coordinates and heading angle; at the robot's current position B after completing the interrupted task, acquiring the robot's current RTK coordinates, including X and Y coordinates and heading angle; calculating the robot's motion control parameters for returning from point B to point A based on the coordinates of points A and B, including the target heading angle and distance; and performing path planning based on the motion control parameters to automatically return to point A. Beneficial effects: The kinematic sampling path planning algorithm achieves a simple and efficient planning method, enabling rapid return to the interruption point and its posture, providing a continuous trajectory for subsequent photovoltaic inspection tasks, and providing a reliable trajectory reference for photovoltaic inspection operations.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic testing, and in particular to an automatic return method and system for photovoltaic panel testing robots at interruption points. Background Technology

[0002] Photovoltaic panel inspection robots are core equipment for the operation and maintenance of photovoltaic power plants. They can autonomously complete the inspection and testing of photovoltaic panels, significantly improving the efficiency and intelligence level of photovoltaic power plant operation and maintenance. In actual operation, the robot needs to frequently interrupt its current inspection task due to reasons such as battery depletion and equipment failure repair, and return to a designated area to complete battery replacement and equipment maintenance. After the operation resumes, it needs to accurately return to the previous interruption point to continue the inspection work. The accurate and rapid return to the interruption point is a key technical requirement to ensure the continuity and integrity of photovoltaic panel inspection operations.

[0003] Currently, the point-to-point return technology for mobile robots is mostly applied in scenarios such as indoor sweeping robots. The mainstream implementation methods are mainly divided into two categories: the first is positioning combined with path planning algorithms based on LiDAR mapping. This method can achieve obstacle avoidance and accurate positioning, but it requires the pre-construction of a high-precision environmental map. In outdoor open environments with large scene areas, such as photovoltaic power stations, the large amount of map data can easily cause the robot controller's memory to overload, and the map update and maintenance costs are high, with poor adaptability. The second is positioning combined with path planning algorithms based on ultra-wideband (UWB). This method is simple to operate and has low deployment difficulty, and the indoor positioning effect is good. However, the positioning accuracy in outdoor scenarios is highly dependent on the number of tag signal towers deployed. Photovoltaic power stations have a wide operating range, and large-scale deployment of signal towers will lead to a sharp increase in equipment costs. Moreover, the signal is easily interfered with by the outdoor environment, making it difficult to meet the actual operating needs of photovoltaic panel inspection robots.

[0004] Given the open outdoor operating environment of photovoltaic power plants, existing point-to-point return technologies all suffer from significant adaptability deficiencies. They either rely on high-precision maps, leading to high memory and maintenance costs, or suffer from high deployment costs and insufficient positioning stability due to outdoor signal limitations. Currently, there is no dedicated automatic return technology solution for photovoltaic panel inspection robots that is specifically adapted to these scenarios. Therefore, there is an urgent need to develop a method for photovoltaic panel inspection robots to automatically return to their operational interruption points, which is not limited by outdoor site conditions, does not rely on high-precision maps, and has low deployment and implementation costs. This would address the pain points of existing technologies in photovoltaic panel inspection robot applications and meet the actual technical needs of photovoltaic power plant operation and maintenance. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to solve the technical problems in the prior art and provide an automatic return method and system for photovoltaic panel inspection robots at interruption points.

[0006] Technical solution: This application proposes an automatic return method for photovoltaic panel inspection robots from interruption points, including the following steps:

[0007] At the interruption point A, record the robot's current real-time dynamic positioning (RTK) coordinate information, including X and Y coordinates and heading angle;

[0008] At the robot's current position B after completing the interrupted task, obtain the robot's current RTK coordinate information, including X and Y coordinates and heading angle;

[0009] Based on the coordinate information of points A and B, calculate the motion control parameters for the robot to return from point B to point A, including the target heading angle and distance;

[0010] The robot performs path planning based on the motion control parameters and automatically returns to point A; the path planning adopts a trajectory sampling method based on a kinematic model.

[0011] Based on the heading angle information at point A, the robot adjusts its attitude and returns to the interruption point of the operation.

[0012] Preferably, based on the coordinate information of points A and B, the motion control parameters for the robot to return from point B to point A are calculated, including:

[0013] Calculate the relative pose angle based on the coordinates of the interruption point A and the robot's current position B;

[0014] Set the relevant parameters for motion planning based on the distance between the breakpoint A and the robot's current position B.

[0015] Preferably, the relative pose angle is calculated based on the coordinates of the interruption point A and the robot's current position B, including:

[0016] The target heading angle is calculated using the following formula:

[0017] ;

[0018] in, Let B be the ordinate of point B. Let B be the x-coordinate. Let A be the ordinate of point A. Let A be the x-coordinate of point A.

[0019] Preferably, the motion planning parameters are set based on the distance between the interruption point A and the robot's current position B, including:

[0020] Set motion planning parameters, including the linear velocity range. Maximum angular velocity Maximum acceleration Maximum angular acceleration Angular velocity resolution Speed ​​resolution Planning time step The predicted time T and the robot's radius;

[0021] Based on the current linear velocity and angular velocity Generate a candidate set of linear velocities under acceleration constraints and angular velocity candidate set :

[0022] Linear velocity candidate set The minimum value of the range is v- * The maximum value is v+ * ,and The range meets the linear velocity range ;

[0023] Angular velocity candidate set The minimum value of the range is w- * The maximum value is w+ * ,and Satisfy maximum angular acceleration .

[0024] Then based on linear velocity resolution and angular velocity resolution Generate candidate values ​​for linear velocity and angular velocity, respectively:

[0025] [v1,v2,…..], [w1,w2,…].

[0026] Preferably, the robot performs path planning based on the motion control parameters and automatically returns to point A; wherein the path planning employs a trajectory sampling method based on a kinematic model, including:

[0027] By substituting the candidate values ​​of linear velocity [v1, v2, ...] and angular velocity [w1, w2, ...] into the following formula, the samples of multiple trajectories are calculated. The formula is as follows:

[0028] x += v1 * cos(yaw) * d t ;

[0029] y += v1*sin(yaw)*d t ;

[0030] Yaw += w1 * dt;

[0031] Where x / y are the robot's current X / Y axis coordinates, v1 is the candidate linear velocity value, yaw is the robot's current heading angle, and d t The time step for motion planning is w1, the candidate angular velocity value is w1, and the updated heading angle of the robot is Yaw.

[0032] Multiple trajectories are filtered, and the optimal path is selected for execution.

[0033] Preferably, multiple trajectories are filtered to select the optimal path for execution, including:

[0034] The trajectory is selected based on the distance from the coordinates of the ends of multiple sampled trajectories to point A. Then, the linear velocity and angular velocity of that trajectory are calculated.

[0035] The preset control cycle is used to repeat the above calculation in each control cycle until the distance from point A is less than the robot radius (radius).

[0036] Preferably, the robot adjusts its attitude based on the heading angle information at point A to return to the interruption point of the operation, including:

[0037] Based on the heading angle Ayaw at the interruption point A, the difference between the robot's current heading angle and Ayaw is calculated to obtain the rotation angle to be obtained, and the robot is controlled to complete the in-situ turn at that angle.

[0038] Secondly, embodiments of the present invention provide an automatic return system for interrupted points of a photovoltaic panel inspection robot, comprising:

[0039] The acquisition unit is used to record the robot's current real-time dynamic positioning (RTK) coordinate information, including X and Y coordinates and heading angle, at the work interruption point A; and to acquire the robot's current RTK coordinate information, including X and Y coordinates and heading angle, at the robot's current position B after completing the interrupted work.

[0040] The calculation unit is used to calculate the motion control parameters of the robot returning from point B to point A based on the coordinate information of points A and B, including the target heading angle and distance.

[0041] The planning unit is used by the robot to plan a path based on the motion control parameters and automatically return to point A; wherein the path planning adopts a trajectory sampling method based on a kinematic model;

[0042] The adjustment unit is used by the robot to adjust its own attitude based on the heading angle information at point A, so as to complete the return to the interruption point of the operation.

[0043] Thirdly, embodiments of the present invention provide an electronic device, including a processor and a memory. The memory stores one or more computer programs; when the one or more computer programs stored in the memory are executed by the processor, the electronic device is able to implement any of the possible design methods described in the first aspect.

[0044] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any of the above embodiments.

[0045] Fifthly, embodiments of the present invention also provide a computer program product that, when run on an electronic device, causes the electronic device to perform any possible design method of any of the above aspects.

[0046] Beneficial effects: This invention adopts a relative positioning method, which eliminates the need to build a high-precision environmental map in advance. It only extracts the x, y, and yaw three-dimensional information from the RTK positioning signal to complete the positioning. This completely solves the problem of controller memory overload caused by the large amount of map data in open outdoor scenes of LiDAR mapping technology. Moreover, it is not limited by the scope of the photovoltaic power station operation site and can be adapted to various photovoltaic maintenance scenarios with different altitudes and different photovoltaic panel deployment angles, greatly improving environmental adaptability.

[0047] This invention eliminates the need for auxiliary positioning equipment such as UWB tag signal towers. It achieves positioning and motion planning solely through RTK positioning signals combined with the robot's own controller, avoiding the problem of drastically increased costs caused by large-scale deployment of signal towers in outdoor environments. Furthermore, the overall technical solution eliminates the need for complex pre-deployment debugging and post-deployment maintenance, simplifying the deployment process and significantly reducing the difficulty of on-site implementation.

[0048] The motion planning of this invention is designed based on the open and unobstructed operation characteristics of photovoltaic panel inspection robots. It achieves precise motion through a simple combination of turning + straight line + turning actions. The trajectory planning algorithm selects the optimal trajectory through periodic sampling. The calculation logic is simple and efficient, with low dependence on the computing performance of the robot controller. It does not require high-performance computing hardware and can be directly deployed and used on microcontrollers, effectively controlling hardware costs while ensuring the real-time execution of the algorithm.

[0049] The path planning algorithm based on kinematic sampling achieves a simple and efficient planning method, which can quickly return to the operation breakpoint and breakpoint posture, providing a continuous trajectory for subsequent photovoltaic inspection operations and providing a reliable trajectory reference for photovoltaic inspection operations.

[0050] This invention achieves precise orientation of the robot to the interruption point by calculating the relative pose in real time, combines multi-trajectory sampling to select the optimal motion parameters, and dynamically updates the planning strategy in each control cycle until the robot reaches the preset range of the interruption point. Finally, it can also accurately match the heading angle of the interruption point to achieve accurate pose regression of the operation interruption point, avoid secondary detection or operation omissions caused by return deviation, and ensure the continuity and integrity of photovoltaic panel inspection operations.

[0051] The positioning and motion planning process of this invention does not require complex map matching or signal interaction. The positioning signal is received in real time, and the motion planning parameters are dynamically adjusted according to the relative distance. The robot can respond quickly and perform a return action, which greatly shortens the interval between the completion of equipment maintenance and the resumption of work, and improves the overall efficiency of photovoltaic panel inspection. Attached Figure Description

[0052] Figure 1 A schematic diagram of the method framework for this invention is provided;

[0053] Figure 2 This invention provides a schematic diagram based on the ends of multiple sampling trajectories;

[0054] Figure 3 The present invention provides an effect diagram of implementing the method through multiple cycles;

[0055] Figure 4 This is a block diagram of a device structure provided in one embodiment of this application;

[0056] Figure 5 This is a block diagram of an electronic device structure provided in one embodiment of this application. Detailed Implementation

[0057] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] Example 1

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "including" and similar expressions used herein mean that the element or object preceding the term covers the element or object listed after the term and its equivalents, but do not exclude other elements or objects.

[0060] In response to the problems existing in the current technology, such as Figure 1-3 As shown, an automatic return method for photovoltaic panel inspection robot at interruption points is proposed, including the following steps:

[0061] S1. At the work interruption point A, record the robot's current real-time dynamic positioning (RTK) coordinate information, including X and Y coordinates and heading angle; when the robot needs to interrupt the work due to battery depletion, at the work interruption point A, record the current RTK coordinate information through the robot's RTK positioning module, including the x-coordinate of point A. A y-axis A and heading angle A yaw And store this information in the robot controller;

[0062] S2. At the robot's current position B after completing the interrupted task, obtain the robot's current RTK coordinate information, including X and Y coordinates and heading angle; after the robot returns to the designated maintenance area to complete the battery replacement, at the current position B, obtain the robot's real-time RTK coordinate information through the RTK positioning module, including the x-coordinate of point B. B y-axis B and heading angle B yaw And transmit it to the controller;

[0063] Based on RTK positioning, the robot controller receives RKT positioning signals via a communication protocol. These signals contain information in six dimensions: X, Y, Z, roll, pitch, and yaw. Because the work environment is constantly changing, and the altitude of the photovoltaic maintenance site and the angle of the photovoltaic panels vary, this invention only implements relative positioning rather than absolute positioning. That is, a coordinate system is established in the current scene, and relative positioning is performed through coordinate transformation. Because relative positioning is performed, only x, y, and yaw are used to implement the RTK-based positioning function of this invention.

[0064] S3. Based on the coordinate information of points A and B, calculate the motion control parameters for the robot to return from point B to point A, including the target heading angle and distance;

[0065] Based on the coordinate information of points A and B, calculate the motion control parameters for the robot to return from point B to point A, including:

[0066] The relative pose angle is calculated based on the coordinates of the interruption point A and the robot's current position B; after the angle is calculated, the robot immediately adjusts its forward direction to align with the direction of the interruption point A.

[0067] Set the relevant parameters for motion planning based on the distance between the breakpoint A and the robot's current position B.

[0068] Calculate the relative pose angle based on the coordinates of the interruption point A and the robot's current position B, including:

[0069] The target heading angle is calculated using the following formula:

[0070] ;

[0071] in, Let B be the ordinate of point B. Let B be the x-coordinate. Let A be the ordinate of point A. Let A be the x-coordinate of point A.

[0072] Set the relevant parameters for motion planning based on the distance between the breakpoint A and the robot's current position B, including:

[0073] Set motion planning parameters, including the linear velocity range. Maximum angular velocity Maximum acceleration Maximum angular acceleration Angular velocity resolution Speed ​​resolution Planning time step The predicted time T and the robot's radius;

[0074] Based on the current linear velocity and angular velocity Generate a candidate set of linear velocities under acceleration constraints and angular velocity candidate set :

[0075] Linear velocity candidate set The minimum value of the range is v- * The maximum value is v+ * ,and The range meets the linear velocity range ;

[0076] Angular velocity candidate set The minimum value of the range is w- * The maximum value is w+ * ,and Satisfy maximum angular acceleration .

[0077] Then based on linear velocity resolution and angular velocity resolution Generate candidate values ​​for linear velocity and angular velocity, respectively:

[0078] [v1,v2,......], [w1,w2,......].

[0079] First, the relative pose angle of point B toward point A is calculated using a formula, which is the target heading angle. The controller then controls the robot to rotate in place to the target heading angle to complete the orientation calibration.

[0080] Based on the actual relative distance between points A and B, set motion planning parameters, such as: linear velocity range [0.1m / s, 0.5m / s], maximum angular velocity 1rad / s, maximum acceleration 0.2m / s², maximum angular acceleration 0.5rad / s², angular velocity resolution 0.1rad / s, velocity resolution 0.05m / s, planning time step 0.1s, prediction time 2s, and robot radius 0.3m. Based on the robot's current linear velocity of 0m / s and angular velocity of 0rad / s, calculate the candidate set range for linear velocity as [0, 0.02], and correct it to [0.1, 0.02] (taking the effective value as 0.1m / s) based on the linear velocity range limit. The candidate set range for angular velocity is [0, 0.05], and the maximum angular velocity limit remains unchanged. Then, generate a candidate value list for linear velocity [0.1] and a candidate value list for angular velocity [0, 0.05, 0.1] based on the resolution.

[0081] S4. The robot performs path planning based on the motion control parameters and automatically returns to point A; wherein the path planning adopts a trajectory sampling method based on a kinematic model;

[0082] The robot performs path planning based on the motion control parameters and automatically returns to point A; the path planning employs a trajectory sampling method based on a kinematic model, including:

[0083] By substituting the candidate values ​​of linear velocity [v1, v2, ...] and angular velocity [w1, w2, ...] into the following formula, the samples of multiple trajectories are calculated. The formula is as follows:

[0084] x += v1 * cos(yaw) * d t ;

[0085] y += v1*sin(yaw)*d t ;

[0086] Yaw += w1 * dt;

[0087] Where x / y are the robot's current X / Y axis coordinates, v1 is the candidate linear velocity value, yaw is the robot's current heading angle, and d t The time step for motion planning is w1, the candidate angular velocity value is w1, and the updated heading angle of the robot is Yaw.

[0088] Multiple trajectories are filtered, and the optimal path is selected for execution.

[0089] Filter through multiple trajectories and select the optimal path for execution, including:

[0090] The trajectory is selected based on the distance from the coordinates of the ends of multiple sampled trajectories to point A. Then, the linear velocity and angular velocity of that trajectory are calculated.

[0091] The preset control cycle is used to repeat the above calculation in each control cycle until the distance from point A is less than the robot radius (radius).

[0092] Combination Figure 2 The robot can be represented by a box, and the arc in front of the robot can represent the sampling of the trajectory. The optimal trajectory is selected based on the distance from the end of the trajectory to point A, and the robot can eventually reach point A.

[0093] The distance between A and B can be divided into multiple segments, each segment can be set as a period, and the same method described above can be performed in each period to reduce errors.

[0094] The candidate values ​​of linear velocity and angular velocity are combined in pairs and substituted into the trajectory sampling formula to obtain multiple sets of predicted trajectories. The distance from the end of each trajectory to point A is calculated. For example, the linear velocity of the closest trajectory (0.1 m / s) and the angular velocity of the closest trajectory (0.05 rad / s) are selected as control parameters, and the robot starts moving by executing these parameters. In each 0.1 s control cycle, the controller repeats the above steps of generating candidate sets of linear velocity and angular velocity, sampling trajectory, and filtering, and dynamically updates the motion control parameters until the straight-line distance between the robot and point A is less than 0.3 m, at which point the robot stops moving.

[0095] Considering the inherent advantages of photovoltaic robots—such as a relatively open working area with no obstacles—and the fact that cleaning robots utilize a differential motion structure and a specialized bow-shaped path, their motion planning can be divided into two types: forward / backward straight movement and in-situ turning left / right. To achieve point-to-point motion planning, this invention combines turning, straight-line movement, and more turning actions, along with a specific algorithm to guide the robot's movements, thereby accurately reaching the work interruption point. (See reference...) Figure 1 .

[0096] S5. Based on the heading angle information at point A, the robot adjusts its own attitude and completes the return to the interruption point of the operation.

[0097] Based on the heading angle information at point A, the robot adjusts its attitude and returns to the interruption point of the operation, including:

[0098] Based on the heading angle A of the work interruption point A yaw Calculate the robot's current heading angle and A yaw The difference is used to obtain the rotation angle, and the robot is controlled to complete the in-situ turn at that angle.

[0099] For example, the controller reads the stored heading angle A at point A. yaw Calculate the current robot heading angle and A yaw The difference is 5°. Control the robot to rotate 5° to the left in place to complete the heading angle matching. At this point, the robot can accurately return to the interruption point A and immediately resume the photovoltaic panel inspection operation.

[0100] In this embodiment, the robot achieves precise return from the inspection position to the work interruption point through the above method. The return position error is less than 0.01m, the heading angle matching error is less than 0.05°, the whole return process is short and does not require manual intervention, effectively ensuring the continuity of photovoltaic panel inspection work. Moreover, the method runs stably on the microcontroller without problems such as memory overload or calculation lag, and is suitable for the outdoor operation requirements of photovoltaic power stations.

[0101] Secondly, combining Figure 4 This invention provides an automatic return system for photovoltaic panel inspection robots that has experienced interruptions, comprising:

[0102] The acquisition unit 201 is used to record the robot's current real-time dynamic positioning (RTK) coordinate information, including X and Y coordinates and heading angle, at the work interruption point A; and to acquire the robot's current RTK coordinate information, including X and Y coordinates and heading angle, at the robot's current position B after completing the interrupted work.

[0103] The calculation unit 202 is used to calculate the motion control parameters of the robot returning from point B to point A based on the coordinate information of point A and point B, including the target heading angle and distance.

[0104] The planning unit 203 is used for the robot to plan a path based on the motion control parameters and automatically return to point A; wherein the path planning adopts a trajectory sampling method based on a kinematic model;

[0105] The adjustment unit 204 is used for the robot to adjust its own attitude based on the heading angle information at point A, so as to complete the return to the interruption point of the operation.

[0106] In other embodiments of the present invention, an electronic device 400 is disclosed, such as... Figure 5 As shown, the electronic device may include: one or more processors 401; a memory 402; a display 403; one or more application programs (not shown); and one or more computer programs 404. These devices can be connected via one or more communication buses 405. The one or more computer programs 404 are stored in the memory 402 and configured to be executed by the one or more processors 401. The one or more computer programs 404 include instructions that can be used to perform actions such as... Figure 1 And the steps in the corresponding embodiments.

[0107] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0108] In the various embodiments of this invention, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as flash memory, portable hard disk, read-only memory, random access memory, magnetic disk, or optical disk.

[0110] The above description is merely a specific implementation of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of the present invention should be covered within the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention should be determined by the protection scope of the claims.

Claims

1. An automatic return method for a photovoltaic panel inspection robot from an interruption point, characterized in that, Includes the following steps: At the interruption point A, record the robot's current real-time dynamic positioning (RTK) coordinate information, including X and Y coordinates and heading angle; At the robot's current position B after completing the interrupted task, obtain the robot's current RTK coordinate information, including X and Y coordinates and heading angle; Based on the coordinate information of points A and B, calculate the motion control parameters for the robot to return from point B to point A, including the target heading angle and distance; The robot performs path planning based on the motion control parameters and automatically returns to point A; the path planning adopts a trajectory sampling method based on a kinematic model. Based on the heading angle information at point A, the robot adjusts its attitude and returns to the interruption point of the operation.

2. The automatic return method for a photovoltaic panel inspection robot at an interruption point according to claim 1, characterized in that, Based on the coordinate information of points A and B, calculate the motion control parameters for the robot to return from point B to point A, including: Calculate the relative pose angle based on the coordinates of the interruption point A and the robot's current position B; Set the relevant parameters for motion planning based on the distance between the breakpoint A and the robot's current position B.

3. The automatic return method for a photovoltaic panel inspection robot at an interruption point according to claim 1, characterized in that, Calculate the relative pose angle based on the coordinates of the interruption point A and the robot's current position B, including: The target heading angle is calculated using the following formula: ； in, Let B be the ordinate of point B. Let B be the x-coordinate. Let A be the ordinate of point A. Let A be the x-coordinate of point A.

4. The automatic return method for a photovoltaic panel inspection robot at an interruption point according to claim 2, characterized in that, Set the relevant parameters for motion planning based on the distance between the breakpoint A and the robot's current position B, including: Set motion planning parameters, including the linear velocity range. Maximum angular velocity Maximum acceleration Maximum angular acceleration Angular velocity resolution Speed ​​resolution Planning time step The predicted time T and the robot's radius (radius) are also factors. Based on the current linear velocity and angular velocity Generate a candidate set of linear velocities under acceleration constraints and angular velocity candidate set : Linear velocity candidate set The minimum value of the range is v- * The maximum value is v+ * ,and The range satisfies the linear velocity range ; Angular velocity candidate set The minimum value of the range is w- * The maximum value is w+ * ,and Satisfying maximum angular acceleration . Then based on linear velocity resolution and angular velocity resolution Generate candidate values ​​for linear velocity and angular velocity, respectively: [v1,v2,......], [w1,w2,......].

5. The automatic return method for a photovoltaic panel inspection robot at an interruption point according to claim 4, characterized in that, The robot performs path planning based on the motion control parameters and automatically returns to point A; the path planning employs a trajectory sampling method based on a kinematic model, including: By substituting the candidate values ​​of linear velocity [v1, v2, ...] and angular velocity [w1, w2, ...] into the following formula, the samples of multiple trajectories are calculated. The formula is as follows: x+=v1*cos(yaw)*d t ; y + = v1 * sin(yaw) * d t ; Yaw += w1 * dt; Where x / y are the robot's current X / Y axis coordinates, v1 is the candidate linear velocity value, yaw is the robot's current heading angle, and d t The time step for motion planning is w1, the candidate angular velocity value is w1, and the updated heading angle of the robot is Yaw. Multiple trajectories are filtered, and the optimal path is selected for execution.

6. The automatic return method for a photovoltaic panel inspection robot at an interruption point according to claim 5, characterized in that, Filter through multiple trajectories and select the optimal path for execution, including: The trajectory is selected based on the distance from the coordinates of the ends of multiple sampled trajectories to point A. Then, the linear velocity and angular velocity of that trajectory are calculated. The preset control cycle is used to repeat the above calculation in each control cycle until the distance from point A is less than the robot radius (radius).

7. The automatic return method for a photovoltaic panel inspection robot at an interruption point according to claim 1, characterized in that, Based on the heading angle information at point A, the robot adjusts its attitude and returns to the interruption point of the operation, including: Based on the heading angle Ayaw at the interruption point A, the difference between the robot's current heading angle and Ayaw is calculated to obtain the rotation angle to be obtained, and the robot is controlled to complete the in-situ turn at that angle.

8. An automatic return system for a photovoltaic panel inspection robot at an interruption point, characterized in that, include: The acquisition unit is used to record the robot's current real-time dynamic positioning (RTK) coordinate information, including X and Y coordinates and heading angle, at the operation interruption point A. At the robot's current position B after completing the interrupted task, obtain the robot's current RTK coordinate information, including X and Y coordinates and heading angle; The calculation unit is used to calculate the motion control parameters of the robot returning from point B to point A based on the coordinate information of points A and B, including the target heading angle and distance. The planning unit is used by the robot to plan a path based on the motion control parameters and automatically return to point A; wherein the path planning adopts a trajectory sampling method based on a kinematic model; The adjustment unit is used by the robot to adjust its own attitude based on the heading angle information at point A, so as to complete the return to the interruption point of the operation.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can run on the processor, and when the computer program is executed by the processor, causes the processor to implement the method as described in any one of claims 1 to 7.

10. 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 7.

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