A Smart Processing Method for Adaptive Grid Industrial Robot Chargers

Abstract

This application provides an intelligent processing method for an adaptive power grid industrial robot charger, relating to the field of robotics technology. It addresses the problem of high power consumption caused by the need for multiple adjustments when the robot approaches the charger in existing solutions. The method involves acquiring the charger's installation location, its model data, and the power grid industrial robot's model data. Based on these data, the method obtains the robot's pose at the alignment point between itself and the charger, and also acquires the pose of the charging start point. A charging path is generated based on the starting point pose and the robot's pose at the alignment point. This charging path is then sent to the robot, enabling it to autonomously walk to the charger for charging. Since the robot is already aligned with the charger when at the alignment point, this method reduces the number of posture adjustments and power loss.

Description

Technical Field

[0001] This application relates to the field of robotics technology, and in particular to an intelligent processing method for an adaptive power grid industrial robot charger. Background Technology

[0002] In the automated operation and maintenance system of smart grids, power grid industrial robots undertake important tasks such as daily inspection, repair, and self-maintenance. After completing their assigned tasks, power grid industrial robots need to autonomously return to their chargers for energy replenishment.

[0003] When returning to the charger for charging, the power grid industrial robot first obtains the charger's location, then automatically navigates to the vicinity of the charger, and adjusts its position to align its charging device with the charger. However, the constant adjustment of its posture and position during alignment consumes a significant amount of power, reducing the charging success rate. Summary of the Invention

[0004] This application provides an intelligent processing method for an adaptive power grid industrial robot charger, which reduces the power consumption of the power grid industrial robot when it walks to the charger for charging and improves the charging success rate.

[0005] This application provides an intelligent processing method for an adaptive power grid industrial robot charger. The intelligent processing method is applied to a backend server, which communicates with the charger and the power grid industrial robot. The intelligent processing method includes:

[0006] Obtain the installation location of the charger, the model data of the charger, and the model data of the power grid industrial robot;

[0007] The pose of the power grid industrial robot at the alignment point where it is aligned with the charger is obtained based on the charger's installation location, the charger's model data, and the power grid industrial robot's model data.

[0008] Obtain the pose of the charging start point of the power grid industrial robot, and generate a charging path based on the pose of the charging start point and the pose of the power grid industrial robot at the alignment point.

[0009] Send a charging path to the power grid industrial robot, enabling the robot to autonomously walk to the charger for charging.

[0010] In the above technical solution, after deploying the power grid industrial robot and charger in the power area, the pose of the alignment point of the power grid industrial robot when aligning with the charger is determined based on the installation location of the charger, the model data of the power grid industrial robot, and the model data of the charger. Then, a charging path is generated based on the charging start point of the power grid industrial robot and the pose of the power grid industrial robot at the alignment point. When the power grid industrial robot travels to the alignment point according to the charging path, the power grid industrial robot is already aligned with the charger. It only needs to move straight to realize the docking and charging of the charging module of the power grid industrial robot and the charging contact of the charger. This reduces the number of times the power grid industrial robot needs to adjust its posture to achieve alignment with the charger, reduces power loss, and improves the charging success rate.

[0011] In one possible implementation, the charger is provided with a mounting point for mounting the charger, a first marker point for alignment with the power grid industrial robot, and a second marker point for alignment with the charger on the power grid industrial robot. The mounting position of the charger includes the position coordinates of the mounting point in the world coordinate system.

[0012] Accordingly, based on the charger's installation location, the charger's model data, and the power grid industrial robot's model data, the pose of the power grid industrial robot at the alignment point where it aligns with the charger is obtained, specifically including:

[0013] Extract the position coordinates of the mounting point in the model coordinate system from the charger model data; generate the first coordinate transformation matrix based on the position coordinates of the mounting point in the world coordinate system and the position coordinates of the mounting point in the model coordinate system;

[0014] Extract the position coordinates of the first marker point in the model coordinate system from the charger model data, and use the first coordinate transformation matrix to transform the position coordinates of the first marker point in the model coordinate system to obtain the position coordinates of the first marker point in the world coordinate system.

[0015] Based on the model data of the power grid industrial robot, the position coordinates of the first marker point in the world coordinate system are translated to obtain the position coordinates of the second marker point in the world coordinate system.

[0016] Based on the model data of the power grid industrial robot and the position coordinates of the second marker point in the world coordinate system, the pose of the power grid industrial robot at the alignment point is obtained.

[0017] In the above technical solution, marker points are set on the power grid industrial robot and the charger. The position data of the first marker point is translated using the model data of the power grid industrial robot, and then transformed into different coordinate systems to obtain the pose of the power grid industrial robot at the alignment point. This ensures that the pose of the power grid industrial robot at the alignment point aligns the first and second marker points with each other, and that the charger and the power grid industrial robot do not interfere with each other. Thus, the power grid industrial robot is in the alignment point pose when it travels to the alignment point according to the charging path. Compared with the existing solution, which first travels to the vicinity of the charger and then makes multiple posture adjustments to the power grid industrial robot through multiple signal interactions with the charger, this solution does not require multiple interactions with the charger, thereby reducing the number of posture adjustments of the power grid industrial robot and improving the charging accuracy.

[0018] In one possible implementation, the position coordinates of the first marker point in the world coordinate system are translated based on the model data of the power grid industrial robot to obtain the position coordinates of the second marker point in the world coordinate system. Specifically, this includes:

[0019] Project the model data of the power grid industrial robot onto a horizontal plane to obtain the projection data of the power grid industrial robot, and obtain the working radius of the power grid industrial robot based on the projection data.

[0020] The normal vector of the reference plane where the first marker point is located is obtained based on the position coordinates of the first marker point in the world coordinate system, and the translation vector is obtained based on the working radius of the power grid industrial robot and the normal vector of the reference plane where the first marker point is located.

[0021] The position coordinates of the first marker point in the world coordinate system are obtained by translating the position coordinates of the first marker point in the world coordinate system using a translation vector.

[0022] In the above technical solution, the dimensions of the power grid industrial robot are obtained based on the model data of the power grid industrial robot. The translation vector is obtained based on the dimensions of the power grid industrial robot and the normal vector of the reference plane where the first marker point is located. In this way, the first marker point can be translated based on the translation vector to obtain the position coordinates of the second marker point in the world coordinate system. The pose of the alignment point is obtained based on the position coordinates of the second marker point in the world coordinate system. When the power grid industrial robot is located at the alignment point, it is ensured that the second marker point and the first marker point are aligned, and the power grid industrial robot and the charger will not interfere with each other.

[0023] In one possible implementation, the pose of the power grid industrial robot at the alignment point is obtained based on the model data of the power grid industrial robot and the position coordinates of the second marker point in the world coordinate system, specifically including:

[0024] Obtain the position coordinates of the second marker point in the model coordinate system from the model data of the power grid industrial robot;

[0025] Based on the position coordinates of the second marker point in the model coordinate system and the position coordinates of the second marker point in the world coordinate system, obtain the second coordinate transformation matrix;

[0026] The coordinate axes of the power grid industrial robot are processed using the second coordinate transformation matrix to obtain the pose of the power grid industrial robot at the alignment point.

[0027] In the above technical solution, the model data of the power grid industrial robot is processed based on the position coordinates of the second marker point in the world coordinate system to obtain the pose of the power grid industrial robot at the alignment point. This ensures that when the power grid industrial robot is at the alignment point and in the pose of the alignment point, the power grid industrial robot and the charger are aligned without the need for multiple adjustments.

[0028] In one possible implementation, obtaining the charging start point of the power grid industrial robot specifically includes:

[0029] The inspection task and initial inspection power of the power grid industrial robot are obtained, and an inspection path is generated based on the inspection task. The inspection path includes the pose of multiple inspection points, the inspection time of each inspection point, and the inspection sensors that need to be activated at each inspection point.

[0030] Using the inspection points in the inspection path as alternative starting points, the remaining power of the power grid industrial robot from each alternative starting point to the alignment point is calculated based on the inspection path, the pose of the power grid industrial robot at the alignment point, and the initial inspection power.

[0031] Based on the remaining battery power of the power grid industrial robot upon reaching the alignment point and the distance from the alignment point to the charging point, a charging starting point is selected from the candidate starting points; the charging point is the location where the power grid industrial robot is charged.

[0032] In existing technical solutions, the industrial robot typically stops performing its task and moves to a charger when its remaining battery power falls below a certain preset threshold. This preset threshold is generated empirically; if it's set too high, the robot needs frequent charging, impacting task efficiency; if it's set too low, there's a risk of the robot running out of power and being unable to continue. The solution described above determines the remaining battery power of the industrial robot after it reaches the alignment point from each candidate starting point based on the inspection task, the robot's initial battery power, and the alignment point's pose. The optimal charging starting point is then selected based on the remaining battery power at the alignment point and the distance from the alignment point to the charging point. This fully utilizes the robot's battery power, improving operational efficiency while reducing the risk of battery depletion.

[0033] In one possible implementation, inspection points along the inspection path are used as candidate starting points. Based on the inspection path, the pose of the power grid industrial robot at the alignment point, and the initial inspection charge, the remaining charge of the power grid industrial robot from each candidate starting point to the alignment point is calculated. Specifically, this includes:

[0034] The remaining power of the power grid industrial robot at each inspection point is calculated based on the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be activated at each inspection point, and the initial inspection power.

[0035] Using the inspection points in the inspection path as alternative starting points, the path planning is performed based on the pose of each inspection point and the pose at the alignment point to obtain the charging path starting from the alternative starting point, and the power consumption of the charging path starting from the alternative starting point is calculated.

[0036] Based on the remaining power of the power grid industrial robot at each inspection point and the power consumption of the charging path from the alternative starting point, calculate the remaining power of the power grid industrial robot from the alternative starting point to the alignment point.

[0037] In the above technical solution, the backend server calculates the remaining power of the power grid industrial robot when it travels to the inspection point based on the inspection task and the initial inspection power. Then, it calculates the remaining power from each inspection point to the alignment point based on the pose of the inspection and the pose of the alignment point. Compared with the power grid industrial robot calculating the remaining power from each inspection point to the alignment point by reading the power stored in the battery management system after each inspection point, predicting the remaining power from each inspection point to the alignment point through the backend can reduce the energy consumed by the power grid industrial robot in calculating the remaining power.

[0038] In one possible implementation, the remaining power of the power grid industrial robot at each inspection point is calculated based on the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be activated at each inspection point, and the initial inspection power. Specifically, this includes:

[0039] The total power consumption of the inspection sensors activated at each inspection point is obtained based on the power consumption per unit time of the inspection sensors activated at each inspection point and the inspection time of each inspection point.

[0040] The single-point driving power consumption of the inspection point is obtained based on the pose of the previous inspection point and the pose of the current inspection point.

[0041] The remaining power of the current inspection point is obtained based on the total power consumption of the inspection sensors activated at the current inspection point, the power consumption of single-point driving, and the remaining power of the previous inspection point; where, if the current inspection point is the first inspection point, the remaining power of the previous inspection point is the initial inspection power.

[0042] The above technical solution takes into account the power consumption of the power grid industrial robot when changing direction, the power consumption when moving in a straight line, and the power consumption when turning on the inspection sensor inspection equipment. It predicts the remaining power at each inspection point, which can improve the accuracy of power prediction and thus improve the charging success rate.

[0043] In one possible implementation, calculating the power consumption of the charging path starting from the alternative starting point specifically includes:

[0044] Based on the poses of two adjacent path points in the charging path starting from the candidate starting point, calculate the difference in yaw angle between the two adjacent path points and the distance between the two adjacent path points.

[0045] The power consumption for turning at a single path point is obtained based on the difference in yaw angle between two adjacent path points, and the power consumption for straight-line travel at a single path point is obtained based on the distance between two adjacent path points.

[0046] The power consumption of the charging path starting from the candidate starting point is obtained by calculating the power consumption of turning at each path point and the power consumption of walking in a straight line at each path point.

[0047] In the above technical solution, the power consumption of the power grid industrial robot in changing direction, in straight-line travel, and in turning on the inspection sensor inspection equipment are all taken into account. The path with the least energy consumption is selected as the charging path. In this way, the power grid industrial robot can allocate more power to the inspection task and improve the efficiency of performing the inspection task.

[0048] In one possible implementation, a charging starting point is selected from candidate starting points based on the remaining battery power of the electric grid industrial robot upon reaching the alignment point and the distance from the alignment point to the charging point. Specifically, this includes:

[0049] Calculate the amount of electricity required for the power grid industrial robot to travel from the alignment point to the charging point based on the distance between the alignment point and the charging point.

[0050] The charging starting point is selected from multiple alternative starting points based on the amount of electricity required for the power grid industrial robot to travel from the alignment point to the charging point and the remaining electricity of the power grid industrial robot from the alternative starting point to the alignment point.

[0051] In the above technical solution, the amount of electricity required for the power grid industrial robot to walk from the alignment point to the charging point is calculated based on the distance from the alignment point to the charging point. This is used as the lower limit of the remaining power. The charging start point is selected when the remaining power is greater than the lower limit. This ensures that the power grid industrial robot will not fail to charge due to low power, thus improving the efficiency of task execution.

[0052] In one possible implementation, a charging starting point is selected from multiple alternative starting points based on the amount of electricity required for the power grid industrial robot to travel from the alignment point to the charging point and the remaining electricity of the power grid industrial robot from the alternative starting point to the alignment point. Specifically, this includes:

[0053] Select the remaining power that is greater than the lower limit value from the remaining power of the grid industrial robot when it travels from the alternative starting point to the alignment point. The lower limit value is the amount of power required for the grid industrial robot to travel from the alignment point to the charging point.

[0054] Choose the candidate starting point with the smallest remaining power from the remaining power that is greater than the lower limit of the power limit as the charging starting point.

[0055] In the above technical solution, based on the amount of electricity required for the power grid industrial robot to walk from the alignment point to the charging point, the candidate starting point corresponding to the smallest remaining electricity value among the remaining electricity values ​​that meet the minimum electricity value requirement can be selected as the charging starting point, which can make full use of the electricity and improve the efficiency of task execution.

[0056] In one possible implementation, the characteristic feature is that obtaining the charging start point of the power grid industrial robot specifically includes:

[0057] The system acquires the inspection task, initial inspection power, and inspection wind information of the power grid industrial robot. Based on the inspection task, it generates an inspection path, which includes the pose of multiple inspection points, the inspection time of each inspection point, and the inspection sensors that need to be activated at each inspection point.

[0058] Using the inspection points in the inspection path as alternative starting points, the remaining power of the power grid industrial robot from each alternative starting point to the alignment point is calculated based on the inspection path, the pose of the power grid industrial robot at the alignment point, the initial inspection power, and the inspection wind information.

[0059] Based on the remaining battery power of the industrial robot upon reaching the alignment point and the distance from the alignment point to the charging point, a charging starting point is selected from the candidate starting points.

[0060] In the above technical solution, the remaining power at each inspection point of the power grid industrial robot during its inspection task, along with the remaining power from each inspection point as a candidate starting point to the alignment point, is used to calculate the remaining power. The optimal charging starting point is then selected based on this remaining power. Therefore, estimating the remaining power is crucial for selecting the optimal charging starting point. Furthermore, considering that the power grid industrial robot primarily operates outdoors, and outdoor weather affects its power consumption, this application incorporates wind information when calculating the remaining power at each inspection point and the remaining power upon reaching the alignment point.

[0061] In one possible implementation, inspection points along the inspection path are used as candidate starting points. Based on the inspection path, the pose of the power grid industrial robot at the alignment point, the initial inspection power, and the inspection wind information, the remaining power of the power grid industrial robot from each candidate starting point to the alignment point is calculated. Specifically, this includes:

[0062] The remaining power of the power grid industrial robot at each inspection point is calculated based on the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be activated at each inspection point, the inspection wind information, and the initial inspection power.

[0063] Based on the pose of each inspection point and the pose at the alignment point, a path planning is performed to obtain a charging path starting from the candidate starting point. The power consumption of the charging path starting from the candidate starting point is calculated based on the inspection wind information.

[0064] Based on the remaining power of the power grid industrial robot at each inspection point and the power consumption of the charging path from the alternative starting point, calculate the remaining power of the power grid industrial robot from the alternative starting point to the alignment point.

[0065] Specifically, the calculation of power consumption along the charging path from the candidate starting point, based on the wind speed information obtained during the inspection, includes:

[0066] The power consumption per unit distance is corrected based on the yaw angle of the previous path point and the inspection wind information to obtain the corrected power consumption per unit distance; the power consumption per unit angle is corrected based on the yaw angle of the previous path point and the inspection wind information to obtain the corrected power consumption per unit angle.

[0067] Based on the poses of two adjacent path points in the charging path starting from the candidate starting point, calculate the difference in yaw angle between the two adjacent path points and the distance between the two adjacent path points.

[0068] The power consumption for turning at a single point is obtained based on the difference in yaw angle between two adjacent path points and the power consumption per unit angle after correction. The power consumption for straight-line driving at a single point is obtained based on the distance between two adjacent path points and the power consumption per unit distance after correction.

[0069] The power consumption of the charging path starting from the candidate starting point is obtained based on the power consumption of turning at each of the path points and the power consumption of driving straight at each of the path points.

[0070] In the above technical solution, for each inspection point on the inspection path, considering the different yaw angles at each inspection point and the different degrees of wind impact on the power grid industrial robot, the power consumption per unit distance and per unit angle is corrected for each inspection point based on the yaw angle, which can improve the accuracy of the remaining power estimation.

[0071] In one possible implementation, after acquiring the charger's installation location, the charger's model data, and the power grid industrial robot's model data, the method further includes:

[0072] Acquire the remaining power of multiple power grid industrial robots at each inspection point and the alternative deployment area of ​​each inspection point when performing inspection tasks.

[0073] The ratio of the number of inspection points with remaining power less than the preset threshold in each candidate deployment area to the total number of inspection points in each candidate deployment area is calculated.

[0074] If the ratio is greater than a preset ratio threshold, the corresponding candidate arrangement area is determined as the target arrangement area, and the target arrangement area is used to arrange the charger.

[0075] In the above technical solution, the remaining power at each inspection point of each power grid industrial robot is collected when performing inspection tasks to obtain the charging demand of the power grid industrial robot when performing tasks. By analyzing the charging demand of each alternative deployment area, it is determined whether to deploy a charger in that area. In this way, multiple chargers can be deployed in the inspection area, and the location of the charger is the area where the power grid industrial robot has the highest probability of running out of power when performing tasks. In this way, the power grid industrial robot can allocate more power for inspection tasks, reduce the power consumed in walking to the charger for charging, and improve task execution efficiency.

[0076] In one possible implementation, the remaining power at each inspection point and the alternative deployment areas of each inspection point are obtained when multiple power grid industrial robots are performing inspection tasks, specifically including:

[0077] The inspection task and the initial inspection power of the power grid industrial robot are obtained, and an inspection path is generated according to the inspection task; wherein, the inspection path includes the pose of multiple inspection points, the inspection time of each inspection point, and the inspection sensors that need to be activated at each inspection point.

[0078] The remaining power at each inspection point is generated based on the inspection path and the initial inspection power.

[0079] In the above technical solution, a backend server can be used to randomly generate inspection tasks, perform path planning based on the inspection tasks, and estimate the remaining power of the power grid industrial robot at each inspection point. In this way, there is no need to obtain the remaining power from the power grid industrial robot, and sufficient data can be generated for the layout of the charger.

[0080] This application provides a backend server, including: a memory and a processor;

[0081] The memory stores the instructions that the computer executes;

[0082] The processor executes computer execution instructions stored in memory, causing the processor to perform the various possible implementations described above.

[0083] This application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the various possible implementations described above.

[0084] This application provides a computer program product, including a computer program that, when executed by a processor, implements the various possible implementations described above. Attached Figure Description

[0085] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0086] Figure 1 A schematic diagram of the architecture of the power grid industrial robot charging system provided in this application;

[0087] Figure 2 This is a schematic diagram of the charger provided in this application;

[0088] Figure 3 A flowchart illustrating the intelligent processing method for the adaptive power grid industrial robot charger provided in this application;

[0089] Figure 4 A schematic diagram illustrating the principle of determining the charging start point provided in this application.

[0090] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0091] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0092] Figure 1 This is a schematic diagram of the architecture of the power grid industrial robot charging system provided in this application, such as... Figure 1 As shown, the charging system includes a charger 300, an electric grid robot 200, and a backend server 100. The backend server 100 communicates with both the charger 300 and the electric grid robot 200. The backend server 100 acquires data from the charger and the electric grid robot, and generates a charging path based on these data. The electric grid robot 200 then travels along the charging path to an alignment point near the charger 300, and its posture at the alignment point aligns its charging module with the charging contacts of the charger 300.

[0093] in, Figure 2 A schematic diagram of the charger provided in this application is shown below. Figure 2 As shown, the charger 300 includes: a charger body 310, a power connector 320, an external power cord connector 330, and a communication cable connector 340. The charger body 310 can employ a sealed die-cast aluminum design to effectively prevent damage to components from water, dust, etc. The communication cable connector 340 allows communication with the vehicle, automatically switching to the appropriate charging curve for different vehicle batteries. It also features historical data storage and retrieval, recording historical charging data and tracking terminal usage habits. Furthermore, it supports Bluetooth communication, transmitting and displaying stored data via Bluetooth. Additionally, the charging current can be set to meet the charging needs of batteries with different capacities.

[0094] Figure 3 The flowchart illustrates an intelligent processing method for an adaptive power grid industrial robot charger provided in this application. The intelligent processing method specifically includes the following steps:

[0095] S101, The backend server obtains the installation location of the charger, the model data of the charger, and the model data of the power grid industrial robot.

[0096] The charger's installation location is defined by its position coordinates in the world coordinate system. After the chargers are deployed in the charging area, their installation locations are extracted from the charger deployment data. Model data for the chargers is extracted from the charger design data, and model data for the power grid industrial robot is extracted from the power grid industrial robot design data.

[0097] S102. The backend server obtains the pose of the power grid industrial robot at the alignment point where it is aligned with the charger based on the charger's installation location, the charger's model data, and the power grid industrial robot's model data.

[0098] The process involves converting the charger's model data to a world coordinate system using the charger's installation location. Then, based on the charger's data in the world coordinate system and the power grid industrial robot's model data, the pose of the alignment point when the power grid industrial robot aligns with the charger is calculated. When the power grid industrial robot is at the alignment point, the charging module of the power grid industrial robot and the charging contacts of the charger are aligned. The power grid industrial robot only needs to continue moving straight in its current posture; no adjustment to the robot's posture is required to achieve docking between the charging module of the power grid industrial robot and the charging contacts of the charger.

[0099] S103. The backend server obtains the pose of the charging start point of the power grid industrial robot and generates a charging path based on the pose of the charging start point and the pose of the power grid industrial robot at the alignment point.

[0100] Specifically, when the power grid industrial robot meets the charging requirements, the position of the power grid industrial robot is used as the charging starting point of the power grid industrial robot. The pose of the charging starting point of the power grid industrial robot is obtained, and the path planning method is used to generate a charging path based on the pose of the charging starting point of the power grid industrial robot and the pose of the power grid industrial robot at the alignment point.

[0101] The charging path includes the position and yaw angle of the industrial robot at each path point. For example, the charging path includes the position and yaw angle of path point A, the position and yaw angle of path point B, the position and yaw angle of path point C, and the position and yaw angle of path point D.

[0102] S104. The backend server sends the charging path to the power grid industrial robot.

[0103] S105, the power grid industrial robot autonomously walks to the charger to charge according to the charging path.

[0104] After receiving the charging path from the backend server, the power grid industrial robot obtains the transformation matrix between the world coordinate system and the navigation coordinate system of the power grid industrial robot. It then uses the transformation matrix to transform the position and yaw angle of each path point in the charging path to obtain the position and yaw angle of each path point in the navigation coordinate system.

[0105] The robot autonomously travels to the alignment point based on the position and yaw angle of each path point in the navigation coordinate system. When the robot is in the alignment point, its charging module aligns with the charging contacts of the charger. Then, it continues to travel straight to the charging point, where its charging module connects with the charging contacts of the charger, and the charger charges the robot.

[0106] In the above technical solution, after deploying the power grid industrial robot and charger in the power area, the pose of the alignment point of the power grid industrial robot when aligning with the charger is determined based on the installation location of the charger, the model data of the power grid industrial robot, and the model data of the charger. Then, a charging path is generated based on the charging start point of the power grid industrial robot and the pose of the power grid industrial robot at the alignment point. When the power grid industrial robot travels to the alignment point according to the charging path, the power grid industrial robot is already aligned with the charger. It only needs to move straight to realize the docking and charging of the charging module of the power grid industrial robot and the charging contact of the charger. This reduces the number of times the power grid industrial robot needs to adjust its posture to achieve alignment with the charger, reduces power loss, and improves the charging success rate.

[0107] In one possible implementation, S102, the backend server obtains the pose of the power grid industrial robot at the alignment point where it is aligned with the charger, based on the charger's installation location, the charger's model data, and the power grid industrial robot's model data. Specifically, this includes:

[0108] S201. The backend server extracts the position coordinates of the installation point in the model coordinate system from the charger's model data; and generates the first coordinate transformation matrix based on the position coordinates of the installation point in the world coordinate system and the position coordinates of the installation point in the model coordinate system.

[0109] The charger includes three mounting points. The mounting position of the charger includes the position of the mounting point in the world coordinate system. The position of the mounting point in the model coordinate system is extracted from the model data of the charger. The first coordinate transformation matrix is ​​generated based on the position of the mounting point in the world coordinate system and the position in the model coordinate system.

[0110] The first coordinate transformation matrix includes a first translation matrix and a first rotation matrix. The first rotation matrix is ​​obtained based on the position coordinates of the installation point in the model coordinate system and the position coordinates of the installation point in the world coordinate system. Then, the first translation matrix is ​​obtained based on the position coordinates of the installation point in the model coordinate system, the position coordinates of the installation point in the world coordinate system, and the first rotation matrix.

[0111] More specifically, the covariance difference matrix is ​​first calculated, then singular value decomposition is performed on the covariance matrix, and the first rotation matrix is ​​obtained based on the decomposition results. Specifically, calculating the covariance difference matrix includes: calculating the centroids of the three installation points in the model coordinate system and calculating the centroids of the three installation points in the world coordinate system. The covariance matrix is ​​obtained based on the position coordinates of the installation points in the model coordinate system, their position coordinates in the world coordinate system, and their centroids in the model and world coordinate systems.

[0112] Taking the first installation point as an example, the difference matrix in the model coordinate system is obtained based on the position coordinates and centroid of the installation point in the model coordinate system. Similarly, the difference matrix in the world coordinate system is obtained based on the position coordinates and centroid of the installation point in the world coordinate system. The product of the difference matrix in the model coordinate system and the difference matrix in the world coordinate system is then calculated to obtain the covariance matrix of one installation point. After calculating the covariance matrices for all three installation points using the same method, the sum of the covariance matrices for all three installation points is calculated to obtain the final covariance matrix.

[0113] Singular value transformation is performed on the covariance matrix to obtain the left singular vector matrix, the singular value diagonal matrix, and the right singular vector matrix. The first rotation matrix is ​​obtained based on the left and right singular vector matrices. An intermediate matrix is ​​calculated based on the first rotation matrix and the position coordinates of the mounting point in the model coordinate system. The first translation matrix is ​​obtained by subtracting the intermediate matrix from the position coordinates of the mounting point in the world coordinate system.

[0114] S202. The backend server extracts the position coordinates of the first marker point in the model coordinate system from the charger's model data, and uses the first coordinate transformation matrix to transform the position coordinates of the first marker point in the model coordinate system to obtain the position coordinates of the first marker point in the world coordinate system.

[0115] The charger has three first marker points, and the power grid industrial robot has three second marker points. Both the reference planes containing the three first marker points and the reference planes containing the three second marker points are perpendicular to the horizontal plane. When the charger's charging contacts mate with the power grid industrial robot's charging module, the first marker points and their corresponding second marker points coincide.

[0116] The first coordinate transformation matrix includes a first translation matrix and a first rotation matrix. The rotation coordinates are obtained by multiplying the first rotation matrix by the position coordinates of the first marker point in the model coordinate system. The rotation coordinates are then added to the first translation matrix to obtain the position coordinates of the first marker point in the world coordinate system.

[0117] S203. The backend server translates the position coordinates of the first marker point in the world coordinate system based on the model data of the power grid industrial robot to obtain the position coordinates of the second marker point in the world coordinate system.

[0118] Specifically, the size data of the power grid industrial robot is determined based on the model data of the power grid industrial robot. Based on the size data of the power grid industrial robot, the position coordinates of the first marker point in the world coordinate system are translated to obtain the position coordinates of the second marker point in the world coordinate system.

[0119] S204. The backend server obtains the pose of the power grid industrial robot at the alignment point based on the model data of the power grid industrial robot and the position coordinates of the second marker point in the world coordinate system.

[0120] Specifically, the position coordinates of the second marker point in the model coordinate system are extracted from the model data of the power grid industrial robot. A second coordinate transformation matrix is ​​generated based on the position coordinates of the second marker point in the model coordinate system and the position coordinates in the world coordinate system. The model data of the power grid industrial robot is transformed using the second coordinate transformation matrix to obtain the pose of the power grid industrial robot at the alignment point.

[0121] In the above technical solution, marker points are set on the power grid industrial robot and the charger. The position data of the first marker point is translated using the model data of the power grid industrial robot, and then transformed into different coordinate systems to obtain the pose of the power grid industrial robot at the alignment point. This ensures that the pose of the power grid industrial robot at the alignment point aligns the first and second marker points with each other, and that the charger and the power grid industrial robot do not interfere with each other. Thus, the power grid industrial robot is in the alignment point pose when it travels to the alignment point according to the charging path. Compared with the existing solution, which first travels to the vicinity of the charger and then makes multiple posture adjustments to the power grid industrial robot through multiple signal interactions with the charger, this solution does not require multiple interactions with the charger, thereby reducing the number of posture adjustments of the power grid industrial robot and improving the charging accuracy.

[0122] In some possible implementations, S203 and the backend server translate the position coordinates of the first marker point in the world coordinate system based on the model data of the power grid industrial robot to obtain the position coordinates of the second marker point in the world coordinate system, specifically including:

[0123] S301. The backend server projects the model data of the power grid industrial robot onto the horizontal plane to obtain the projection data of the power grid industrial robot, and obtains the working radius of the power grid industrial robot based on the projection data.

[0124] By setting the vertical coordinate of the power grid industrial robot's model data to the same height value, the projection data of the power grid industrial robot on the horizontal plane can be obtained.

[0125] A boundary extraction algorithm is used to process the projection data of the power grid industrial robot to obtain its boundary data. The working radius of the power grid industrial robot is then calculated based on this boundary data. More specifically, the minimum bounding rectangle of the projected boundary of the power grid industrial robot is calculated based on its boundary data, and the working radius is obtained from this minimum bounding rectangle. In some examples, the longer side of the minimum bounding rectangle of the projected boundary of the power grid industrial robot is used as its working radius.

[0126] S302. The backend server obtains the normal vector of the reference plane where the first marker point is located based on the position coordinates of the first marker point in the world coordinate system, and obtains the translation vector based on the working radius of the power grid industrial robot and the normal vector of the reference plane where the first marker point is located.

[0127] Specifically, two intersecting vectors are obtained using the three first marker points, and the normal vector of the reference plane containing the first marker points is obtained based on the two intersecting vectors. The translation vector is obtained by multiplying the working radius of the power grid industrial robot by the normal vector of the reference plane containing the first marker points.

[0128] S303. The backend server uses a translation vector to translate the position coordinates of the first marker point in the world coordinate system to obtain the position coordinates of the second marker point in the world coordinate system.

[0129] Taking any one of the three first marker points as an example, the position coordinates of the first marker point in the world coordinate system are added with a translation vector to realize the translation of the position coordinates of the first marker point in the world coordinate system, and the translated position coordinates are used as the position coordinates of the second marker point in the world coordinate system.

[0130] In the above technical solution, the dimensions of the power grid industrial robot are obtained based on the model data of the power grid industrial robot. The translation vector is obtained based on the dimensions of the power grid industrial robot and the normal vector of the reference plane where the first marker point is located. In this way, the first marker point can be translated based on the translation vector to obtain the position coordinates of the second marker point in the world coordinate system. The pose of the alignment point is obtained based on the position coordinates of the second marker point in the world coordinate system. When the power grid industrial robot is located at the alignment point, it is ensured that the second marker point and the first marker point are aligned, and the power grid industrial robot and the charger will not interfere with each other.

[0131] In some possible implementations, S204, the backend server obtains the pose of the power grid industrial robot at the alignment point based on the model data of the power grid industrial robot and the position coordinates of the second marker point in the world coordinate system, specifically including:

[0132] S401, The backend server obtains the position coordinates of the second marker point in the model coordinate system from the model data of the power grid industrial robot.

[0133] Specifically, the identifier of the second marker point is obtained from the model data of the power grid industrial robot, and the position coordinates of the second marker point in the model coordinate system are extracted from the model data of the power grid industrial robot based on the identifier of the second marker point.

[0134] S402. The backend server obtains the second coordinate transformation matrix based on the position coordinates of the second marker point in the model coordinate system and the position coordinates of the second marker point in the world coordinate system.

[0135] The method for obtaining the second coordinate transformation matrix is ​​similar to that for obtaining the first coordinate transformation matrix, and will not be described again here.

[0136] S403. The backend server uses the second coordinate transformation matrix to process the coordinate axes of the power grid industrial robot to obtain the pose of the power grid industrial robot at the alignment point.

[0137] In this system, the model coordinates and navigation coordinates of the power grid industrial robot coincide. When the power grid industrial robot is located at the origin of the world coordinate system and all three axis angles are zero, the world coordinate system and the model coordinate system coincide. The three axis angles are yaw, pitch, and roll, all of which are zero in the world coordinate system. A second rotation matrix is ​​used to transform the coordinate axes of the power grid industrial robot to obtain its pose at the alignment point.

[0138] In the above technical solution, the model data of the power grid industrial robot is processed based on the position coordinates of the second marker point in the world coordinate system to obtain the pose of the power grid industrial robot at the alignment point. This ensures that when the power grid industrial robot is at the alignment point and in the pose of the alignment point, the power grid industrial robot and the charger are aligned without the need for multiple adjustments.

[0139] In existing technical solutions, the electric grid robot typically stops performing its tasks and moves to a charger to recharge when its remaining battery power falls below a certain preset threshold. This preset threshold is generated empirically; if it's set too high, the robot will need to recharge frequently, affecting its task efficiency; if it's set too low, there's a risk that the robot will run out of power and be unable to continue performing its tasks.

[0140] To address the aforementioned issues, in some possible implementation methods, S103 involves the backend server obtaining the charging start point of the power grid industrial robot, specifically including:

[0141] S501: The backend server obtains the inspection task and initial inspection power of the power grid industrial robot, and generates the inspection path according to the inspection task.

[0142] The inspection path includes the poses of multiple inspection points, the inspection time for each inspection point, and the inspection sensors that need to be activated at each inspection point. The inspection task of the power grid industrial robot includes the inspection area and inspection equipment. It acquires the layout of the inspection equipment within the inspection area and generates the poses of multiple inspection points based on this layout. It determines the inspection sensors to be activated at each inspection point and the activation time based on the inspection equipment near each inspection point.

[0143] S502. The backend server uses the inspection points in the inspection path as alternative starting points. Based on the inspection path, the pose of the power grid industrial robot at the alignment point, and the initial inspection power, it calculates the remaining power of the power grid industrial robot from each alternative starting point to the alignment point.

[0144] in, Figure 4 This application provides a schematic diagram illustrating the principle of determining the charging start point. Figure 4 For example, the inspection path includes 12 inspection points. Taking any one of these inspection points as an example, calculate the remaining battery power when traveling to the target point, starting from that inspection point as the candidate starting point. For example... Figure 4 The China Electric Power Robot (CEW) moves to the 5th inspection point. Using the 5th inspection point as a candidate starting point, it calculates the remaining power from the candidate starting point to the alignment point. First, based on the inspection path and the initial inspection power, the remaining power at each inspection point is calculated. Then, the remaining power to the alignment point is calculated.

[0145] More specifically, the robot calculates the distance traveled to the inspection point based on its pose, calculates the power consumption of the robot's movement based on the distance traveled to the inspection point, calculates the power consumption of activating the inspection sensors based on the inspection sensors activated at the inspection point, and calculates the remaining power when it reaches the inspection point based on the power consumption of the power grid industrial robot's movement and the power consumption of activating the inspection sensors.

[0146] The power required to walk to the alignment point is calculated based on the pose of the inspection point and the alignment point. The remaining power of the power grid industrial robot to reach the alignment point is obtained based on the remaining power upon reaching the inspection point and the power required to walk from the inspection point to the alignment point.

[0147] S503: The backend server selects the charging start point from the candidate start points based on the remaining power of the power grid industrial robot when it reaches the alignment point and the distance from the alignment point to the charging point.

[0148] The charging point is the location where the charger charges the power grid industrial robot. The required power is calculated based on the distance the power grid industrial robot travels from the alignment point to the charging point. The calculated required power is used as the lower limit of the remaining power. It is then determined whether the remaining power of the power grid industrial robot when it reaches the alignment point is greater than the lower limit of the remaining power. If so, the corresponding inspection point is used as the charging start point. Otherwise, it is determined whether the next remaining power is within the power range.

[0149] In the above technical solution, the remaining power of the power grid industrial robot after walking from each alternative starting point to the alignment point is determined based on the inspection task, the initial inspection power of the power grid industrial robot, and the pose of the alignment point. The optimal charging starting point is selected based on the remaining power of the alignment point and the distance from the alignment point to the charging point, so as to make full use of the power of the power grid industrial robot, improve the work efficiency, and reduce the risk of the power grid industrial robot running out of power.

[0150] In some possible implementations, S502 and the backend server use inspection points in the inspection path as alternative starting points. Based on the inspection path, the pose of the power grid industrial robot at the alignment point, and the initial inspection charge, they calculate the remaining charge of the power grid industrial robot from each alternative starting point to the alignment point. Specifically, this includes:

[0151] S601: The backend server calculates the remaining power of the power grid industrial robot at each inspection point based on the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be turned on at each inspection point, and the initial inspection power.

[0152] Specifically, the power consumption to travel to each inspection point is calculated based on the position and initial power consumption of each inspection point; the power consumption to activate the inspection sensors at each inspection point is calculated based on the inspection time and the number of inspection sensors that need to be activated at each inspection point; the power consumption of each inspection point is calculated based on the power consumption to travel to each inspection point and the power consumption to activate the inspection sensors at each inspection point; and the remaining power at each inspection point is calculated based on the initial power consumption and the power consumption of each inspection point.

[0153] S602. The backend server obtains the charging path starting from the candidate starting point based on the pose of each inspection point and the pose at the alignment point, and calculates the power consumption of the charging path starting from the candidate starting point.

[0154] In this process, all inspection points are used as candidate starting points. Taking any one inspection point as an example, path planning is performed based on the pose of the power grid industrial robot at the inspection point and the pose at the alignment point to obtain the charging path starting from the candidate starting point, and the power consumption of the charging path is calculated.

[0155] More specifically, by statistically analyzing historical data of the power grid industrial robot, the power consumption per unit distance and per unit angle are obtained. The path planning results include the positions and yaw angles of different path points. The distances between path points are calculated based on their positions. The straight-line travel power consumption at each path point is obtained based on the distances between path points and the power consumption per unit distance. The turning-around power consumption at each path point is calculated based on the yaw angle and the power consumption per unit angle. The sum of the straight-line travel power consumption and the turning-around power consumption at each path point is calculated as the power consumption of the charging path.

[0156] S603. The backend server calculates the remaining power of the power grid industrial robot from the alternative starting point to the alignment point based on the remaining power of the robot at each inspection point and the power consumption of the charging path from the alternative starting point.

[0157] Specifically, the difference between the remaining power of the power grid industrial robot at the inspection point and the power consumption of the charging path starting from the alternative starting point is calculated to obtain the remaining power of the power grid industrial robot from the alternative starting point to the alignment point.

[0158] In the above technical solution, the backend server calculates the remaining power of the power grid industrial robot when it travels to the inspection point based on the inspection task and the initial inspection power. Then, it calculates the remaining power from each inspection point to the alignment point based on the pose of the inspection and the pose of the alignment point. Compared with the power grid industrial robot calculating the remaining power from each inspection point to the alignment point by reading the power stored in the battery management system after each inspection point, predicting the remaining power from each inspection point to the alignment point through the backend can reduce the energy consumed by the power grid industrial robot in calculating the remaining power.

[0159] In some possible implementations, S601 and the backend server calculate the remaining power of the power grid industrial robot at each inspection point based on the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be activated at each inspection point, and the initial inspection power. Specifically, this includes:

[0160] S701: The backend server obtains the total power consumption of the inspection sensors activated at each inspection point based on the power consumption per unit time of the inspection sensors activated at each inspection point and the inspection time of each inspection point.

[0161] Specifically, for each inspection point, the backend server calculates the power consumption of the inspection sensor per unit time and the inspection time of the inspection point to obtain the power consumption of each inspection sensor turned on at the inspection point. Then, it calculates the sum of the power consumption of each inspection sensor turned on at the inspection point to obtain the total power consumption of the inspection sensors turned on at that inspection point.

[0162] S702: The background server obtains the single-point driving power consumption of the inspection point based on the pose of the previous inspection point and the pose of the current inspection point.

[0163] The backend server calculates the straight-line travel distance based on the positions of the previous and current inspection points, and then calculates the single-point straight-line travel power consumption based on the straight-line travel distance and the power consumption per unit distance. It also calculates the angle difference between the yaw angle of the previous and current inspection points, and calculates the single-point turning power consumption based on the angle difference and the power consumption per unit angle. Finally, it obtains the single-point travel power consumption of each inspection point based on the single-point straight-line travel power consumption and the single-point turning power consumption.

[0164] S703: The background server obtains the remaining power of the current inspection point based on the total power consumption of the inspection sensors activated at the current inspection point, the power consumption of single-point driving, and the remaining power of the previous inspection point.

[0165] Specifically, if the current inspection point is the first inspection point, then the remaining power of the previous inspection point is the initial inspection power. If the current inspection point is any other than the first inspection point, then the previous inspection point is the inspection point whose inspection sequence precedes the current inspection point.

[0166] For each inspection point, the backend server calculates the sum of the power consumption of the activated inspection sensors and the power consumption of single-point driving at the current inspection point to obtain the power consumption of the current inspection point. The remaining power of the current inspection point is obtained by subtracting the power consumption of the current inspection point from the remaining power of the previous inspection point.

[0167] The above technical solution takes into account the power consumption of the power grid industrial robot when changing direction, the power consumption when moving in a straight line, and the power consumption when turning on the inspection sensor inspection equipment. It predicts the remaining power at each inspection point, which can improve the accuracy of power prediction and thus improve the charging success rate.

[0168] In some possible implementations, S602 calculates the power consumption of the charging path starting from the alternative starting point, specifically including:

[0169] S801. Based on the poses of two adjacent path points in the charging path starting from the candidate starting point, calculate the difference in yaw angle between the two adjacent path points and the distance between the two adjacent path points.

[0170] The charging path starting from the candidate starting point includes four path points. The difference in yaw angle between the first and second path points is calculated based on the pose of the power grid industrial robot at the first and second path points, and the straight-line distance between the first and second path points is also calculated.

[0171] Calculate the difference in yaw angle between the second and third path points of the power grid industrial robot based on the poses of the robot at the second and third path points, and calculate the straight-line distance between the robot at the second and third path points.

[0172] Calculate the difference in yaw angle between the third and fourth path points of the power grid industrial robot based on the poses of the robot at the third and fourth path points, and calculate the straight-line distance between the robot at the third and fourth path points.

[0173] S802. Obtain the single-point turning power consumption of a path point based on the difference in yaw angle between two adjacent path points, and obtain the single-point straight-line driving power consumption of a path point based on the distance between two adjacent path points.

[0174] Specifically, the first second turning power consumption is obtained based on the power consumption per unit angle and the difference in yaw angle between the first and second path points of the power grid industrial robot; the second second turning power consumption is obtained based on the power consumption per unit angle and the difference in yaw angle between the second and third path points of the power grid industrial robot; and the third second turning power consumption is obtained based on the power consumption per unit angle and the difference in yaw angle between the third and fourth path points of the power grid industrial robot.

[0175] The first power consumption for linear travel is obtained based on the power consumption per unit distance and the distance between the first and second path points of the power grid industrial robot. The second power consumption for linear travel is obtained based on the power consumption per unit distance and the distance between the second and third path points of the power grid industrial robot. The third power consumption for linear travel is obtained based on the power consumption per unit distance and the distance between the third and fourth path points of the power grid industrial robot.

[0176] S803. Obtain the power consumption of the charging path starting from the candidate starting point based on the power consumption of turning at each path point and the power consumption of straight driving at each path point.

[0177] The backend server sums the three steering power consumptions and three first straight-line driving power consumptions obtained in the above steps to obtain the total power consumption of the charging path starting from the candidate starting point.

[0178] In the above technical solution, the power consumption of the power grid industrial robot in changing direction, in straight-line travel, and in turning on the inspection sensor inspection equipment are all taken into account. The path with the least energy consumption is selected as the charging path. In this way, the power grid industrial robot can allocate more power to the inspection task and improve the efficiency of performing the inspection task.

[0179] In one possible implementation, S503, based on the remaining battery power of the electric grid industrial robot upon reaching the alignment point and the distance from the alignment point to the charging point, selects a charging starting point from the candidate starting points, specifically including:

[0180] S901: The back-end server calculates the amount of electricity required for the power grid industrial robot to travel from the alignment point to the charging point based on the distance between the alignment point and the charging point.

[0181] Specifically, the power consumption per unit distance of the power grid industrial robot's straight-line movement is obtained, and the power required for the power grid industrial robot to move from the alignment point to the charging point is calculated by multiplying the distance from the alignment point to the charging point and the power consumption per unit distance of the straight-line movement.

[0182] S902. Select a charging starting point from multiple alternative starting points based on the amount of electricity required for the power grid industrial robot to travel from the alignment point to the charging point and the remaining electricity of the power grid industrial robot from the alternative starting point to the alignment point.

[0183] Among them, the amount of electricity required for the power grid industrial robot to walk from the alignment point to the charging point is used as the lower limit of the remaining electricity. From multiple remaining electricity values, the candidate starting point corresponding to the remaining electricity lower limit is selected as the charging starting point.

[0184] In the above technical solution, the amount of electricity required for the power grid industrial robot to walk from the alignment point to the charging point is calculated based on the distance from the alignment point to the charging point. This is used as the lower limit of the remaining power. The charging start point is selected when the remaining power is greater than the lower limit. This ensures that the power grid industrial robot will not fail to charge due to low power, thus improving the efficiency of task execution.

[0185] In one possible implementation, S902, selecting a charging starting point from multiple alternative starting points based on the power required for the power grid industrial robot to travel from the alignment point to the charging point and the remaining power of the power grid industrial robot from the alternative starting point to the alignment point, specifically includes:

[0186] S1001. Select a remaining power value greater than the lower limit value from the remaining power of the power grid industrial robot from the alternative starting point to the alignment point. The lower limit value is the power required for the power grid industrial robot to travel from the alignment point to the charging point.

[0187] S1002. Select the candidate starting point corresponding to the smallest remaining power from the remaining power that is greater than the lower limit of power as the charging starting point.

[0188] If there are 5 candidate starting points, the remaining power from the 5 candidate starting points to the alignment point can be calculated. If the remaining power from 4 of the candidate starting points to the alignment point is greater than the lower limit of the power, then the candidate starting point with the smallest remaining power from the 4 candidate starting points to the alignment point is selected as the charging starting point.

[0189] In the above technical solution, based on the amount of electricity required for the power grid industrial robot to walk from the alignment point to the charging point, the candidate starting point corresponding to the smallest remaining electricity value among the remaining electricity values ​​that meet the minimum electricity value requirement can be selected as the charging starting point, which can make full use of the electricity and improve the efficiency of task execution.

[0190] Considering that the power grid industrial robot is an outdoor robot, weather factors have an impact on its power consumption. For example, windy weather can increase or decrease the resistance of the industrial robot's movement.

[0191] To improve the accuracy of predicting the remaining power of the power grid industrial robot, weather factors are taken into account. In one possible implementation, S103, the backend server obtains the charging start point of the power grid industrial robot, specifically including:

[0192] S1101: The backend server obtains the inspection task, initial inspection power, and inspection wind information of the power grid industrial robot, and generates the inspection path according to the inspection task.

[0193] The inspection path includes the poses of multiple inspection points, the inspection time for each inspection point, and the inspection sensors that need to be activated at each inspection point. The inspection task of the power grid industrial robot includes the inspection area and inspection equipment. It acquires the layout of the inspection equipment within the inspection area and generates the poses of multiple inspection points based on this layout. It determines the inspection sensors to be activated at each inspection point and the activation time based on the inspection equipment near each inspection point.

[0194] S1102. Using the inspection points in the inspection path as alternative starting points, calculate the remaining power of the power grid industrial robot from each alternative starting point to the alignment point based on the inspection path, the pose of the power grid industrial robot at the alignment point, the initial inspection power, and the inspection wind information.

[0195] The process involves several steps: First, the power consumption for traveling to each inspection point is obtained based on the pose and wind information of each inspection point along the inspection path. Second, the power consumption at each inspection point due to the activation of inspection sensors is obtained based on the inspection time and the required sensors for each inspection. Third, the remaining power at each inspection point is calculated based on the power consumption for traveling to each inspection point, the power consumption due to the activation of inspection sensors, and the initial inspection quantity. Finally, the remaining power consumption for traveling to the alignment point is calculated based on the pose of each inspection point, the pose of the alignment point, and the wind information, using that inspection point as a candidate starting point.

[0196] S1103. Based on the remaining power of the power grid industrial robot upon reaching the alignment point and the distance from the alignment point to the charging point, select the charging starting point from the candidate starting points.

[0197] This step has been described in detail in the above embodiments and will not be repeated here.

[0198] In the above technical solution, since the power grid industrial robot operates outdoors, when calculating the power consumption for walking to each inspection point and the power consumption for walking from each alternative starting point to the alignment point based on the inspection wind information, the increase or decrease of the walking resistance of the power grid industrial robot by wind force is taken into account, which can improve the accuracy of power consumption prediction. This can increase the remaining power obtained from the alternative starting point to the alignment point, accurately determine the charging starting point, and thus reduce the risk that the power grid industrial robot will not be able to continue working due to power depletion.

[0199] In one possible implementation, S1102, using inspection points along the inspection path as alternative starting points, calculates the remaining power of the power grid industrial robot from each alternative starting point to the alignment point based on the inspection path, the pose of the power grid industrial robot at the alignment point, the initial inspection power, and the inspection wind information. Specifically, this includes:

[0200] S1201. Calculate the remaining power of the power grid industrial robot at each inspection point based on the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be turned on at each inspection point, the inspection wind information, and the initial inspection power.

[0201] Specifically, the power consumption for traveling to each inspection point is determined based on the position and wind information of each inspection point, and the power consumption of the inspection sensors at each inspection point is determined based on the inspection time of each inspection point and the inspection sensors that need to be turned on at each inspection point.

[0202] More specifically, for each inspection point, the yaw angle of the previous inspection point is extracted from its pose. Based on the yaw angle of the previous inspection point and the wind speed information, the power consumption per unit distance is corrected to obtain the corrected power consumption per unit distance. Then, based on the corrected power consumption per unit distance and the distance between the previous and current inspection points, the power consumption for single-point straight-line travel at the current inspection point is calculated.

[0203] The power consumption per unit angle is corrected based on the yaw angle of the previous inspection point, the yaw angle of the current inspection point, and the wind force information, to obtain the corrected power consumption per unit angle. The single-point change-of-direction power consumption of the current inspection point is calculated based on the angle difference between the yaw angle of the current inspection point and the yaw angle of the previous inspection point, and the corrected power consumption per unit angle.

[0204] The remaining power of the current inspection is obtained based on the remaining power of the previous inspection, the power consumption of the current inspection point when changing direction at a single point, and the power consumption of the current inspection point when traveling straight at a single point.

[0205] More specifically, the power consumption per unit distance is corrected based on the yaw angle and wind information of the previous inspection point to obtain the corrected power consumption per unit distance, which includes:

[0206] The power consumption per unit distance was obtained through testing under different wind directions and strengths. A second wind direction coefficient function was obtained under different wind strengths by fitting the test data. Based on the inspected wind force, a first wind direction coefficient function was selected from multiple functions. The yaw angle of the previous inspection point and the angle between the inspected wind direction were obtained. The wind direction coefficient was obtained based on the preset wind direction coefficient function and the calculated angle. The corrected power consumption per unit distance was obtained by multiplying the wind direction coefficient by the power consumption per unit distance.

[0207] The power consumption per unit angle is corrected based on the yaw angle and wind information from the previous inspection point to obtain the corrected power consumption per unit angle, specifically including:

[0208] The power consumption per unit angle was obtained through testing under different wind directions and strengths. A second wind direction coefficient function was obtained under different wind strengths by fitting the test data. Based on the inspected wind force, a second wind direction coefficient function was selected from multiple functions. The angle between the yaw angle of the previous inspection point and the inspected wind direction was obtained. The wind direction coefficient was obtained based on the preset wind direction coefficient function and the calculated angle. The corrected power consumption per unit angle was obtained by multiplying the wind direction coefficient by the power consumption per unit angle.

[0209] S1202. Based on the pose of each inspection point and the pose at the alignment point, perform path planning to obtain a charging path starting from the candidate starting point, and calculate the power consumption of the charging path starting from the candidate starting point based on the inspection wind information.

[0210] Using inspection points as alternative starting points, a charging path from the alternative starting point to the alignment point is generated based on the poses of each inspection point and the alignment point.

[0211] The power consumption of the charging path starting from the candidate starting point is calculated based on the wind speed information detected during the inspection, specifically including:

[0212] The power consumption per unit distance is corrected based on the yaw angle of the previous path point and the wind speed information during inspection, resulting in the corrected power consumption per unit distance. Similarly, the power consumption per unit angle is corrected based on the yaw angle of the previous path point and the wind speed information during inspection, resulting in the corrected power consumption per unit angle.

[0213] Based on the poses of two adjacent path points in the charging path starting from the candidate starting point, calculate the difference in yaw angle between the two adjacent path points and the distance between the two adjacent path points.

[0214] The power consumption for turning at a single point is obtained by calculating the difference in yaw angle between two adjacent path points and the power consumption per unit angle after correction. The power consumption for straight-line driving at a single point is obtained by calculating the distance between two adjacent path points and the power consumption per unit distance after correction.

[0215] The power consumption of the charging path starting from the candidate starting point is obtained based on the power consumption of turning at each path point and the power consumption of straight driving at each path point.

[0216] S1203. Based on the remaining power of the power grid industrial robot at each inspection point and the power consumption of the charging path from the alternative starting point, calculate the remaining power of the power grid industrial robot from the alternative starting point to the alignment point.

[0217] In the above technical solution, the remaining power at each inspection point of the power grid industrial robot during its inspection task, along with the remaining power from each inspection point as a candidate starting point to the alignment point, is calculated. The optimal charging starting point is then selected based on the remaining power. Therefore, the estimation of the remaining power is crucial for selecting the optimal charging starting point. Furthermore, considering that the power grid industrial robot primarily operates outdoors, and outdoor weather affects its power consumption, this application incorporates wind information when calculating the remaining power at each inspection point and the remaining power upon reaching the alignment point. More specifically, for each inspection point along the inspection path, considering the different yaw angles at each point and the varying degrees of wind impact on the power grid industrial robot, the power consumption per unit distance and per unit angle is corrected based on the yaw angle for each inspection point. This improves the accuracy of the remaining power estimation.

[0218] In existing technologies, a charger is usually configured for each power grid industrial robot. This means that the power grid industrial robot needs to consider whether there is enough remaining power when performing tasks, so that the power grid industrial robot can travel from the charging start point to the charging point, which will reduce the efficiency of performing inspection tasks.

[0219] To address the aforementioned problems, some embodiments of this application also provide an intelligent processing method for an adaptive power grid industrial robot charger, which specifically includes:

[0220] S1301, The backend server obtains the remaining power of multiple power grid industrial robots at each inspection point and the alternative deployment area where each inspection point is located when performing inspection tasks.

[0221] As an example, before the power grid industrial robot performs its inspection task, the backend server sends a power acquisition instruction to the power grid industrial robot. This power acquisition instruction is used to enable the power grid industrial robot to store the remaining power at each inspection point and send the remaining power at each inspection point to the backend server after the inspection task is completed.

[0222] In another example, the initial inspection power of the inspection task and the power grid industrial robot is obtained, and an inspection path is generated based on the inspection task. The inspection path includes the pose of multiple inspection points, the inspection time of each inspection point, and the inspection sensors that need to be turned on at each inspection point. The remaining power of each inspection point is generated based on the inspection path and the initial inspection power.

[0223] The inspection task of the power grid industrial robot includes the inspection area and inspection equipment. It acquires the layout of the inspection equipment within the inspection area and generates the poses of multiple inspection points based on this layout. It then determines the inspection sensors to be activated at each inspection point and the activation time based on the inspection equipment near each point.

[0224] The remaining power of the power grid industrial robot at each inspection point is calculated based on the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be activated at each inspection point, and the initial inspection power.

[0225] Specifically, the power consumption to travel to each inspection point is calculated based on the position and initial power consumption of each inspection point; the power consumption to activate the inspection sensors at each inspection point is calculated based on the inspection time and the number of inspection sensors that need to be activated at each inspection point; the power consumption of each inspection point is calculated based on the power consumption to travel to each inspection point and the power consumption to activate the inspection sensors at each inspection point; and the remaining power at each inspection point is calculated based on the initial power consumption and the power consumption of each inspection point.

[0226] In the above technical solution, a backend server can be used to randomly generate inspection tasks, perform path planning based on the inspection tasks, and estimate the remaining power of the power grid industrial robot at each inspection point. In this way, there is no need to obtain the remaining power from the power grid industrial robot, and sufficient data can be generated for the layout of the charger.

[0227] The backend server divides the inspection area into multiple alternative deployment areas. The backend server determines the alternative deployment area where each inspection point is located based on the location range covered by each alternative deployment area and the location of each inspection point.

[0228] S1302. The backend server calculates the ratio of the number of inspection points in each candidate deployment area whose remaining power is less than the preset threshold to the total number of inspection points in each candidate deployment area.

[0229] Taking any candidate deployment area as an example, the preset threshold can be determined based on the size of the candidate deployment area. If there are 20 inspection points in the candidate deployment area, and 15 of the 20 inspection points are found to have remaining power less than the preset threshold, then the calculation ratio is 0.75.

[0230] S1303. If the ratio is greater than the preset ratio threshold, the corresponding alternative arrangement area is determined as the target arrangement area, and the target arrangement area is used to arrange the charger.

[0231] The preset ratio threshold is set to 0.7. If 0.75 > 0.7, then the candidate arrangement area is taken as the target arrangement area, and the target arrangement area is used to arrange the charger.

[0232] In the above technical solution, the remaining power at each inspection point of each power grid industrial robot is collected when performing inspection tasks to obtain the charging demand of the power grid industrial robot when performing tasks. By analyzing the charging demand of each alternative deployment area, it is determined whether to deploy a charger in that area. In this way, multiple chargers can be deployed in the inspection area, and the location of the charger is the area where the power grid industrial robot has the highest probability of running out of power when performing tasks. In this way, the power grid industrial robot can allocate more power for inspection tasks, reduce the power consumed in walking to the charger for charging, and improve task execution efficiency.

[0233] The backend server provided in this embodiment includes at least one processor and a memory. Optionally, the device also includes a communication component. The processor, memory, and communication component are connected via a bus.

[0234] In a specific implementation, at least one processor executes computer execution instructions stored in memory, causing at least one processor to perform the above-described method.

[0235] The specific implementation process of the processor can be found in the above method embodiments, and its implementation principle and technical effect are similar, so it will not be repeated here.

[0236] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0237] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0238] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0239] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0240] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0241] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0242] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0243] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0244] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0245] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0246] If a function 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 this invention, or the part that contributes to the prior art, or a 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.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0247] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0248] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for intelligent processing of an adaptive power grid industrial robot charger, characterized by, The intelligent processing method is applied to a background server, the background server is in communication connection with a charger, and the background server is in communication connection with a power grid industrial robot. Obtain the installation position of the charger, the model data of the charger, and the model data of the power grid industrial robot. Obtain the pose of the power grid industrial robot at an alignment point aligned with the charger according to the installation position of the charger, the model data of the charger, and the model data of the power grid industrial robot. Obtain the pose of the charging starting point of the power grid industrial robot, and generate a charging path according to the pose of the charging starting point of the power grid industrial robot and the pose of the power grid industrial robot at the alignment point. Send the charging path to the power grid industrial robot, so that the power grid industrial robot autonomously walks to the charger for charging according to the charging path. The charger is provided with a mounting point for mounting the charger and a first marker point for alignment with the power grid industrial robot, the power grid industrial robot is provided with a second marker point for alignment with the charger, and the installation position of the charger includes the position coordinates of the mounting point in a world coordinate system. Accordingly, the pose of the power grid industrial robot at an alignment point aligned with the charger is obtained according to the installation position of the charger, the model data of the charger, and the model data of the power grid industrial robot, and specifically includes: Extract the position coordinates of the mounting point in a model coordinate system from the model data of the charger, and generate a first coordinate conversion matrix according to the position coordinates of the mounting point in the world coordinate system and the position coordinates of the mounting point in the model coordinate system. Extract the position coordinates of the first marker point in the model coordinate system from the model data of the charger, and perform coordinate conversion on the position coordinates of the first marker point in the model coordinate system using the first coordinate conversion matrix to obtain the position coordinates of the first marker point in the world coordinate system. Translate the position coordinates of the first marker point in the world coordinate system according to the model data of the power grid industrial robot to obtain the position coordinates of the second marker point in the world coordinate system. Obtain the pose of the power grid industrial robot at the alignment point according to the model data of the power grid industrial robot and the position coordinates of the second marker point in the world coordinate system.

2. The intelligent processing method of claim 1, wherein, The translation of the position coordinates of the first marker point in the world coordinate system according to the model data of the power grid industrial robot to obtain the position coordinates of the second marker point in the world coordinate system specifically includes: Project the model data of the power grid industrial robot to a horizontal plane to obtain the projection data of the power grid industrial robot, and obtain the working radius of the power grid industrial robot according to the projection data of the power grid industrial robot; Obtain the normal vector of the reference plane where the first marker point is located according to the position coordinates of the first marker point in the world coordinate system, and obtain a translation vector according to the working radius of the power grid industrial robot and the normal vector of the reference plane where the first marker point is located; Translate the position coordinates of the first marker point in the world coordinate system using the translation vector to obtain the position coordinates of the second marker point in the world coordinate system.

3. The intelligent processing method of claim 2, wherein, According to the model data of the power grid industrial robot and the position coordinates of the second marker point in the world coordinate system, a pose of the power grid industrial robot at the alignment point is obtained, specifically comprising: obtaining the position coordinates of the second marker point in the model coordinate system from the model data of the power grid industrial robot; obtaining a second coordinate conversion matrix according to the position coordinates of the second marker point in the model coordinate system and the position coordinates of the second marker point in the world coordinate system; processing the coordinate axes of the power grid industrial robot using the second coordinate conversion matrix to obtain the pose of the power grid industrial robot at the alignment point.

4. The intelligent processing method of any one of claims 1 to 3, wherein, The charging starting point of the power grid industrial robot is obtained, specifically comprising: obtaining the inspection task and the initial inspection power of the power grid industrial robot, and generating an inspection path according to the inspection task, wherein the inspection path comprises the poses of multiple inspection points, the inspection time of each inspection point, and the inspection sensors that need to be turned on at each inspection point; taking the inspection points in the inspection path as candidate starting points, calculating the residual power of the power grid industrial robot from each candidate starting point to the alignment point according to the inspection path, the pose of the power grid industrial robot at the alignment point, and the initial inspection power; selecting the charging starting point from the candidate starting points according to the residual power of the power grid industrial robot from each candidate starting point to the alignment point and the distance from the alignment point to the charging point; the charging point is the position point where the power grid industrial robot charges.

5. The intelligent processing method of claim 4, wherein, Taking the inspection points in the inspection path as candidate starting points, calculating the residual power of the power grid industrial robot from each candidate starting point to the alignment point according to the inspection path, the pose of the power grid industrial robot at the alignment point, and the initial inspection power, specifically comprising: calculating the residual power of the power grid industrial robot at each inspection point according to the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be turned on at each inspection point, and the initial inspection power; taking the inspection points in the inspection path as candidate starting points, performing path planning according to the pose of each inspection point and the pose at the alignment point to obtain a charging path starting from the candidate starting point, and calculating the power consumption of the charging path starting from the candidate starting point; calculating the residual power of the power grid industrial robot from the candidate starting point to the alignment point according to the residual power of the power grid industrial robot at each inspection point and the power consumption of the charging path starting from the candidate starting point.

6. The intelligent processing method of claim 5, wherein, Calculating the residual power of the power grid industrial robot at each inspection point according to the pose of each inspection point, the inspection time of each inspection point, the inspection sensors that need to be turned on at each inspection point, and the initial inspection power, specifically comprising: obtaining the total power consumption of the inspection sensors turned on at each inspection point according to the power consumption per unit time of the inspection sensors turned on at each inspection point and the inspection time of each inspection point; obtaining the single-point driving power consumption of the inspection point according to the pose of the previous inspection point and the pose of the current inspection point; The remaining power of the current inspection point is obtained according to the total power consumption of the inspection sensor opened by the current inspection point, the single-point driving power consumption and the remaining power of the previous inspection point; wherein, if the current inspection point is the first inspection point, the remaining power of the previous inspection point is the initial inspection power.

7. The intelligent processing method of claim 5, wherein, The power consumption of the charging path starting from the alternative starting point is calculated, specifically including: According to the poses of two adjacent path points in the charging path starting from the alternative starting point, the difference of the yaw angles between the two adjacent path points and the distance between the two adjacent path points are calculated; The single-point turning power consumption of the path point is obtained according to the difference of the yaw angles between the two adjacent path points, and the single-point straight walking power consumption of the path point is obtained according to the distance between the two adjacent path points; The power consumption of the charging path starting from the alternative starting point is obtained according to the single-point turning power consumption of each path point and the single-point straight walking power consumption of each path point.

8. The intelligent processing method of claim 4, wherein, The charging starting point is selected from the alternative starting points according to the remaining power of the grid industrial robot reaching the alignment point and the distance from the alignment point to the charging point, specifically including: The power required for the grid industrial robot to walk from the alignment point to the charging point is calculated according to the distance from the alignment point to the charging point; The charging starting point is selected from the multiple alternative starting points according to the power required for the grid industrial robot to walk from the alignment point to the charging point and the remaining power of the grid industrial robot reaching the alignment point from the alternative starting point.

9. The intelligent processing method of claim 8, wherein, The charging starting point is selected from the multiple alternative starting points according to the power required for the grid industrial robot to walk from the alignment point to the charging point and the remaining power of the grid industrial robot reaching the alignment point from the alternative starting point, specifically including: The remaining power greater than the power lower limit value is selected from the remaining power of the grid industrial robot reaching the alignment point from the alternative starting point, wherein the power lower limit value is the power required for the grid industrial robot to walk from the alignment point to the charging point; The alternative starting point corresponding to the minimum remaining power is selected as the charging starting point from the remaining power greater than the power lower limit value.

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