Robot Energy Management Method, Related Systems, Storage Media, and Programs

Abstract

The present invention provides a mobile robot energy management method, system and storage medium, which specifically include: obtaining planned movement data, planned operation data, real-time mobile obstacle data and real-time operation obstacle data, analyzing actual movement path data and actual operation path data, calculating overall energy consumption data, obtaining the state of charge data of the lithium battery of the mobile robot based on Kalman filter analysis, performing energy management before executing movement operations and operation operations according to the total energy consumption of the robot and the remaining power before operation, and collecting the post-operation state data of the lithium battery of the mobile robot, so as to perform energy management after executing movement operations and operation operations, etc. implementation steps. Based on the above solution, it is possible to perform energy management on the mobile robot before and after use, ensure that the mobile robot can smoothly and continuously complete tasks, and avoid phenomena such as overcharging or power-off during task execution due to improper power management.

Description

Technical Field

[0001] The present invention relates to the field of industrial robot control, and relates to a mobile robot energy management method, related systems, storage media and programs applied to industrial production. Background Art

[0002] Industrial robots can replace human labor to complete simple and repetitive tasks in the industrial production process, and can serve product quality and production efficiency while ensuring operation safety.

[0003] Classified by whether the position is fixed, industrial robots can be divided into fixed robots and mobile robots. Fixed robots are set on the flowing production line, and the production line drives the products or components to move. When reaching a specific position of the fixed robot, the fixed robot performs specific operations according to the preset operation instructions. Mobile robots can move freely within a certain area to complete tasks such as picking up parts, assembling, and transporting. This technical solution is optimized based on mobile robots.

[0004] Mobile robots generally require a long moving distance and need to cooperate with robotic arms for fine operations. Most of these robots come with independent energy sources, but due to their carrying of moving functions and operating functions, they consume a large amount of energy during the production process and generally have poor endurance. How to manage the energy of mobile robots and solve the "endurance anxiety" of mobile robots during the production process is an urgent problem to be solved in the industry. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a mobile robot energy management method, system and storage medium for the above-mentioned defects of the prior art, which can perform energy management before and after use to ensure that the mobile robot can smoothly and continuously complete tasks and avoid overcharging or power interruption during task execution due to improper power management.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0007] A mobile robot energy management method, the method includes the following steps:

[0008] S1. Obtain planned movement data and planned operation data, where the planned movement data is used to control the mobile robot to perform movement operations within a preset movement space according to preset movement instructions, and the planned operation data is used to control the mobile robot to perform operation operations within a preset operation space according to preset operation instructions;

[0009] S2. Obtain real-time mobile obstacle data and real-time operation obstacle data. The real-time mobile obstacle data includes the real-time two-dimensional data of obstacles in the mobile space, and the real-time operation obstacle data includes the real-time three-dimensional data of obstacles in the operation space;

[0010] S3. Analyze the real-time mobile obstacle data based on the planned mobile data to obtain the actual mobile path data, which is used to control the mobile robot to move and perform mobile operations in the preset mobile space according to the optimized mobile instructions;

[0011] S4. Analyze the real-time operation obstacle data based on the planned operation data to obtain the actual operation path data, which is used to control the mobile robot to perform operation operations in the preset operation space according to the optimized operation instructions;

[0012] S5. Calculate the mobile energy consumption data based on the actual mobile path data and the AGV operation parameters, calculate the operation energy consumption data based on the actual operation path data and the robotic arm operation parameters, and sum the mobile energy consumption data and the operation energy consumption data to obtain the overall energy consumption data. The overall energy consumption data is the total energy consumption of the robot required to execute the instructions included in the actual mobile path data and the actual operation path data;

[0013] S6. Analyze the state of charge data of the lithium battery of the mobile robot based on the Kalman filter. The state of charge data includes the remaining power before operation of the lithium battery, and the remaining power before operation is the remaining power of the mobile robot before performing mobile operations and operation operations;

[0014] S7. Perform energy management before performing mobile operations and operation operations according to the total energy consumption of the robot and the remaining power before operation; when the remaining power before operation > the total energy consumption of the robot, the energy management analysis result is to perform operation operations, and control the mobile robot to perform mobile operations and operation operations; when the remaining power before operation ≤ the total energy consumption of the robot, the energy management analysis result is to perform charging operations, and control the mobile robot to perform charging operations;

[0015] S8. Control the mobile robot to perform mobile operations in the preset mobile space according to the optimized mobile instructions, and control the mobile robot to perform operation operations in the preset operation space according to the optimized operation instructions until all mobile operations and operation operations are completed;

[0016] S9. Collect the post-operation status data of the lithium battery of the mobile robot, calculate the remaining power of the lithium battery after operation according to the post-operation status data, compare it with a preset charging threshold, and determine whether the mobile robot needs to perform a charging operation, so as to perform energy management after performing a moving operation and an operating operation; the post-operation status data includes voltage, current, and temperature, and the remaining power after operation is the remaining power of the mobile robot after performing a moving operation and an operating operation.

[0017] Further, in step S3, analyzing the actual movement path data according to the planned movement data and the real-time movement obstacle data specifically includes:

[0018] S301. Establish a movement space coordinate system within a preset movement space, and the movement space coordinate system is a two-dimensional Cartesian coordinate system;

[0019] S302. Obtain the planned movement data, analyze multiple discrete path points of the planned movement path according to the planned movement data, and further convert to obtain the coordinate values corresponding to the multiple discrete path points in the movement space coordinate system. Among them, the first discrete path point is the initial position point, and the last discrete path point is the target position point;

[0020] S303. Collect the real-time movement obstacle data, analyze multiple obstacle position points according to the real-time movement obstacle data, and further convert to obtain the coordinate values corresponding to the multiple obstacle position points in the movement space coordinate system;

[0021] S304. Based on the coordinate values corresponding to the discrete path points and the coordinate values corresponding to the obstacle position points, search for the target nodes in the movement space coordinate system based on the A* algorithm, and perform parent node backtracking along each searched target node to form obstacle avoidance movement path data;

[0022] S305. Select 3 path position points from the obstacle avoidance movement path data, and further obtain the coordinate values corresponding to the 3 path position points in the movement space coordinate system;

[0023] S306. Based on the coordinate values corresponding to the 3 path position points in the movement space coordinate system, perform smoothing processing on the obstacle avoidance movement path data according to the quadratic Bezier curve to obtain the actual movement path data, where the formula of the quadratic Bezier curve is , specifically, 、 and are the coordinate values corresponding to the 3 path position points in the movement space coordinate system, the parameter t ranges from 0 to 1, and B(t) represents the coordinate value corresponding to each position point on the actual movement path data in the movement space coordinate system.

[0024] Further, in step S4, the actual operation path data obtained by analyzing the planned operation data and the real-time operation obstacle data specifically includes:

[0025] S401. Obtain the planned operation data, and obtain the spatial coordinate values corresponding to the preset operation path of the robotic arm in the operation space according to the planned operation data;

[0026] S402. Scan and obtain the spatial coordinate values corresponding to the obstacles in the operation space;

[0027] S403. According to the spatial coordinate values of the robotic arm in the operation space, obtain the spatial coordinate values corresponding to at least three robotic arm base points on the robotic arm 、 and , according to 、 and construct a robotic arm space packaging box; according to the spatial coordinate values of the obstacles in the operation space, obtain the spatial coordinate values corresponding to at least two obstacle base points of the obstacles and , according to and construct an obstacle space packaging box;

[0028] S404. By comparing whether there is an overlap in the projections of the robotic arm space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operation space, perform collision detection and matching on the robotic arm and the obstacle to determine whether there is a collision risk; when there is no overlap in the projections of the robotic arm space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operation space, it is determined that there is no collision risk, and the planned operation data is used as the actual operation path data; when there is an overlap in the projections of the robotic arm space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operation space, it is determined that there is a collision risk, and path optimization and adjustment are performed;

[0029] S405. Based on the RRT algorithm, perform obstacle avoidance path planning to obtain the actual operation path of the robotic arm in the operation space, and generate actual operation path data according to the actual operation path.

[0030] Further, in step S5, the steps for calculating the mobile energy consumption data based on the actual movement path data and the AGV operation parameters specifically include:

[0031] S501. Analyze the AGV power based on the AGV operation parameters;

[0032] S502. Analyze the AGV running trajectory length and AGV running speed based on the actual movement path data and AGV operation parameters, and calculate the AGV running time based on the AGV running trajectory length and AGV running speed;

[0033] S503. According to and calculate the mobile energy consumption data, where is the AGV power, L is the AGV running trajectory length, is the AGV running speed, is the AGV running time, is the mobile energy consumption;

[0034] In step S5, calculating the running energy consumption data based on the actual operation path data and the robotic arm operation parameters specifically includes the following steps:

[0035] S511. Analyze the average speed, frictional resistance, displacement, mass, running time, and joint driving power of each joint in the robotic arm based on the robotic arm operation parameters;

[0036] S512. Analyze the robotic arm running time based on the actual operation path data and the robotic arm operation parameters;

[0037] S513. Calculate the inertial energy consumption of the robotic arm through where is the inertial energy consumption of the robotic arm, m is the joint mass, is the average speed;

[0038] S514. Calculate the frictional energy consumption of the robotic arm through where is the frictional energy consumption of the robotic arm, is the frictional resistance, is the displacement;

[0039] S515. Calculate the electrical energy consumption of the robotic arm through where is the electrical energy consumption of the robotic arm, is the joint driving power, is the running time of the robotic arm;

[0040] S516. Calculate the running energy consumption data of the robotic arm according to where the running energy consumption data is the sum of the inertial energy consumption, frictional energy consumption, and electrical energy consumption.

[0041] Furthermore, in step S6, analyzing the state of charge data of the lithium battery of the mobile robot based on the Kalman filter specifically includes the following steps:

[0042] S601. Set the initial state vector and initial error covariance of the lithium battery. Among them, the initial state vector is , and the initial error covariance is , is the initial state vector of the lithium battery at time step k = 0, is the initial state of charge value, is the initial voltage state value, is the initial current state value, is the initial error covariance vector of the lithium battery at time step k = 0, is the state of charge uncertainty value, is the voltage state uncertainty value, is the current state uncertainty value;

[0043] S602. Perform state prediction according to the state prediction equation and prediction error covariance, and analyze the state prediction state vector and error covariance vector one step forward to obtain the state prediction state vector and error covariance vector of the lithium battery at time step k - 1. Among them, the state prediction equation is , and the prediction error covariance is , A is the state transition matrix, B is the control matrix, is the control input value at time step k - 1, is the covariance matrix of the process noise, T represents matrix transpose, is the prediction error covariance corresponding to time step K, is the prediction error covariance corresponding to time step K - 1, is the state prediction equation corresponding to time step K, is the state prediction equation corresponding to time step K - 1;

[0044] S603. Perform Kalman filter gain update to obtain the Kalman filter gain. Among them, the Kalman filter gain is , H is the observation matrix, and R is the covariance matrix of the observation noise;

[0045] S604. Perform state update and error covariance update based on the Kalman filter gain to obtain the updated state vector and updated error covariance vector of the lithium battery. Among them, the updated state vector of the lithium battery is , is the voltage observation value or current observation value of the current time step of the lithium battery, and the updated error covariance vector is , is the identity matrix;

[0046] At each time step k, the state prediction and Kalman filter gain update are repeated, and the state of charge data of the lithium battery corresponding to each time step k is output.

[0047] Correspondingly, a mobile robot energy management system, the system includes:

[0048] A planning data acquisition module, configured to acquire planned movement data and planned operation data, where the planned movement data is used to control the mobile robot to perform a movement operation in a preset movement space according to a preset movement instruction, and the planned operation data is used to control the mobile robot to perform an operation in a preset operation space according to a preset operation instruction;

[0049] A real-time data acquisition module, configured to acquire real-time mobile obstacle data and real-time operation obstacle data, where the real-time mobile obstacle data includes real-time two-dimensional data of obstacles in the movement space, and the real-time operation obstacle data includes real-time three-dimensional data of obstacles in the operation space;

[0050] An actual movement path analysis module, configured to analyze actual movement path data based on the planned movement data and the real-time mobile obstacle data, where the actual movement path data is used to control the mobile robot to perform a movement operation in the preset movement space according to an optimized movement instruction;

[0051] An actual operation path analysis module, configured to analyze actual operation path data based on the planned operation data and the real-time operation obstacle data, where the actual operation path data is used to control the mobile robot to perform an operation in the preset operation space according to an optimized operation instruction;

[0052] An energy consumption analysis module, configured to calculate mobile energy consumption data based on the actual movement path data and the AGV operation parameters, calculate operation energy consumption data based on the actual operation path data and the robotic arm operation parameters, and obtain overall energy consumption data by summing the mobile energy consumption data and the operation energy consumption data, where the overall energy consumption data is the total energy consumption of the robot required to execute the instructions included in the actual movement path data and the actual operation path data;

[0053] A state of charge analysis module, configured to analyze the state of charge data of the lithium battery of the mobile robot based on Kalman filtering, where the state of charge data includes the remaining power before operation of the lithium battery, and the remaining power before operation is the remaining power of the mobile robot before performing the movement operation and the operation;

[0054] The first energy management module is used to perform energy management before executing the moving operation and the running operation according to the total energy consumption of the robot and the remaining power before operation; when the remaining power before operation > the total energy consumption of the robot, the energy management analysis result is to execute the running operation, and control the mobile robot to execute the moving operation and the running operation; when the remaining power before operation ≤ the total energy consumption of the robot, the energy management analysis result is to execute the charging operation, and control the mobile robot to execute the charging operation;

[0055] The execution module is used to control the mobile robot to execute the moving operation within the preset moving space according to the optimized moving instruction, and control the mobile robot to execute the running operation within the preset operation space according to the optimized running instruction until all the moving operations and running operations are completed;

[0056] The second energy management module is used to collect the post-operation status data of the lithium battery of the mobile robot, calculate the remaining power after operation of the lithium battery according to the post-operation status data, compare it with the preset charging threshold, and judge whether the mobile robot needs to execute the charging operation, so as to perform energy management after executing the moving operation and the running operation; the post-operation status data includes voltage, current, and temperature, and the remaining power after operation is the remaining power of the mobile robot after executing the moving operation and the running operation.

[0057] Furthermore, the actual moving path analysis module specifically includes:

[0058] The moving space coordinate establishment unit is used to establish a moving space coordinate system within the preset moving space, and the moving space coordinate system is a two-dimensional Cartesian coordinate system;

[0059] The moving coordinate calculation unit is used to obtain the planned moving data, analyze multiple discrete path points of the planned moving path according to the planned moving data, and further convert to obtain the coordinate values corresponding to the multiple discrete path points in the moving space coordinate system, where the first discrete path point is the initial position point and the last discrete path point is the target position point;

[0060] The moving obstacle coordinate acquisition unit collects the real-time data of the moving obstacles, analyzes multiple obstacle position points according to the real-time data of the moving obstacles, and further converts to obtain the coordinate values corresponding to the multiple obstacle position points in the moving space coordinate system;

[0061] The moving obstacle avoidance path analysis unit is used to search for the target nodes in the moving space coordinate system based on the A* algorithm according to the coordinate values corresponding to the discrete path points and the coordinate values corresponding to the obstacle position points, and perform parent node backtracking along each searched target node to form the obstacle avoidance moving path data;

[0062] A moving path position selection unit for selecting 3 path position points from the obstacle avoidance moving path data and further obtaining the coordinate values of the 3 path position points corresponding to the moving space coordinate system;

[0063] A moving path smoothing processing unit for smoothing the obstacle avoidance moving path data based on the coordinate values of the 3 path position points corresponding to the moving space coordinate system according to the quadratic Bezier curve to obtain the actual moving path data, where the formula of the quadratic Bezier curve is , specifically, 、 and are the coordinate values of the 3 path position points corresponding to the moving space coordinate system, the parameter t ranges from 0 to 1, and B(t) represents the coordinate value of each position point on the actual moving path data corresponding to the moving space coordinate system.

[0064] Furthermore, the actual operation path analysis module specifically includes:

[0065] A planned operation data acquisition unit for acquiring planned operation data and obtaining the coordinate values corresponding to the preset operation path of the robotic arm in the operation space according to the analysis of the planned operation data;

[0066] An operation obstacle coordinate acquisition unit for scanning and acquiring the coordinate values corresponding to the obstacles in the operation space;

[0067] A packaging box construction unit for obtaining the coordinate values corresponding to at least 3 robotic arm base points on the robotic arm according to the coordinate values of the robotic arm in the operation space 、 and , according to 、 and construct a robotic arm space packaging box; according to the coordinate values of the obstacles in the operation space, obtain the coordinate values corresponding to at least 2 obstacle base points of the obstacles and , according to and construct an obstacle space packaging box;

[0068] A projection analysis unit for performing collision detection and matching on the robotic arm and obstacles by comparing whether there is an overlap in the projections of the robotic arm spatial packaging box and the obstacle spatial packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operating space, and determining whether there is a collision risk; when there is no overlap in the projections of the robotic arm spatial packaging box and the obstacle spatial packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operating space, it is determined that there is no collision risk, and the planned operation data is used as the actual operation path data; when there is an overlap in the projections of the robotic arm spatial packaging box and the obstacle spatial packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operating space, it is determined that there is a collision risk, and path optimization and adjustment are performed.

[0069] An operation obstacle avoidance path planning unit for performing obstacle avoidance path planning based on the RRT algorithm to obtain the actual operation path of the robotic arm in the operating space, and generating actual operation path data according to the actual operation path.

[0070] Furthermore, the energy consumption analysis module specifically includes:

[0071] An AGV power analysis unit for analyzing the AGV power based on the AGV operation parameters;

[0072] An AGV moving time analysis unit for analyzing the AGV running track length and AGV running speed based on the actual moving path data and AGV operation parameters, and calculating the AGV running time based on the AGV running track length and AGV running speed;

[0073] An AGV moving energy consumption calculation unit for calculating the moving energy consumption data according to and wherein, is the AGV power, L is the AGV running track length, is the AGV running speed, is the AGV running time, is the moving energy consumption;

[0074] A robotic arm parameter acquisition unit for analyzing the average speed, frictional resistance, displacement, mass, running time, and joint driving power of each joint in the robotic arm based on the robotic arm operation parameters;

[0075] A robotic arm running time calculation unit for analyzing the robotic arm running time based on the actual operation path data and robotic arm operation parameters;

[0076] A robotic arm moment of inertia energy calculation unit for calculating the moment of inertia energy consumption of the robotic arm through wherein, is the moment of inertia energy consumption of the robotic arm, m is the joint mass, is the average speed;

[0077] A robotic arm friction energy calculation unit for calculating the friction energy consumption of the robotic arm through where is the friction energy consumption of the robotic arm, is the frictional resistance, is the displacement; is the displacement;

[0078] A robotic arm electrical energy consumption calculation unit for calculating the electrical energy consumption of the robotic arm through where is the electrical energy consumption of the robotic arm, is the joint drive power, is the joint drive power, is the operating time of the robotic arm;

[0079] A robotic arm operating energy consumption calculation unit for calculating the operating energy consumption data of the robotic arm according to where the operating energy consumption data is the sum of the kinetic energy consumption, friction energy consumption and electrical energy consumption.

[0080] Furthermore, the state of charge analysis module specifically includes:

[0081] An initial condition setting unit for setting the initial state vector and initial error covariance of the lithium battery, where the initial state vector is and the initial error covariance is , is the initial state vector of the lithium battery at time step k = 0, is the initial state of charge value, is the initial voltage state value, is the initial current state value, is the initial error covariance vector of the lithium battery at time step k = 0, is the state of charge uncertainty value, is the voltage state uncertainty value, is the current state uncertainty value;

[0082] A state prediction unit for performing state prediction according to the state prediction equation and prediction error covariance, and analyzing the state prediction state vector and error covariance vector one step forward to obtain the state prediction state vector and error covariance vector of the lithium battery at time step k - 1, where the state prediction equation is , and the prediction error covariance is , A is the state transition matrix, B is the control matrix, is the control input value at time step k - 1, is the covariance matrix of the process noise, T represents matrix transpose, is the prediction error covariance corresponding to time step K, is the prediction error covariance corresponding to time step K - 1, is the state prediction equation corresponding to time step K, is the state prediction equation corresponding to time step K - 1;

[0083] The Kalman filter gain update unit is used to perform Kalman filter gain update to obtain the Kalman filter gain, where the Kalman filter gain is , H is the observation matrix, and R is the covariance matrix of the observation noise;

[0084] The update execution unit is used to perform state update and error covariance update based on the Kalman filter gain to obtain the updated state vector and updated error covariance vector of the lithium battery. Among them, the updated state vector of the lithium battery is , is the voltage observation value or current observation value of the lithium battery at the current time step, and the updated error covariance vector is , is the identity matrix;

[0085] The state of charge acquisition unit is used to repeatedly perform state prediction and Kalman filter gain update at each time step k, and output the state of charge data of the lithium battery corresponding to each time step k.

[0086] Correspondingly, a storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the mobile robot energy management method as described above.

[0087] Compared with the prior art, the beneficial effects of the technical solution are as follows: After adjusting the planned movement data and planned operation data to obtain the actual movement path data and actual operation path data, analyze and obtain the total energy consumption of the robot, and perform energy management evaluations before and after the task is executed to ensure that the mobile robot can complete the task smoothly and continuously, and avoid the phenomena of overcharging or power-off during the task execution due to improper power management. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 is a schematic flow chart of the mobile robot energy management method of the present invention.

[0089] Figure 2 is a schematic structural diagram of the mobile robot energy management system of the present invention.

[0090] In the figure, the components represented by each reference numeral are as follows:

[0091] Planning data acquisition module 1, real-time data acquisition module 2, actual movement path analysis module 3, actual operation path analysis module 4, energy consumption analysis module 5, state of charge analysis module 6, first energy management module 7, execution module 8, second energy management module 9. Detailed implementation manners

[0092] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0093] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "front", "rear", "left", "right", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0094] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. When a component is referred to as "fixed to" or "disposed on" another element, it can be directly on another component or there may also be an intermediate component. When a component is considered to be "connected" to another element, it can be directly connected to another element or there may be an intermediate element at the same time. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0095] Industrial robots can replace humans to complete simple and repetitive tasks in the industrial production process, and can serve product quality and production efficiency while ensuring operation safety.

[0096] Classified by whether the position is fixed, industrial robots can be divided into fixed robots and mobile robots. Fixed robots are set on the production line. The production line drives the products or components to move. When reaching a specific position of the fixed robot, the fixed robot performs specific operations according to the preset operation instructions. Mobile robots, on the other hand, can move freely within a certain area to complete tasks such as picking up parts, assembling, and transporting. This technical solution is optimized based on mobile robots.

[0097] Mobile robots generally need to have a long moving distance and need to cooperate with a robotic arm for fine operations. Most of these robots come with an independent energy source. However, due to their functions of movement and operation, they consume a large amount of energy during the production process and generally have a weak endurance. How to manage the energy of mobile robots and solve the "endurance anxiety" of mobile robots during the production process is an urgent problem to be solved in the industry.

[0098] As Figure 1 shown, to solve the above technical problems, this technical solution provides an energy management method, system and storage medium for mobile robots. Before performing a task, the required energy consumption is obtained by analyzing the mobile energy consumption data and operating energy consumption data required by the mobile robot, and the battery margin of the lithium battery is analyzed through the state of charge data, so as to determine whether the lithium battery can support the mobile robot to complete the task in one go; after the task is completed, the remaining power of the lithium battery after operation is calculated according to the state data after operation, and it is judged whether the mobile robot needs to perform a charging operation to make preparations for the next task in advance. Based on the above solution, the energy of the mobile robot is managed to solve the "endurance anxiety" of the mobile robot during the production process.

[0099] As Figure 1 shown, an energy management method for a mobile robot includes the following steps:

[0100] S1. Obtain the planned movement data and planned operation data. The planned movement data is used to control the mobile robot to perform movement operations in a preset movement space according to a preset movement instruction, and the planned operation data is used to control the mobile robot to perform operation operations in a preset operation space according to a preset operation instruction. Structurally, the mobile robot includes two major functional components: an AGV and a robotic arm. The AGV is responsible for moving in the movement space, while the robotic arm is responsible for operating in the operation space. In step S1, the planned movement data is the movement path data pre-planned according to the actual situation of the movement space. The mobile robot is carried on an AGV. Ideally, the AGV can move according to the instruction in the two-dimensional movement space from one point to another point, and the AGV reaching from one point to another point is the result of executing the instruction contained in the planned movement data; similarly, the planned operation data is the operation path data pre-planned according to the actual situation of the operation space. By executing the planned operation data, the robotic arm can be controlled to operate in a specific manner to complete the refined actions required for production.

[0101] S2. Obtain real-time mobile obstacle data and real-time operation obstacle data. The real-time mobile obstacle data includes real-time two-dimensional data of obstacles in the mobile space, and the real-time operation obstacle data includes real-time three-dimensional data of obstacles in the operation space. In step S2, the real-time mobile obstacle data reflects the real-time position of obstacles in the mobile space, and the real-time operation obstacle data reflects the real-time position of obstacles in the operation space. The real-time mobile obstacle data and the real-time operation obstacle data can be obtained based on an industrial binocular camera.

[0102] S3. Analyze the planned mobile data and the real-time mobile obstacle data to obtain actual mobile path data, which is used to control the mobile robot to move and perform mobile operations in the preset mobile space according to the optimized mobile instructions. The planned mobile data can control the movement of the AGV under ideal conditions. However, due to the presence of obstacles in the mobile space, the planned mobile data is no longer applicable. In step S3, by combining the planned mobile data and the real-time mobile obstacle data, the actual mobile path data can be calculated. Through the actual mobile path data, the influence of obstacles on the AGV can be overcome, enabling the AGV to make minor adjustments based on the planned mobile path and complete the established actions.

[0103] S4. Analyze the planned operation data and the real-time operation obstacle data to obtain actual operation path data, which is used to control the mobile robot to perform operation operations in the preset operation space according to the optimized operation instructions. The principle of step S4 is similar to that of step S3. The planned operation data can control the operation of the robotic arm under ideal conditions. However, due to the presence of obstacles in the operation space, the planned operation data is no longer applicable. In step S4, by combining the planned operation data and the real-time operation obstacle data, the actual operation path data can be calculated. Through the actual operation path data, the influence of obstacles on the robotic arm can be overcome, enabling the robotic arm to make minor adjustments based on the planned operation data and complete the established actions.

[0104] S5. Calculate the mobile energy consumption data based on the actual mobile path data and the AGV operation parameters, calculate the operation energy consumption data based on the actual operation path data and the robotic arm operation parameters, and sum the mobile energy consumption data and the operation energy consumption data to obtain the overall energy consumption data. The overall energy consumption data is the total energy consumption of the robot required to execute the instructions included in the actual mobile path data and the actual operation path data. The function of step S5 is to calculate the energy consumption of the AGV and the robotic arm under actual working conditions based on the actual mobile path data and the AGV operation parameters, the actual operation path data and the robotic arm operation parameters. The mobile energy consumption data corresponds to the AGV, and the operation energy consumption data corresponds to the robotic arm. Then, the overall energy consumption data can be calculated based on the mobile energy consumption data and the operation energy consumption data.

[0105] S6. Analyze the state of charge data of the lithium battery of the mobile robot based on Kalman filtering. The state of charge data includes the remaining power before operation of the lithium battery, and the remaining power before operation is the remaining power before the mobile robot executes the moving operation and the running operation. The function of step S6 is to calculate the remaining power of the mobile robot before executing the task through Kalman filtering analysis.

[0106] S7. Perform energy management before executing the moving operation and the running operation according to the total energy consumption of the robot and the remaining power before operation; when the remaining power before operation > the total energy consumption of the robot, the result of energy management analysis is to execute the running operation, and control the mobile robot to execute the moving operation and the running operation; when the remaining power before operation ≤ the total energy consumption of the robot, the result of energy management analysis is to execute the charging operation, and control the mobile robot to execute the charging operation. The function of step S7 is to judge whether the current power can support the completion of the task before the mobile robot executes the task by comparing the remaining power before operation and the total energy consumption of the robot, and perform corresponding control: if the current power can support the completion of the task, execute the current task, if the current power cannot support the completion of the task, execute the charging task.

[0107] S8. Control the mobile robot to execute the moving operation within the preset moving space according to the optimized moving instruction, and control the mobile robot to execute the running operation within the preset operation space according to the optimized running instruction until all the moving operations and running operations are completed. When the current power of the mobile robot can support the completion of the task, step S8 is executed, the AGV executes the moving operation, and the robotic arm executes the running operation until the task is completed.

[0108] S9. Collect the post-operation state data of the lithium battery of the mobile robot, calculate the remaining power after operation of the lithium battery according to the post-operation state data, compare it with the preset charging threshold, and judge whether the mobile robot needs to execute the charging operation, so as to perform energy management after executing the moving operation and the running operation; the post-operation state data includes voltage, current, and temperature, and the remaining power after operation is the remaining power after the mobile robot executes the moving operation and the running operation. In step S9, when the mobile robot completes the task, it is judged whether charging is required based on the remaining power and the charging threshold, so as to facilitate the execution of the next task. Among them, the charging threshold can be adjusted and set according to the factory parameters, experimental monitoring and actual measurement.

[0109] Based on the above technical solutions, after adjusting the planned movement data and planned operation data to obtain the actual movement path data and actual operation path data, analyze and obtain the total energy consumption of the robot, and perform energy management evaluation before and after executing the task to ensure that the mobile robot can complete the task smoothly and continuously, and avoid the phenomenon of overcharging or power interruption during the execution of the task due to improper power management.

[0110] Preferably, in step S3, according to the planned movement data and the real-time movement obstacle data, the analysis to obtain the actual movement path data specifically includes:

[0111] S301. Establish a movement space coordinate system within a preset movement space, and the movement space coordinate system is a two-dimensional Cartesian coordinate system. In step S301, the movement space is a two-dimensional plane. According to the actual situation, a movement space coordinate system is established in the movement space, and subsequent operations such as path planning and path optimization are based on the movement space coordinate system.

[0112] S302. Obtain the planned movement data, analyze multiple discrete path points of the planned movement paths according to the planned movement data, and further convert to obtain the coordinate values corresponding to the multiple discrete path points in the movement space coordinate system. Among them, the first discrete path point is the initial position point, and the last discrete path point is the target position point. In step S302, based on the established movement space coordinate system, obtain the coordinate values corresponding to the multiple discrete path points in the planned movement data.

[0113] S303. Collect the real-time movement obstacle data, analyze multiple obstacle position points according to the real-time movement obstacle data, and further convert to obtain the coordinate values corresponding to the multiple obstacle position points in the movement space coordinate system. In step S302, collect the obstacle position points through an industrial binocular camera, and based on the established movement space coordinate system, obtain the coordinate values corresponding to the obstacle position points.

[0114] S304. Based on the coordinate values corresponding to the discrete path points and the coordinate values corresponding to the obstacle position points, search for the target nodes in the movement space coordinate system based on the A* algorithm, and perform parent node backtracking along each searched target node to form the obstacle avoidance movement path data. The process of analyzing the obstacle avoidance movement path data based on the A* algorithm generally includes: 1. Initialization: Create an open list, that is, the OPEN table, for storing nodes to be checked; create a closed list, that is, the CLOSE table, for storing nodes that have been checked; add the starting point to the OPEN table and set its G value to 0, where the G value represents the actual cost from the starting point to the current point. 2. Define the heuristic function: The heuristic function is used to estimate the distance from the current node to the target node. Usually, in a two-dimensional grid, the Euclidean distance, Manhattan distance, or Chebyshev distance can be used as the heuristic function. 3. Algorithm loop: When the OPEN table is not empty, repeat the following steps: Select a node from the OPEN table, usually select the node with the smallest F value, where F = + , where is the actual cost from the starting point to the current point, It is the cost estimated by the heuristic function. Move the selected node from the OPEN list to the CLOSE list. If this node is the target node, the algorithm ends, and the shortest path is found by backtracking. 4. Expand the node: Expand the selected node, that is, check all its adjacent nodes. For each adjacent node: If it is an obstacle or already in the CLOSE list, ignore it. If it is not in the OPEN list, calculate its value and value, where the value represents the actual cost from the starting point to this node, and the value represents the cost estimated by the heuristic function, add it to the OPEN list, and set its parent node as the current node. If it is already in the OPEN list, but the value that can be reached through the current node is smaller, then update its value, parent node, and recalculate its F value. 5. Reorder the OPEN list: Sort the nodes in the OPEN list according to the F value so as to select the node with the smallest F value in the next loop. 6. Backtrack to generate the path: When the target node is added to the CLOSE list, the algorithm ends. Generate the shortest path by starting from the target node and backtracking along the parent node of each node until reaching the starting point. In step S304, based on the A* algorithm analysis, the obstacle avoidance movement path data is obtained, which can combine the actual situation of the obstacle position points on the basis of planning the movement data, and efficiently form the shortest travel path, which is beneficial to quickly adapt to the movement environment during the implementation process.

[0115] S305. Select 3 path position points from the obstacle avoidance movement path data, and further obtain the coordinate values of the 3 path position points corresponding to the movement space coordinate system. In step S305, taking the movement space coordinate system as the reference for the path position points, obtain their corresponding coordinate axes in the movement space coordinate system.

[0116] S306. Based on the coordinate values of the 3 path position points corresponding to the movement space coordinate system, smooth the obstacle avoidance movement path data according to the quadratic Bezier curve to obtain the actual movement path data, where the formula of the quadratic Bezier curve is , specifically, , and are the coordinate values of the 3 path position points corresponding to the movement space coordinate system, the parameter t ranges from 0 to 1, and B(t) represents the coordinate value of each position point on the actual movement path data corresponding to the movement space coordinate system. In step 306, smoothing the obstacle avoidance movement path data through the quadratic Bezier curve can simplify the calculation and analysis process, reduce the data processing cost, and is beneficial to quickly form the actual movement path data.

[0117] Based on the above technical solution, the obstacle avoidance movement path data is formed by backtracking the parent node based on the A* algorithm, and the actual movement path data is obtained through smoothing processing. The preset planned movement data can be optimized to ensure that the mobile robot is not affected by obstacles on the route during operation, while ensuring the operability of the movement path.

[0118] Preferably, in step S4, the actual operation path data obtained by analyzing the planned operation data and the real-time operation obstacle data specifically includes:

[0119] S401. Obtain the planned operation data, and obtain the spatial coordinate values ​​corresponding to the preset operation path of the robot arm in the operation space according to the planned operation data analysis. In step S401, the operation space of the robot arm is a three-dimensional space. Correspondingly, based on the planned operation data, the planned operation data is converted into spatial coordinate values ​​in the three-dimensional space, and then the three-dimensional space coordinate system is used for path planning, path optimization and other operations.

[0120] S402. Scan and obtain the spatial coordinate values ​​corresponding to the obstacles in the operating space. In step S402, the obstacle position points are collected by an industrial binocular camera, and the spatial coordinate values ​​corresponding to the obstacle position points are obtained based on the established three-dimensional space of the robot arm operation. It should be noted that this spatial coordinate value should include three values ​​of the X-axis, Y-axis and Z-axis.

[0121] S403. According to the spatial coordinate values ​​of the robot arm in the operating space, obtain the spatial coordinate values ​​corresponding to at least three robot arm base points on the robot arm , and ,according to , and Construct a robotic arm space packaging box; obtain the spatial coordinate values ​​corresponding to at least two obstacle base points of the obstacle based on the spatial coordinate values ​​of the obstacle in the operating space and ,according to and Constructing obstacle space packaging box. In this technical solution, a collision detection model is established to analyze the planned operation data and the real-time data of the operation obstacle, so as to make a collision risk judgment. Specifically, in step S403, the collision risk judgment is made by establishing an axis-aligned bounding box.

[0122] S404. Collision detection and matching are performed on the robotic arm and the obstacle by comparing whether there is an overlap in the projections of the robotic arm spatial packaging box and the obstacle spatial packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operating space, and it is judged whether there is a collision risk; when there is no overlap in the projections of the robotic arm spatial packaging box and the obstacle spatial packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operating space, it is judged that there is no collision risk, and the planned operation data is used as the actual operation path data; when there is an overlap in the projections of the robotic arm spatial packaging box and the obstacle spatial packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operating space, it is judged that there is a collision risk, and path optimization and adjustment are performed. In step S404, collision monitoring is carried out through the axis-aligned bounding box, and there are two specific situations in the results. One is that there is a collision risk, and the other is that there is no collision risk: when there is no collision risk, no adjustment is required, and the planned operation data is directly used as the actual operation path data; when there is a collision risk, step S405 is executed for path adjustment.

[0123] S405. Perform obstacle avoidance path planning based on the RRT algorithm to obtain the actual operation path of the robotic arm in the operation space, and generate actual operation path data according to the actual operation path. The following presents a specific embodiment of obstacle avoidance path planning based on the RRT algorithm, which specifically includes the following steps: 1. Initialize the RRT tree: Select the starting point as the root node of the tree and add it to the RRT tree; set a maximum number of iterations k to control the search process. 2. Generate a random point: Randomly sample a point in the environment as the target point Q_rand; if Q_rand is inside an obstacle, regenerate the random point until Q_rand is in an obstacle-free area. 3. Find the nearest node: Find the node in the RRT tree that is closest to Q_rand, denoted as Q_near. 4. Expand the tree: Expand a distance d from Q_near towards Q_rand to obtain a new node Q_new, ensuring that the line connecting Q_new and Q_near does not intersect with any obstacles; where d is usually a fixed step size or a variable step size based on a certain strategy. 5. Add a new node: If Q_new is a new node that has not been added to the tree before, add it to the RRT tree and connect Q_new to Q_near. 6. Check the target point: If Q_new is close to or reaches the target point, stop the search and backtrack the path from the starting point to the target point; otherwise, return to step 2 and continue the search. 7. Iteration and termination: Repeat steps 2 to 6 until a path to the target point is found or the maximum number of iterations k is reached, obtaining the actual operation path data. It should be noted that this embodiment only provides an implementation idea at the program control level. When analyzing the actual operation path data, this purpose can also be achieved through other means. There are various different solutions to this technical problem in the industry, and this technical solution will not be listed one by one here.

[0124] Bounding box collision detection is a fast and effective preliminary collision detection method. Based on the above technical solution, it is used to judge the collision risk by comparing whether there is an overlap in the projections of the robotic arm space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis. If there is a collision risk, obstacle avoidance path planning is performed through the RRT algorithm, which can quickly eliminate the situation where no collision occurs and provide a basis for subsequent detailed collision detection, avoiding the influence of obstacles in the space on the robotic arm during operation.

[0125] Preferably, in step S5, calculating the mobile energy consumption data based on the actual movement path data and the AGV operation parameters specifically includes the following steps:

[0126] S501. Analyze the AGV power based on the AGV operation parameters. In step S501, the AGV operation parameters can be obtained through the factory parameters of the AGV or the parameters collected during multiple actual operations, and the AGV power can be further analyzed based on the AGV operation parameters.

[0127] S502. Analyze the AGV running track length and AGV running speed based on the actual movement path data and AGV operation parameters, and calculate the AGV running time based on the AGV running track length and AGV running speed. In step S502, the AGV running track length can be analyzed through the actual movement path data, and the AGV running speed can be analyzed through the analysis of AGV operation parameters. Based on the existing data, the AGV running time can be calculated.

[0128] S503. According to and calculate the mobile energy consumption data, where is the AGV power, L is the AGV running track length, is the AGV running speed, is the AGV running time, is the mobile energy consumption.

[0129] Based on the above technical solution, it is possible to estimate the mobile energy consumption data of the AGV by collecting some basic data to judge the required energy consumption before moving.

[0130] Preferably, in step S5, calculating the operation energy consumption data based on the actual operation path data and the robotic arm operation parameters specifically includes the following steps:

[0131] S511. Analyze the average speed, frictional resistance, displacement, mass, operation time, and joint driving power of each joint in the robotic arm based on the robotic arm operation parameters. In step S511, based on the factory parameters of the robotic arm or the parameters collected during multiple actual operations, parameters such as the average speed, frictional resistance, displacement, mass, operation time, and joint driving power of each joint can be obtained.

[0132] S512. Analyze the robotic arm operation time based on the actual operation path data and the robotic arm operation parameters. In step S512, considering that the robotic arm will not have large-angle direction changes during normal applications, the robotic arm operation model can be simplified to a planar operation model to analyze and calculate the robotic arm operation time.

[0133] S513. Calculate the energy consumption of the robotic arm's moment of inertia through where is the energy consumption of the robotic arm's moment of inertia, m is the joint mass, is the average speed. During the movement of the robotic arm, energy is consumed due to inertia, and this part of the energy is related to the mass, speed, and acceleration of the robotic arm. In step S513, the energy consumption of the moment of inertia is calculated through the kinetic energy formula.

[0134] S514. Through Calculate the frictional energy consumption of the robotic arm, where is the frictional energy consumption of the robotic arm, is the frictional resistance, is the displacement.

[0135] S515. Through Calculate the electrical energy consumption of the robotic arm, where is the electrical energy consumption of the robotic arm, is the joint drive power, is the operating time of the robotic arm. The electrical energy consumption of the robotic arm depends on the efficiency of the motor, driver, and other electrical components. The energy consumption of the robotic arm motor is usually related to its power and working time. In step S515, the electrical energy consumption is calculated through the power formula.

[0136] S516. According to Calculate the operating energy consumption data of the robotic arm, where the operating energy consumption data is the sum of the kinetic energy consumption, frictional energy consumption, and electrical energy consumption.

[0137] Through the above technical solution, the mobile energy consumption data of the mobile robot is calculated based on the AGV power and the AGV operating time; by analyzing the kinetic energy consumption, frictional energy consumption, and electrical energy consumption of the robotic arm, the operating energy consumption data of the robotic arm can be calculated comprehensively and accurately, providing reliable data support for energy management.

[0138] Preferably, in step S6, obtaining the state of charge data of the lithium battery of the mobile robot based on Kalman filter analysis specifically includes the following steps:

[0139] S601. Set the initial state vector and the initial error covariance of the lithium battery, where the initial state vector is , and the initial error covariance is , is the initial state vector of the lithium battery at time step k = 0, is the initial state of charge value, is the initial voltage state value, is the initial current state value, is the initial error covariance vector of the lithium battery at time step k = 0, is the state of charge uncertainty value, is the voltage state uncertainty value, is the current state uncertainty value.

[0140] S602. Perform state prediction according to the state prediction equation and the prediction error covariance, and analyze the state prediction state vector and the error covariance vector one step forward to obtain the state prediction state vector and the error covariance vector of the lithium battery at time step k-1. Among them, the state prediction equation is , and the prediction error covariance is , A is the state transition matrix, B is the control matrix, is the control input value at time step k-1, is the covariance matrix of the process noise, T represents matrix transpose, is the prediction error covariance corresponding to time step K, is the prediction error covariance corresponding to time step K-1, is the state prediction equation corresponding to time step K, is the state prediction equation corresponding to time step K-1.

[0141] S603. Perform Kalman filter gain update to obtain the Kalman filter gain. Among them, the Kalman filter gain is , H is the observation matrix, and R is the covariance matrix of the observation noise.

[0142] S604. Based on the Kalman filter gain, perform state update and error covariance update to obtain the updated state vector and the updated error covariance vector of the lithium battery. Among them, the updated state vector of the lithium battery is , is the voltage observation value or current observation value of the lithium battery at the current time step, and the updated error covariance vector is , is the identity matrix.

[0143] S605. At each time step k, repeat state prediction and Kalman filter gain update, and output the state of charge data of the lithium battery corresponding to each time step k.

[0144] Generally speaking, the state of charge data of the lithium battery of the mobile robot obtained based on Kalman filter analysis is roughly divided into steps such as setting initial conditions, one-step forward state prediction, Kalman filter gain update, and state update and error covariance update. Based on the above steps, the state of charge data is obtained through Kalman filter analysis, which can judge the battery margin of the lithium battery before the mobile robot executes the task. Subsequently, the current battery power of the mobile robot can be judged by comprehensively comparing the integrated mobile energy consumption data and the running energy consumption data with the state of charge data, so as to effectively manage energy.

[0145] Such as Figure 2As shown in the figure, a mobile robot energy management system is based on a mobile robot energy management device. The system includes a planning data acquisition module 1, a real-time data acquisition module 2, an actual movement path analysis module 3, an actual operation path analysis module 4, an energy consumption analysis module 5, a state of charge analysis module 6, a first energy management module 7, an execution module 8, and a second energy management module 9.

[0146] Specifically, the planning data acquisition module 1 is used to acquire planning movement data and planning operation data. The planning movement data is used to control the mobile robot to perform movement operations in a preset movement space according to a preset movement instruction. The planning operation data is used to control the mobile robot to perform operation operations in a preset operation space according to a preset operation instruction. The real-time data acquisition module 2 is used to acquire real-time movement obstacle data and real-time operation obstacle data. The real-time movement obstacle data includes real-time two-dimensional data of obstacles in the movement space. The real-time operation obstacle data includes real-time three-dimensional data of obstacles in the operation space. The actual movement path analysis module 3 is used to analyze and obtain actual movement path data based on the planning movement data and the real-time movement obstacle data. The actual movement path data is used to control the mobile robot to perform movement operations in the preset movement space according to the optimized movement instruction. The actual operation path analysis module 4 is used to analyze and obtain actual operation path data based on the planning operation data and the real-time operation obstacle data. The actual operation path data is used to control the mobile robot to perform operation operations in the preset operation space according to the optimized operation instruction. The energy consumption analysis module 5 is used to calculate movement energy consumption data based on the actual movement path data and the AGV operation parameters, calculate operation energy consumption data based on the actual operation path data and the robotic arm operation parameters, and obtain the overall energy consumption data by summing the movement energy consumption data and the operation energy consumption data. The overall energy consumption data is the total energy consumption of the robot required to execute the instructions included in the actual movement path data and the actual operation path data. The state of charge analysis module 6 is used to analyze and obtain the state of charge data of the lithium battery of the mobile robot based on Kalman filtering. The state of charge data includes the remaining power before operation of the lithium battery. The remaining power before operation is the remaining power of the mobile robot before performing movement operations and operation operations. The first energy management module 7 is used to perform energy management before performing movement operations and operation operations according to the total energy consumption of the robot and the remaining power before operation. When the remaining power before operation > the total energy consumption of the robot, the energy management analysis result is to perform operation operations, and control the mobile robot to perform movement operations and operation operations. When the remaining power before operation ≤ the total energy consumption of the robot, the energy management analysis result is to perform charging operations, and control the mobile robot to perform charging operations. The execution module 8 is used to control the mobile robot to perform movement operations in the preset movement space according to the optimized movement instruction, and control the mobile robot to perform operation operations in the preset operation space according to the optimized operation instruction until all movement operations and operation operations are completed.The second energy management module 9 is used to collect the post-operation status data of the lithium battery of the mobile robot, calculate the remaining post-operation power of the lithium battery according to the post-operation status data, compare it with a preset charging threshold, and determine whether the mobile robot needs to perform a charging operation, so as to perform energy management after performing a moving operation and an operating operation; the post-operation status data includes voltage, current, and temperature, and the remaining post-operation power is the remaining power of the mobile robot after performing a moving operation and an operating operation.

[0147] Preferably, the actual movement path analysis module specifically includes: a movement space coordinate establishment unit for establishing a movement space coordinate system within a preset movement space, and the movement space coordinate system is a two-dimensional Cartesian coordinate system; a movement coordinate calculation unit for obtaining planned movement data, analyzing a plurality of discrete path points of a plurality of planned movement paths according to the planned movement data, and further converting to obtain the coordinate values corresponding to the plurality of discrete path points in the movement space coordinate system, wherein the first discrete path point is the initial position point and the last discrete path point is the target position point; a movement obstacle coordinate acquisition unit for collecting real-time movement obstacle data, analyzing a plurality of obstacle position points according to the real-time movement obstacle data, and further converting to obtain the coordinate values corresponding to the plurality of obstacle position points in the movement space coordinate system; a movement obstacle avoidance path analysis unit for searching for target nodes in the movement space coordinate system based on the A* algorithm according to the coordinate values corresponding to the discrete path points and the coordinate values corresponding to the obstacle position points, and backtracking the parent nodes along each searched target node to form obstacle avoidance movement path data; a movement path position selection unit for selecting 3 path position points from the obstacle avoidance movement path data, and further obtaining the coordinate values corresponding to the 3 path position points in the movement space coordinate system; a movement path smoothing processing unit for smoothing the obstacle avoidance movement path data based on the coordinate values corresponding to the 3 path position points in the movement space coordinate system according to a quadratic Bezier curve to obtain actual movement path data, wherein the formula of the quadratic Bezier curve is , specifically, 、 and are the coordinate values corresponding to the 3 path position points in the movement space coordinate system, the parameter t ranges from 0 to 1, and B(t) represents the coordinate value corresponding to each position point on the actual movement path data in the movement space coordinate system.

[0148] Preferably, the actual operation path analysis module specifically includes: a planned operation data acquisition unit for acquiring planned operation data and obtaining the spatial coordinate values corresponding to the preset operation path of the robotic arm in the operation space according to the analysis of the planned operation data; an operation obstacle coordinate acquisition unit for scanning and obtaining the spatial coordinate values corresponding to the obstacles in the operation space; a packaging box construction unit for obtaining the spatial coordinate values corresponding to at least 3 robotic arm base points on the robotic arm according to the spatial coordinate values of the robotic arm in the operation space 、 and and constructing a robotic arm space packaging box according to 、 and ; obtaining the spatial coordinate values corresponding to at least 2 obstacle base points of the obstacle according to the spatial coordinate values of the obstacle in the operation space and and constructing an obstacle space packaging box according to and ; a projection analysis unit for performing collision detection and matching on the robotic arm and the obstacle by comparing whether there is an overlap in the projections of the robotic arm space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operation space, and determining whether there is a collision risk; when there is no overlap in the projections of the robotic arm space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operation space, it is determined that there is no collision risk, and the planned operation data is used as the actual operation path data; when there is an overlap in the projections of the robotic arm space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis of the three-dimensional coordinate system corresponding to the operation space, it is determined that there is a collision risk, and path optimization and adjustment are performed; an operation obstacle avoidance path planning unit for performing obstacle avoidance path planning based on the RRT algorithm to obtain the actual operation path of the robotic arm in the operation space and generating actual operation path data according to the actual operation path

[0149] Preferably, the energy consumption analysis module specifically includes: an AGV power analysis unit for analyzing the AGV power based on the AGV operation parameters; an AGV moving time analysis unit for analyzing the AGV running track length and AGV running speed based on the actual moving path data and AGV operation parameters, and calculating the AGV running time based on the AGV running track length and AGV running speed; an AGV moving energy consumption calculation unit for calculating the moving energy consumption data according to and , where is the AGV power, L is the AGV running track length, is the AGV running speed, is the AGV running time, is the mobile energy consumption; the robotic arm parameter acquisition unit is used to analyze the average speed, frictional resistance, displacement, mass, operating time, and joint driving power of each joint in the robotic arm based on the robotic arm operating parameters; the robotic arm operating time calculation unit is used to analyze the robotic arm operating time based on the actual operating path data and the robotic arm operating parameters; the robotic arm moment of inertia energy calculation unit is used to calculate the moment of inertia energy consumption of the robotic arm through where is the moment of inertia energy consumption of the robotic arm, m is the joint mass, is the average speed; the robotic arm frictional energy calculation unit is used to calculate the frictional energy consumption of the robotic arm through where is the frictional energy consumption of the robotic arm, is the frictional resistance, is the displacement; the robotic arm electrical energy consumption calculation unit is used to calculate the electrical energy consumption of the robotic arm through where is the electrical energy consumption of the robotic arm, is the joint driving power, is the operating time of the robotic arm; the robotic arm operating energy consumption calculation unit is used to calculate the operating energy consumption data of the robotic arm according to where the operating energy consumption data is the sum of the moment of inertia energy consumption, frictional energy consumption, and electrical energy consumption.

[0150] Preferably, the state of charge analysis module specifically includes: an initial condition setting unit for setting the initial state vector and initial error covariance of the lithium battery, where the initial state vector is and the initial error covariance is , is the initial state vector of the lithium battery at time step k = 0, is the initial state of charge value, is the initial voltage state value, is the initial current state value, is the initial error covariance vector of the lithium battery at time step k = 0, is the state of charge uncertainty value, is the voltage state uncertainty value, is the current state uncertainty value; a state prediction unit for performing state prediction according to the state prediction equation and prediction error covariance, and analyzing the state prediction state vector and error covariance vector one step forward to obtain the state prediction state vector and error covariance vector of the lithium battery at time step k - 1, where the state prediction equation is and the prediction error covariance is , A is the state transition matrix, B is the control matrix, is the control input value at time step k-1, is the covariance matrix of process noise, and T represents matrix transpose, is the predicted error covariance corresponding to time step K, is the predicted error covariance corresponding to time step K-1, is the state prediction equation corresponding to time step K, is the state prediction equation corresponding to time step K-1; a Kalman filter gain update unit, configured to perform Kalman filter gain update to obtain a Kalman filter gain, where the Kalman filter gain is , H is the observation matrix, and R is the covariance matrix of observation noise; an update execution unit, configured to perform state update and error covariance update based on the Kalman filter gain to obtain an updated state vector and an updated error covariance vector of the lithium battery, where the updated state vector of the lithium battery is , is the voltage observation value or current observation value of the lithium battery at the current time step, and the updated error covariance vector is , is the identity matrix; a state of charge acquisition unit, configured to repeat state prediction and Kalman filter gain update at each time step k, and output the state of charge data of the lithium battery corresponding to each time step k.

[0151] Correspondingly, the present technical solution further includes a storage medium storing a computer program, where the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the energy management method for a mobile robot as described above.

[0152] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above description, and all such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A mobile robot energy management method, characterized in that: The method comprises the following steps: S1. Acquire planned movement data and planned operation data; the planned movement data is used to control the mobile robot to perform movement operations in a preset movement space according to preset movement instructions, and the planned operation data is used to control the mobile robot to perform operation operations in a preset operation space according to preset operation instructions; S2. Obtaining real-time data of movement obstacles and real-time data of operation obstacles, wherein the real-time data of movement obstacles includes real-time two-dimensional data of obstacles in the movement space, and the real-time data of operation obstacles includes real-time three-dimensional data of obstacles in the operation space; S3. According to the planned movement data and the real-time movement obstacle data, the actual movement path data is analyzed and obtained, and the actual movement path data is used to control the mobile robot to move in a preset movement space according to the optimized movement instruction to perform the movement operation; S4. According to the planned operation data and the real-time data of the operation obstacle, the actual operation path data is analyzed, and the actual operation path data is used to control the mobile robot to perform the operation operation in the preset operation space according to the optimized operation instruction; S5. Calculate the movement energy consumption data based on the actual movement path data and the AVG operation parameters, calculate the operation energy consumption data based on the actual operation path data and the robot arm operation parameters, and obtain the overall energy consumption data by summing the movement energy consumption data and the operation energy consumption data, wherein the overall energy consumption data is the total energy consumption of the robot required for the robot to execute the instructions contained in the actual movement path data and the actual operation path data; S6. Based on Kalman filter analysis, the state of charge data of the lithium battery of the mobile robot is obtained, wherein the state of charge data includes the remaining power of the lithium battery before operation, and the remaining power before operation is the remaining power before the mobile robot performs the moving operation and the running operation; S7. Perform energy management before executing the moving operation and the running operation according to the total energy consumption of the robot and the remaining power before operation; when the remaining power before operation is greater than the total energy consumption of the robot, the energy management analysis result is to execute the running operation, and the mobile robot is controlled to execute the moving operation and the running operation; when the remaining power before operation is less than or equal to the total energy consumption of the robot, the energy management analysis result is to execute the charging operation, and the mobile robot is controlled to execute the charging operation; Among them, the mobile robot includes two major functional components: AGV and robotic arm. The AGV is responsible for completing movement in the mobile space, while the robotic arm is responsible for completing operations in the operation space. The mobile robot is carried on an AGV. Under ideal conditions, the AGV can move according to instructions in the two-dimensional mobile space, from one point to another, and the AGV reaching one point from another is the result of executing the instructions contained in the planned mobile data.

2. The method according to claim 1, characterized in that The method further comprises: S8. Control the mobile robot to perform a mobile operation in a preset mobile space according to the optimized mobile instruction, and control the mobile robot to perform an operation operation in a preset operation space according to the optimized operation instruction until all mobile operations and operation operations are completed; S9. Collect the post-operation status data of the mobile robot's lithium battery, calculate the post-operation remaining power of the lithium battery based on the post-operation status data, compare it with the preset charging threshold, and determine whether the mobile robot needs to perform a charging operation, so as to perform energy management after performing the moving operation and the running operation; the post-operation status data includes voltage, current, and temperature, and the post-operation remaining power is the remaining power of the mobile robot after performing the moving operation and the running operation.

3. The method according to claim 2, characterized in that In step S3, the actual movement path data is analyzed based on the planned movement data and the real-time movement obstacle data, specifically including: S301. Establishing a mobile space coordinate system in a preset mobile space, wherein the mobile space coordinate system is a two-dimensional Cartesian coordinate system; S302. Acquire planned movement data, analyze the planned movement data to obtain a plurality of discrete path points of the planned movement path, and further convert the coordinate values ​​of the plurality of discrete path points corresponding to the mobile space coordinate system, wherein the first discrete path point is the initial position point and the last discrete path point is the target position point; S303. Collecting real-time data of mobile obstacles, analyzing the real-time data of mobile obstacles to obtain multiple obstacle positions, and further converting the coordinate values ​​of the multiple obstacle positions corresponding to the mobile space coordinate system; S304. According to the coordinate values ​​corresponding to the discrete path points and the coordinate values ​​corresponding to the obstacle position points, the target node in the mobile space coordinate system is searched based on the A* algorithm, and the parent node is backtracked along each searched target node to form obstacle avoidance movement path data; S305. Select three path position points in the obstacle avoidance moving path data, and further obtain the coordinate values ​​corresponding to the three path position points in the moving space coordinate system; S306. Based on the coordinate values ​​corresponding to the three path position points in the mobile space coordinate system, the obstacle avoidance movement path data is smoothed according to the quadratic Bezier curve to obtain the actual movement path data, wherein the formula of the quadratic Bezier curve is B(t)=(1-t) 2 R0+2(1-t)tR1+t 2 R2, specifically, R0, R1 and R2 are the coordinate values ​​of the three path position points in the mobile space coordinate system, the parameter t ranges from 0 to 1, and B(t) represents the coordinate value of each position point on the actual mobile path data in the mobile space coordinate system.

4. The method according to any one of claims 2 to 3, characterized in that: In step S4, the actual operation path data obtained by analysis based on the planned operation data and the real-time operation obstacle data specifically includes: S401. Acquire the planned operation data, and obtain the spatial coordinate value corresponding to the preset operation path of the robot arm in the operation space according to the planned operation data analysis; S402. Scan and obtain the spatial coordinate values ​​corresponding to the obstacles in the operating space; S403. According to the spatial coordinate values ​​of the robot arm in the operating space, obtain the spatial coordinate values ​​(X a ,Y a ,Z a )、(X b ,Y b ,Z b ) and (X c ,Y c ,Z c ), according to (X a ,Y a ,Z a )、(X b ,Y b ,Z b ) and (X c ,Y c ,Z c ) Build a robotic arm space packaging box; According to the spatial coordinate values ​​of the obstacle in the operating space, obtain the spatial coordinate values ​​(X d ,Y d ,Z d ) and (X e ,Y e ,Z e ), according to (X d ,Y d ,Z d ) and (X e ,Y e ,Z e )Build the obstacle space packaging box; S404. By comparing whether there is overlap in the projections of the robot space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system corresponding to the operating space, collision detection and matching are performed on the robot and the obstacle to determine whether there is a collision risk; when there is no overlap in the projections of the robot space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system corresponding to the operating space, it is determined that there is no collision risk, and the planned operation data is used as the actual operation path data; when there is overlap in the projections of the robot space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system corresponding to the operating space, it is determined that there is a collision risk, and the path optimization adjustment is performed; S405. Perform obstacle avoidance path planning based on the RRT algorithm to obtain the actual operation path of the robot arm in the operation space, and generate actual operation path data based on the actual operation path.

5. The method according to any one of claims 2 to 3, characterized in that: In step S5, the mobile energy consumption data is calculated based on the actual mobile path data and the AGV operation parameters, specifically including the following steps: S501. Analyze AGV power based on AGV operating parameters; S502. Analyze the AGV running track length and the AGV running speed based on the actual moving path data and the AGV running parameters, and calculate the AGV running time based on the AGV running track length and the AGV running speed; S503. Calculate the mobile energy consumption data according to Q1=P1×T1 and T1=L / V1, where P1 is the AGV power, L is the AGV running track length, V1 is the AGV running speed, T1 is the AGV running time, and Q1 is the mobile energy consumption; In step S5, the operation energy consumption data is calculated based on the actual operation path data and the operation parameters of the robot arm, and specifically includes the following steps: S511. Analyze the average speed, friction resistance, displacement, mass, operation time and joint drive power of each joint in the robot arm based on the robot arm operation parameters; S512. Analyze the robot operation time based on the actual operation path data and the robot operation parameters; S513. Pass Calculate the inertial energy consumption of the robot arm, where E1 is the inertial energy consumption of the robot arm, m is the joint mass, is the average speed; S514. Through E2 = F f ×S calculates the friction energy consumption of the robot arm, where E2 is the friction energy consumption of the robot arm, F f is the friction resistance, S is the displacement; S515. Calculate the electrical energy consumption of the robot arm by E3=P2×T2, where E3 is the electrical energy consumption of the robot arm, P2 is the joint drive power, and T2 is the operating time of the robot arm; S516. Calculate the operating energy consumption data of the robot arm according to Q2=E1+E2+E3, wherein the operating energy consumption data Q2 is the sum of motion inertia energy consumption, friction energy consumption and electrical energy consumption.

6. The method according to any one of claims 2 to 3, characterized in that: In step S6, obtaining the state of charge data of the lithium battery of the mobile robot based on Kalman filter analysis specifically includes the following steps: S601. Set the initial state vector and initial error covariance of the lithium battery, where the initial state vector is The initial error covariance is is the initial state vector of the lithium battery at time step k = 0, SOC initial is the initial state of charge value, V initial is the initial voltage state value, I initial is the initial current state value, P 0|0 is the initial error covariance vector of the lithium battery at time step k = 0, σ 2 SOC is the uncertainty value of the state of charge, σ 2 V is the voltage state uncertainty value, σ 2 I is the uncertainty value of the current state; S602. Perform state prediction according to the state prediction equation and the prediction error covariance, analyze the state prediction state vector and the error covariance vector one step forward, and obtain the state prediction state vector and the error covariance vector of the lithium battery at the time step k-1, where the state prediction equation is The prediction error covariance is P k|k-1 =AP k-1|k-1 +A T +Q, A is the state transfer matrix, B is the control matrix, u k-1 is the control input value at time step k-1, Q is the covariance matrix of the process noise, T represents the matrix transpose, P k|k-1 is the prediction error covariance corresponding to time step K, P k-1|k-1 is the prediction error covariance corresponding to time step K-1, is the state prediction equation corresponding to time step K, is the state prediction equation corresponding to time step K-1; S603. Execute Kalman filter gain update to obtain Kalman filter gain, where Kalman filter gain is K k =P k|k-1 H T (HP k|k-1 H T +R) -1 , H is the observation matrix, R is the covariance matrix of the observation noise; S604. Perform state update and error covariance update based on Kalman filter gain to obtain an updated state vector and an updated error covariance vector of the lithium battery, wherein the updated state vector of the lithium battery is z k is the voltage observation value or current observation value of the lithium battery at the current time step, and the updated error covariance vector is P k|k =(IK k H)P k|k-1 , I is the identity matrix; S605. At each time step k, repeat the state prediction and Kalman filter gain update, and output the lithium battery state of charge data corresponding to each time step k.

7. A mobile robot energy management system, characterized in that: The system comprises: A planning data acquisition module, used to acquire planning movement data and planning operation data; the planning movement data is used to control the mobile robot to perform movement operations in a preset movement space according to preset movement instructions, and the planning operation data is used to control the mobile robot to perform operation operations in a preset operation space according to preset operation instructions; A real-time data acquisition module, used to acquire real-time data of movement obstacles and real-time data of operation obstacles, wherein the real-time movement obstacle data includes real-time two-dimensional data of obstacles in the movement space, and the real-time operation obstacle data includes real-time three-dimensional data of obstacles in the operation space; An actual movement path analysis module is used to analyze the planned movement data and the real-time movement obstacle data to obtain the actual movement path data, and the actual movement path data is used to control the mobile robot to move and perform movement operations in a preset movement space according to the optimized movement instructions; An actual operation path analysis module is used to analyze the actual operation path data obtained according to the planned operation data and the real-time operation obstacle data, and the actual operation path data is used to control the mobile robot to perform operation operations in a preset operation space according to the optimized operation instructions; An energy consumption analysis module is used to calculate the movement energy consumption data based on the actual movement path data and the AGV operation parameters, calculate the operation energy consumption data based on the actual operation path data and the robot arm operation parameters, and obtain the overall energy consumption data by summing the movement energy consumption data and the operation energy consumption data, wherein the overall energy consumption data is the total energy consumption of the robot required for the robot to execute the instructions contained in the actual movement path data and the actual operation path data; A state of charge analysis module, used to obtain the state of charge data of the lithium battery of the mobile robot based on Kalman filter analysis, wherein the state of charge data includes the remaining power of the lithium battery before operation, and the remaining power before operation is the remaining power before the mobile robot performs movement operation and operation operation; The first energy management module is used to perform energy management before executing the moving operation and the running operation according to the total energy consumption of the robot and the remaining power before the operation; when the remaining power before the operation is greater than the total energy consumption of the robot, the energy management analysis result is to execute the running operation, and the mobile robot is controlled to execute the moving operation and the running operation; when the remaining power before the operation is less than or equal to the total energy consumption of the robot, the energy management analysis result is to execute the charging operation, and the mobile robot is controlled to execute the charging operation; Among them, the mobile robot includes two major functional components: AGV and robotic arm. The AGV is responsible for completing movement in the mobile space, while the robotic arm is responsible for completing operations in the operation space. The mobile robot is carried on an AGV. Under ideal conditions, the AGV can move according to instructions in the two-dimensional mobile space, from one point to another, and the AGV reaching one point from another is the result of executing the instructions contained in the planned mobile data.

8. The system according to claim 7, characterized in that The system further comprises: An execution module, used to control the mobile robot to perform a movement operation in a preset movement space according to the optimized movement instruction, and to control the mobile robot to perform an operation operation in a preset operation space according to the optimized operation instruction, until all movement operations and operation operations are completed; The second energy management module is used to collect the post-operation status data of the mobile robot's lithium battery, calculate the post-operation remaining power of the lithium battery based on the post-operation status data, compare it with the preset charging threshold, and determine whether the mobile robot needs to perform a charging operation, so as to perform energy management after performing the moving operation and the running operation; the post-operation status data includes voltage, current, and temperature, and the post-operation remaining power is the remaining power of the mobile robot after performing the moving operation and the running operation.

9. The system according to claim 8, characterized in that The actual moving path analysis module specifically includes: A mobile space coordinate establishing unit, used to establish a mobile space coordinate system in a preset mobile space, wherein the mobile space coordinate system is a two-dimensional Cartesian coordinate system; A mobile coordinate calculation unit is used to obtain the planned mobile data, obtain a plurality of discrete path points of the planned mobile path according to the planned mobile data analysis, and further convert the coordinate values ​​corresponding to the plurality of discrete path points in the mobile space coordinate system, wherein the first discrete path point is the initial position point and the last discrete path point is the target position point; A mobile obstacle coordinate acquisition unit collects real-time data of mobile obstacles, obtains multiple obstacle position points according to the real-time data of mobile obstacles, and further converts the coordinate values ​​corresponding to the multiple obstacle position points in the mobile space coordinate system; A mobile obstacle avoidance path analysis unit is used to search for target nodes in the mobile space coordinate system based on the A* algorithm according to the coordinate values ​​corresponding to the discrete path points and the coordinate values ​​corresponding to the obstacle position points, and to perform parent node backtracking along each searched target node to form obstacle avoidance mobile path data; A moving path position selection unit, used to select three path position points in the obstacle avoidance moving path data, and further obtain the coordinate values ​​corresponding to the three path position points in the moving space coordinate system; The moving path smoothing processing unit is used to smooth the obstacle avoidance moving path data according to the coordinate values ​​corresponding to the three path position points in the moving space coordinate system according to the quadratic Bezier curve to obtain the actual moving path data, wherein the formula of the quadratic Bezier curve is B(t)=(1-t) 2 R0+2(1-t)tR1+t 2 R2, specifically, R0, R1 and R2 are the coordinate values ​​of the three path position points in the mobile space coordinate system, the parameter t ranges from 0 to 1, and B(t) represents the coordinate value of each position point on the actual mobile path data in the mobile space coordinate system; The actual operation path analysis module specifically includes: A planning operation data acquisition unit is used to acquire the planning operation data, and obtain the spatial coordinate value corresponding to the preset operation path of the robot arm in the operation space according to the planning operation data analysis; An operation obstacle coordinate acquisition unit is used to scan and obtain the space coordinate values ​​corresponding to obstacles in the operation space; The packaging box construction unit is used to obtain the spatial coordinate values ​​(X a ,Y a ,Z a )、(X b ,Y b ,Z b ) and (X c ,Y c ,Z c ), according to (X a ,Y a ,Z a )、(X b ,Y b ,Z b ) and (X c ,Y c ,Z c ) Build a robotic arm space packaging box; According to the spatial coordinate values ​​of the obstacle in the operating space, obtain the spatial coordinate values ​​(X d ,Y d ,Z d ) and (X e ,Y e ,Z e ), according to (X d ,Y d ,Z d ) and (X e ,Y e ,Z e )Build the obstacle space packaging box; The projection analysis unit is used to perform collision detection and matching between the robot and the obstacle by comparing whether there is overlap in the projections of the robot space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system corresponding to the operating space, and to determine whether there is a collision risk; when there is no overlap in the projections of the robot space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system corresponding to the operating space, it is determined that there is no collision risk, and the planned operation data is used as the actual operation path data; when there is overlap in the projections of the robot space packaging box and the obstacle space packaging box on the X-axis, Y-axis, and Z-axis in the three-dimensional coordinate system corresponding to the operating space, it is determined that there is a collision risk, and the path optimization adjustment is performed; The operation obstacle avoidance path planning unit is used to perform obstacle avoidance path planning based on the RRT algorithm, obtain the actual operation path of the robot arm in the operation space, and generate actual operation path data according to the actual operation path.

10. A storage medium or a computer program, characterized in that The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 6.

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