Inspection robot charging decision-making method and system

Abstract

The invention provides an inspection robot charging decision-making method and system, and particularly relates to the technical field of coal mine robots, and the method comprises the steps: collecting the value of a position encoder, the power supply voltage of a motor, the load current of the motor and the operation efficiency data of the motor through the forward and reverse driving of a robot on a track, and calculating the electric power and energy consumption; and a forward energy consumption map and a reverse energy consumption map are established. In the inspection process, the robot continuously collects data to calibrate and update the energy consumption map. And when the electric quantity is lower than a specified threshold value, according to the value of the position encoder and the position of the fixed charging pile, calculating the distance of moving to the nearest charging pile along the positive and negative directions and energy consumption, and selecting the charging pile with the lowest energy consumption as a charging target. According to the method, the technical problem that the energy utilization efficiency of the inspection robot is low in the coal mine guide rail environment with gradient changes is solved, so that the energy utilization efficiency is improved, the endurance time of the robot is prolonged, and continuous and stable execution of a coal mine inspection task is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine robots, and in particular to a charging decision method and system for an inspection robot. Background Art

[0002] With the growing demand for safe production in coal mines, coal mine inspection robots have gradually become an important tool for underground safety monitoring. These robots can operate for a long time in harsh environments such as high temperature, high humidity, dust and gas, perform various inspection tasks, and effectively improve the safety and efficiency of coal mine production. However, coal mine inspection robots also face many challenges in practical applications, especially in terms of endurance and energy management. Due to the limitations of battery capacity and energy management technology, robots are prone to interruption of tasks due to lack of power during long inspections, which not only affects the continuity and safety of coal mine production, but also restricts the widespread application of robots.

[0003] In response to the problem of insufficient battery life of coal mine inspection robots, traditional technology usually adopts the method of setting up charging piles at the starting and ending points of the track, and planning the charging path based on the shortest distance principle. When the robot's battery power is lower than the preset threshold, it will automatically select the nearest charging pile for charging. This method has good applicability on straight rails and can alleviate the robot's battery life pressure to a certain extent. However, in the guide rail environment with changing slopes underground in coal mines, traditional methods often ignore the relationship between energy consumption and path, causing the robot to consume a lot of energy during the uphill process, increasing total energy consumption, reducing energy utilization efficiency, and failing to meet the needs of energy saving and consumption reduction and extending battery life.

[0004] In summary, how to improve the energy utilization efficiency of inspection robots in a coal mine guide rail environment with slope changes is a core technical problem that needs to be solved urgently. Summary of the invention

[0005] The main purpose of the present invention is to provide a patrol robot charging decision method and system to solve the technical problem of low energy utilization efficiency of the patrol robot in a coal mine guide rail environment with slope changes, improve the energy utilization efficiency, extend the robot's battery life, and ensure the continuous and stable execution of coal mine inspection tasks.

[0006] In order to achieve the above objectives, the present invention provides a patrol robot charging decision method and system.

[0007] In a first aspect, the present invention provides a charging decision method for an inspection robot, the method comprising: By having the robot travel forward and backward on the track, the position encoder value, motor supply voltage, motor load current and motor operation efficiency data of the robot are collected to calculate the electric power and energy consumption of the robot; establishing forward and reverse energy consumption maps based on the electric power and the energy consumption; During the inspection process, the current position encoder value of the robot, the motor supply voltage, the motor load current and the motor operating efficiency data are continuously collected to calibrate and update the energy consumption map; When the battery level of the robot is lower than a specified threshold, the distance that the robot moves to the nearest charging pile in the forward direction and the reverse direction, and the corresponding energy consumption are calculated according to the position of the fixed charging pile, the position encoder value, and the calibrated and updated energy consumption map; The energy consumption in the forward direction and the reverse direction are compared, and the charging pile with the lowest energy consumption is selected as the charging target.

[0008] Optionally, by the robot running forward and backward on the track, the position encoder value, motor supply voltage, motor load current and motor operation efficiency data of the robot are collected to calculate the electric power and energy consumption of the robot, including: The robot runs from the starting point to the end point at a preset constant speed, and records the forward robot operation data at preset time intervals along the way, and stores them in the forward energy consumption data table, wherein the robot operation data is used to indicate the position encoder value of the robot, the motor supply voltage, the motor load current, the motor operation efficiency, the electric power and the energy consumption data; The robot then runs in the reverse direction from the end point to the starting point at the preset constant speed, and records the reverse robot operation data at preset time intervals along the way, and stores them in a reverse energy consumption data table.

[0009] Optionally, establishing forward and reverse energy consumption maps according to the electric power and the energy consumption includes: Calculating the energy consumption according to the electric power and the preset time period; Calculate the energy consumption per unit distance of the robot at the position of the position encoder value according to the energy consumption, the preset constant speed and the preset time period; The data points of the energy consumption per unit distance are fitted to obtain the forward and reverse energy consumption functions per unit distance at different positions of the robot on the track, wherein the forward and reverse energy consumption functions are used to indicate the forward and reverse energy consumption maps.

[0010] Optionally, during the inspection process, continuously collecting the current position encoder value of the robot, the motor supply voltage, the motor load current and the motor operation efficiency data, and calibrating and updating the energy consumption map, including: During the inspection process, the robot continuously collects the position encoder value of the robot at the current position, the motor supply voltage, the motor load current and the motor operation efficiency data in real time, and compares them with the existing data of the current position; If they are inconsistent, the energy consumption map is calibrated and updated using algorithms such as weighted averaging or Kalman filtering.

[0011] In a second aspect, the present invention provides a patrol robot charging decision system, the decision system is applied to the decision method described in the first aspect, and the decision system includes: A data acquisition module, wherein the data acquisition module is used to collect the position encoder value, motor supply voltage, motor load current and motor operation efficiency data of the robot by the robot running forward and reverse on the track; An energy consumption calculation module, the energy consumption calculation module is connected to the data acquisition module, and the energy consumption calculation module is used to calculate the electrical power and energy consumption of the robot; an energy consumption map management module, the energy consumption map management module being connected to the energy consumption calculation module, the energy consumption map management module being used to establish forward and reverse energy consumption maps according to the electric power and the energy consumption, and to calibrate and update the energy consumption map; A charging decision module, the charging decision module is connected to the energy consumption map management module, and the charging decision module is used to determine when the battery level of the robot is lower than a specified threshold, and calculate the distance of the robot moving to the nearest charging pile in the forward direction and the reverse direction, as well as the corresponding energy consumption, according to the position of the fixed charging pile, the position encoder value, and the calibrated and updated energy consumption map, and compare the energy consumption in the forward direction and the reverse direction, and select the charging pile with the lowest energy consumption as the charging target.

[0012] Optionally, the decision system further includes: A motion control module, the motion control module is connected to the charging decision module, and the motion control module is used to plan a moving path according to the charging target to control the movement of the robot; A task management module, the task management module is connected to the robot, and the task management module is used to manage the pause, recording and resumption of the inspection task of the robot; A communication module, wherein the communication module is connected to other modules in the decision-making system, and the communication module is used to establish a communication channel for data interaction and command reception between the robot and the decision-making system.

[0013] The inspection robot charging decision method and system provided in this application are intended to improve the energy utilization efficiency and inspection efficiency of the robot. The method collects key data such as position encoder value, motor supply voltage, motor load current and motor operation efficiency in real time by the robot driving forward and backward on the track, and then accurately calculates the robot's electric power and energy consumption, and constructs forward and reverse energy consumption maps accordingly. During the inspection operation, the robot continuously collects and updates the above data to achieve dynamic calibration and optimization of the energy consumption map. When the robot's power drops below the preset threshold, the system will calculate the distance to the nearest charging pile in the forward and reverse directions and the corresponding energy consumption according to the current position encoder value and the position of the fixed charging pile. By comparing the energy consumption in the two directions, the system can intelligently select the charging pile with the lowest energy consumption as the charging target, thereby ensuring that the robot is charged in time with the optimal path, and ensuring the continuity and efficiency of the inspection task. This method solves the technical problem of low energy utilization efficiency of the inspection robot in a coal mine guide rail environment with slope changes, thereby improving energy utilization efficiency, extending the robot's battery life, and ensuring the continuous and stable execution of coal mine inspection tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A flowchart of the inspection robot charging decision method provided in this application; Figure 2 A schematic diagram of the inspection robot charging decision system provided in this application; Figure 3 Flowchart of the optimal charging decision algorithm provided for this application.

[0015] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0017] The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0018] In the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0019] The present application provides a patrol robot charging decision method and system, which collects robot driving data to calculate electric power and energy consumption and constructs a forward and reverse energy consumption map. The map is updated in real time during the inspection, and when the power is low, the energy consumption of each charging pile is calculated, and the lowest energy consumption path is selected for charging to achieve efficient energy management. This method solves the technical problem of low energy utilization efficiency of patrol robots in coal mine guide rail environments with slope changes, thereby improving energy utilization efficiency, extending the robot's battery life, and ensuring the continuous and stable execution of coal mine inspection tasks.

[0020] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0021] Figure 1 A flow chart of the inspection robot charging decision method provided in this application, such as Figure 1 As shown, the inspection robot charging decision method provided in this embodiment includes: S101: By having the robot travel forward and backward on the track, the position encoder value, motor supply voltage, motor load current and motor operating efficiency data of the robot are collected to calculate the electric power and energy consumption of the robot.

[0022] The robot travels forward and backward on the track to collect the position encoder value, motor supply voltage, motor load current and motor operation efficiency data of the robot to calculate the electric power and energy consumption of the robot, specifically including: The robot runs from the starting point to the end point at a preset constant speed, and records the forward robot operation data at preset time intervals along the way, and stores them in the forward energy consumption data table, wherein the robot operation data is used to indicate the position encoder value of the robot, the motor supply voltage, the motor load current, the motor operation efficiency, the electric power and the energy consumption data; The robot then runs in the reverse direction from the end point to the starting point at the preset constant speed, and records the reverse robot operation data at preset time intervals along the way, and stores them in a reverse energy consumption data table.

[0023] The following is the specific implementation process of this step: 1. Preparation Robot setup: Make sure the robot has the position encoder installed correctly and can accurately record its current position. At the same time, the robot needs to be equipped with sensors to collect real-time data such as motor supply voltage, motor load current and motor operating efficiency.

[0024] Track setup: Select a predetermined track, make sure it is flat and free of obstacles, and set the start and end points.

[0025] Data recording system: Establish forward energy consumption data table and reverse energy consumption data table to store the data collected during the operation of the robot.

[0026] 2. Forward driving and data collection Start the robot: Start the robot at the starting point and set it to move forward along the track at a preset constant speed (such as 1m / s).

[0027] Data collection: During the robot's driving process, the operating data is recorded every preset time period (such as 1 second). These data include the position encoder value (used to determine the current position of the robot), motor supply voltage, motor load current and motor operating efficiency.

[0028] Calculate electric power and energy consumption: Based on the collected motor supply voltage and motor load current data, use the electric power calculation formula to calculate the robot's electric power. At the same time, calculate the robot's energy consumption (such as electric energy consumption) based on the motor operation efficiency and time data. The specific calculation process is as follows: Acquisition parameters: Position encoder value: Indicates the current position of the robot on the track.

[0029] Motor supply voltage : The unit is volt (V).

[0030] Motor load current : The unit is ampere (A).

[0031] Motor operating efficiency : The operating efficiency of the motor, the value range is .

[0032] Calculation parameters: Electric power : (Unit: Watt, W).

[0033] Energy consumption : Calculated based on power and time interval, (Unit: Joule, J), where: is the sampling time interval (that is, every preset time period).

[0034] Data storage: Each recorded operation data (including position encoder value, motor supply voltage, motor load current, motor operation efficiency, electric power and energy consumption) is stored in the forward energy consumption data table.

[0035] 3. Reverse driving and data collection Robot return: When the robot reaches the end point, set it to travel back to the starting point at the same preset constant speed.

[0036] Data collection and calculation: Repeat the data collection and calculation steps during forward driving, but this time the data is stored in the reverse energy consumption data table.

[0037] 4. Data Verification and Sorting Data verification: Check whether the records in the forward energy consumption data table and the reverse energy consumption data table are complete and accurate. Mark or remove abnormal data (such as values ​​that are obviously deviated from the normal range).

[0038] Data collation: The verified data will be collated to provide a reliable data basis for the subsequent establishment of energy consumption maps.

[0039] Through the detailed description of the above embodiment, we clearly show the specific execution process of step S101. This process ensures that the robot can accurately collect the required data when traveling forward and backward on the track, and calculate the electric power and energy consumption, providing key data support for the subsequent establishment of the energy consumption map.

[0040] S102: Establishing forward and reverse energy consumption maps according to the electric power and the energy consumption.

[0041] The step of establishing a forward and reverse energy consumption map according to the electric power and the energy consumption specifically includes: Calculating the energy consumption according to the electric power and the preset time period; Calculate the energy consumption per unit distance of the robot at the position of the position encoder value according to the energy consumption, the preset constant speed and the preset time period; The data points of the energy consumption per unit distance are fitted to obtain the forward and reverse energy consumption functions per unit distance at different positions of the robot on the track, wherein the forward and reverse energy consumption functions are used to indicate the forward and reverse energy consumption maps.

[0042] The following is the specific implementation process of this step: 1. Data Preparation Obtaining data: Extracting the electric power (P) and the corresponding energy consumption (E) data in each preset time period from the data collected during the forward and reverse driving of the robot. These data have been calculated and recorded in the previous steps (such as S101).

[0043] Time synchronization: Ensure that the data collected during forward and reverse driving are consistent in time, that is, the data in each preset time period is corresponding.

[0044] 2. Calculate energy consumption per unit distance Calculate total energy consumption: For each preset time period, calculate the total energy consumption based on the power (P) and the duration of the time period ( , i.e. the preset time period), calculate the energy consumption of the time period (E=P ).

[0045] Calculate the travel distance: Use the change in the position encoder value to calculate the distance the robot travels in each preset time period ( ). This can be achieved by differencing the position encoder values.

[0046] Calculate the energy consumption per unit distance: Divide the energy consumption (E) in each time period by the corresponding driving distance ( ), and obtain the energy consumption per unit distance in this time period (e=E / ), in order to facilitate the subsequent fitting of the energy consumption function, it can be used ,in, is the position encoder value.

[0047] 3. Constructing Energy Consumption Function Data point fitting: curve fitting method is used to The data points are processed and the calculated energy consumption per unit distance data points (i.e. The fitting method can be linear regression, polynomial regression or other appropriate fitting methods, depending on the distribution of the data and the accuracy requirements of the fitting.

[0048] Constructing energy consumption functions: Based on the fitting results, construct the forward and reverse energy consumption functions of the robot at different positions on the track. These functions can be expressed as the position (determined by the position encoder value) and the energy consumption per unit distance (i.e. ) is the mathematical relationship between them.

[0049] Generate energy consumption maps: Use the constructed energy consumption function to generate forward and reverse energy consumption maps of the robot at different positions on the track. These maps can be presented in the form of charts, graphs, or three-dimensional graphics to intuitively display the robot's energy consumption distribution. and , to reflect the impact of uphill and downhill slopes and load changes on energy consumption.

[0050] 4. Verification and Adjustment Data verification: Verify the generated energy consumption map to ensure that it is consistent with the actual situation. This can be achieved by conducting the robot driving experiment again, collecting data and comparing it with the energy consumption map.

[0051] Adjustment and optimization: If the verification results show that the energy consumption map deviates from the actual situation, the fitting method, fitting parameters or data points can be adjusted and optimized to improve the accuracy of the energy consumption map.

[0052] Through the detailed description of the above embodiment, we clearly show the specific execution process of step S102. This process uses the electric power and energy consumption data collected during the robot's driving process, calculates the energy consumption per unit distance and constructs an energy consumption function, and finally generates the forward and reverse energy consumption maps of the robot at different positions on the track. This provides an important reference for the subsequent inspection process, energy consumption optimization, and the formulation of charging strategies.

[0053] S103: During the inspection process, the current position encoder value, the motor supply voltage, the motor load current and the motor operating efficiency data of the robot are continuously collected to calibrate and update the energy consumption map.

[0054] Wherein, step S103 specifically includes: During the inspection process, the robot continuously collects the position encoder value of the robot at the current position, the motor supply voltage, the motor load current and the motor operation efficiency data in real time, and compares them with the existing data of the current position; If they are inconsistent, the energy consumption map is calibrated and updated using algorithms such as weighted averaging or Kalman filtering.

[0055] The following is the specific implementation process of this step: 1. Real-time data collection During the inspection mission, the robot will continue to move and collect key data about its current location in real time. This data includes: Position encoder value: Used to accurately determine the robot's position on the track.

[0056] Motor supply voltage: reflects the current working voltage of the motor and is an important parameter for calculating electric power and energy consumption.

[0057] Motor load current: This indicates the current consumption of the motor under a specific load and is also crucial to the calculation of energy consumption.

[0058] Motor operating efficiency: Measures the efficiency of a motor in converting electrical energy into mechanical energy and is a key indicator for evaluating energy consumption efficiency.

[0059] 2. Data comparison The robot compares the real-time collected data with the existing data of the current position. This data is usually stored in the robot's internal memory or transmitted wirelessly to a remote server for storage and comparison. The purpose of the comparison is to detect whether there are differences or changes in the data.

[0060] 3. Data calibration and update If the comparison results show inconsistent data, that is, there is a significant difference between the real-time collected data and the existing data, the robot will use specific algorithms to calibrate and update the energy consumption map. These algorithms may include: Weighted average: The real-time collected data is weighted averaged with the existing data to smooth data fluctuations and reduce errors.

[0061] Kalman filter: A more advanced filtering algorithm that can predict future states based on historical data and real-time data, and dynamically adjust relevant parameters in the energy consumption map.

[0062] After adopting these algorithms, the robot will be able to more accurately reflect the energy consumption on the current track, thereby optimizing subsequent inspection paths and charging strategies.

[0063] 4. Real-time performance of energy consumption map By continuously collecting and comparing data, and calibrating and updating the energy consumption map, the robot can ensure the real-time and accuracy of the energy consumption map. This helps the robot make more intelligent decisions during the inspection process, such as choosing the optimal inspection route and charging strategy, thereby extending the inspection time and reducing energy consumption.

[0064] It is understandable that during daily inspection tasks, the robot's operating environment and state may change, such as track wear, motor performance changes, etc. In order to maintain the accuracy of the energy consumption map, it needs to be dynamically updated. The implementation process of step S103 ensures that the robot can continuously collect key data during the inspection process and calibrate and update the energy consumption map in real time. This not only improves the robot's inspection efficiency, but also optimizes its energy utilization, providing a strong guarantee for the stable operation of the entire system.

[0065] S104: When the battery level of the robot is lower than a specified threshold, the distance that the robot moves to the nearest charging pile in the forward direction and the reverse direction, as well as the corresponding energy consumption, are calculated according to the position of the fixed charging pile, the position encoder value, and the calibrated and updated energy consumption map.

[0066] S105: Compare the energy consumption in the forward direction and the reverse direction, and select the charging pile with the lowest energy consumption as the charging target.

[0067] The following are steps S104-S105: specific implementation process: 1. Power detection and threshold judgment During the inspection process, the robot will continuously monitor its battery power and compare it with a preset power threshold. When the power is lower than the threshold, the robot will trigger the charging strategy selection process, i.e., step S104.

[0068] 2. Get the location information of the charging pile The robot stores the location information of the fixed charging pile inside, which usually includes the coordinates of the charging pile or the relative position relative to the current position of the robot. This information can be obtained through the prior map construction or positioning system.

[0069] 3. Position encoder value reading The robot accurately determines its current position on the track by reading the value of its position encoder. The position encoder is a high-precision sensor that can provide real-time feedback on the robot's movement distance and position information.

[0070] 4. Calculate the moving distance Based on the location information of the charging station and the current position of the robot, the robot will calculate the distance to the nearest charging station in the forward and reverse directions respectively. This usually involves simple geometric calculations or path planning algorithms.

[0071] 5. Energy consumption calculation After determining the moving distance, the robot will use the established energy consumption map or energy consumption model to calculate the energy consumption required to move to the charging pile in the forward and reverse directions according to the robot's real-time status (such as motor supply voltage, motor load current, etc.). This usually involves the integration of electric power or the query of the energy consumption model (for specific calculations, please refer to the relevant calculation process provided in the context).

[0072] 6. Data storage and backup The calculated moving distance and energy consumption data will be stored in the robot's internal memory for subsequent comparison and decision-making. At the same time, these data can also be transmitted to a remote server for storage and analysis.

[0073] 7. Prepare for charging strategy selection After completing the above calculations, the robot will be ready to enter the next step, which is to compare the energy consumption in the forward and reverse directions and select the charging pile with the lowest energy consumption as the charging target.

[0074] 8. Optimal charging decision algorithm When the above process is implemented, this embodiment provides an optimal charging decision algorithm to describe the specific implementation process of the above process in detail. When the robot detects that the power is lower than the specified threshold, the algorithm needs to calculate the energy consumption of the nearest forward and reverse charging piles according to the energy consumption map, and select the charging pile with the lowest energy consumption for charging. The algorithm specifically includes: 8.1. Determination of the location of charging piles Fixed charging pile position: The charging pile is set at a fixed encoder position on the rail Place.

[0075] Current position: The encoder value of the robot's current position is .

[0076] 8.2 Distance Calculation Forward distance: Calculate the distance the robot moves in the positive direction to the nearest charging station:

[0077] Reverse distance: Calculate the distance the robot moves in the reverse direction to the nearest charging station:

[0078] 8.3 Energy consumption calculation Forward energy consumption:

[0079] Reverse energy consumption:

[0080] 8.4. Best Choice Energy consumption comparison: For the charging piles with the closest forward and reverse distances, compare the forward and reverse energy consumption.

[0081] Select strategy: Minimum energy consumption principle: choose The smallest Corresponding charging pile .

[0082] 8.5 Mobile Execution Path planning: Plan the path and speed of the robot's movement based on the selected charging station location.

[0083] Energy monitoring: During the move, the remaining power is monitored in real time to ensure that it can reach the charging station.

[0084] Charging process: After reaching the charging station, the robot will charge until the power reaches the set value.

[0085] 8.6 Optimal Charging Decision Algorithm Flowchart like Figure 3 As shown, Figure 3 The optimal charging decision algorithm flow chart provided for this application; the algorithm flow chart further explains the specific process of the optimal charging decision algorithm: Initialization: Loading energy consumption map and , get the location of the charging pile .

[0086] Power monitoring: Real-time detection of robot power ,when When the charging decision algorithm is triggered.

[0087] Energy consumption calculation: For the closest charging pile in the forward and reverse directions , calculate the forward energy consumption and reverse energy consumption , select the value with smaller energy consumption as the minimum energy consumption of the charging pile .

[0088] Best choice: among all In the example, select the charging pile corresponding to the minimum value. .

[0089] Path planning: planning where the robot will go Path.

[0090] Mobile execution: The robot moves to the charging station according to the planned path and starts charging.

[0091] Mission resumption: After charging is completed, the robot returns to the mission pause position and continues the inspection mission.

[0092] 8.7. Optimization of the optimal charging decision algorithm Cache mechanism: To reduce the amount of calculation, frequently used paths and energy consumption results can be cached.

[0093] Adaptive sampling: adjust the sampling interval according to the rate of change of energy consumption, and increase the sampling frequency in areas where energy consumption changes dramatically.

[0094] 9. Task management and recovery Task suspension: When the battery level is lower than the threshold and the robot decides to go to the charging station, the robot suspends the current inspection task.

[0095] Task record: record the location and status of unfinished tasks so that they can be continued after charging is completed.

[0096] Mission resumption: After charging is completed, the robot returns to the location where the mission was paused and continues to perform the unfinished inspection mission.

[0097] To sum up, the implementation process of steps S104-S105 ensures that the robot can accurately calculate the moving distance and energy consumption to the nearest charging pile when the battery is low, and ensures that the robot can make the best charging decision when the battery is low, thereby efficiently utilizing energy, extending the inspection time, and improving the stability and efficiency of the entire system.

[0098] Figure 2 The schematic diagram of the patrol robot charging decision system provided in this application is a detailed description of the patrol robot charging decision system, such as Figure 2 As shown, the inspection robot charging decision system provided in this embodiment includes: A data acquisition module, wherein the data acquisition module is used to collect the position encoder value, motor supply voltage, motor load current and motor operation efficiency data of the robot by the robot running forward and reverse on the track; An energy consumption calculation module, the energy consumption calculation module is connected to the data acquisition module, and the energy consumption calculation module is used to calculate the electrical power and energy consumption of the robot; an energy consumption map management module, the energy consumption map management module being connected to the energy consumption calculation module, the energy consumption map management module being used to establish forward and reverse energy consumption maps according to the electric power and the energy consumption, and to calibrate and update the energy consumption map; A charging decision module, the charging decision module is connected to the energy consumption map management module, and the charging decision module is used to determine when the battery level of the robot is lower than a specified threshold, and calculate the distance of the robot moving to the nearest charging pile in the forward direction and the reverse direction, as well as the corresponding energy consumption, according to the position of the fixed charging pile, the position encoder value, and the calibrated and updated energy consumption map, and compare the energy consumption in the forward direction and the reverse direction, and select the charging pile with the lowest energy consumption as the charging target.

[0099] Preferably, the decision-making system further includes: A motion control module, the motion control module is connected to the charging decision module, and the motion control module is used to plan a moving path according to the charging target to control the movement of the robot; A task management module, the task management module is connected to the robot, and the task management module is used to manage the pause, recording and resumption of the inspection task of the robot; A communication module, wherein the communication module is connected to other modules in the decision-making system, and the communication module is used to establish a communication channel for data interaction and command reception between the robot and the decision-making system.

[0100] The following is a detailed description of the modules and their connection methods in this embodiment.

[0101] 1. System Overview The inspection robot charging decision system of this embodiment integrates multiple modules such as data collection, energy consumption calculation, energy consumption map management, charging decision, motion control, task management and communication, forming a complete intelligent charging decision process.

[0102] 2. Module Details and Connection Methods 1. Data acquisition module Function: By inspecting the robot's forward and reverse travel on the track, key data such as position encoder value, motor supply voltage, motor load current and motor operating efficiency are collected in real time.

[0103] Connection method: The data acquisition module is directly connected to the sensors and actuators of the inspection robot to ensure that the required data is obtained in real time and accurately.

[0104] 2. Energy consumption calculation module Function: Based on the data provided by the data acquisition module, calculate the electrical power and energy consumption of the inspection robot.

[0105] Connection method: The energy consumption calculation module is connected to the data acquisition module through a data interface to receive and process the collected data.

[0106] 3. Energy consumption map management module Function: According to the electric power and energy consumption data provided by the energy consumption calculation module, establish forward and reverse energy consumption maps, and regularly calibrate and update the energy consumption maps to reflect the latest energy consumption conditions.

[0107] Connection method: The energy consumption map management module and the energy consumption calculation module are connected through a data bus to achieve real-time transmission and update of data.

[0108] 4. Charging decision module Function: When the inspection robot's battery level is lower than the specified threshold, the robot calculates the distance to the nearest charging pile and the corresponding energy consumption in the forward and reverse directions according to the position of the fixed charging pile and the position encoder value, and selects the charging pile with the lowest energy consumption as the charging target.

[0109] Connection method: The charging decision module is connected to the energy consumption map management module through an internal communication protocol, and interacts with the location information database of the fixed charging pile to obtain the accurate location of the charging pile.

[0110] 5. Motion control module Function: Plan the movement path and control the movement of the inspection robot according to the charging target determined by the charging decision module.

[0111] Connection method: The motion control module is connected to the charging decision module through the control command interface, and interacts with the motion control system of the inspection robot to achieve path planning and motion control.

[0112] 6. Task management module Function: Manage the pause, recording and resumption of the inspection tasks of the inspection robot to ensure that the inspection task information is not lost during the charging process.

[0113] Connection method: The task management module is directly connected to the task management system of the inspection robot and communicates through the internal task management protocol.

[0114] 7. Communication module Function: Build a communication channel for data interaction and command reception between the inspection robot and the decision-making system to ensure unimpeded information transmission between various modules.

[0115] Connection method: The communication module is connected to other modules in the decision-making system through a unified communication protocol and network architecture to achieve real-time transmission of data and accurate reception of instructions.

[0116] 3. Workflow The inspection robot travels on the track, and the data acquisition module collects data in real time.

[0117] The energy consumption calculation module calculates the electric power and energy consumption based on the collected data.

[0118] The energy consumption map management module establishes and updates the energy consumption map according to the calculation results.

[0119] When the inspection robot's battery level is lower than the specified threshold, the charging decision module calculates and selects the optimal charging pile.

[0120] The motion control module plans the path according to the charging target and controls the robot movement.

[0121] The task management module records and manages inspection tasks.

[0122] The communication module ensures smooth information transmission between modules.

[0123] Through the detailed description of the above embodiments, we can clearly understand the various modules and their connection methods of the inspection robot charging decision system of the present invention, and how they work together to achieve the goal of intelligent charging decision-making.

[0124] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0125] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0126] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A charging decision method for an inspection robot, characterized in that: The method comprises: By having the robot travel forward and backward on the track, the position encoder value, motor supply voltage, motor load current and motor operation efficiency data of the robot are collected to calculate the electric power and energy consumption of the robot; establishing forward and reverse energy consumption maps based on the electric power and the energy consumption; During the inspection process, the current position encoder value of the robot, the motor supply voltage, the motor load current and the motor operating efficiency data are continuously collected to calibrate and update the energy consumption map; When the battery level of the robot is lower than a specified threshold, the distance that the robot moves to the nearest charging pile in the forward direction and the reverse direction, and the corresponding energy consumption are calculated according to the position of the fixed charging pile, the position encoder value, and the calibrated and updated energy consumption map; The energy consumption in the forward direction and the reverse direction are compared, and the charging pile with the lowest energy consumption is selected as the charging target.

2. The method according to claim 1, characterized in that: The robot is driven forward and backward on the track to collect the position encoder value, motor supply voltage, motor load current and motor operation efficiency data of the robot to calculate the electric power and energy consumption of the robot, including: The robot runs from the starting point to the end point at a preset constant speed, and records the forward robot operation data at preset time intervals along the way, and stores them in the forward energy consumption data table, wherein the robot operation data is used to indicate the position encoder value of the robot, the motor supply voltage, the motor load current, the motor operation efficiency, the electric power and the energy consumption data; The robot then runs in the reverse direction from the end point to the starting point at the preset constant speed, and records the reverse robot operation data at preset time intervals along the way, and stores them in a reverse energy consumption data table.

3. The method according to claim 2, characterized in that The establishing of forward and reverse energy consumption maps according to the electric power and the energy consumption includes: Calculating the energy consumption according to the electric power and the preset time period; Calculate the energy consumption per unit distance of the robot at the position of the position encoder value according to the energy consumption, the preset constant speed and the preset time period; The data points of the energy consumption per unit distance are fitted to obtain the forward and reverse energy consumption functions per unit distance at different positions of the robot on the track, wherein the forward and reverse energy consumption functions are used to indicate the forward and reverse energy consumption maps.

4. The method according to claim 1, characterized in that During the inspection process, the current position encoder value of the robot, the motor supply voltage, the motor load current and the motor operation efficiency data are continuously collected to calibrate and update the energy consumption map, including: During the inspection process, the robot continuously collects the position encoder value of the robot at the current position, the motor supply voltage, the motor load current and the motor operation efficiency data in real time, and compares them with the existing data of the current position; If they are inconsistent, a weighted average or Kalman filter algorithm is used to calibrate and update the energy consumption map.

5. A patrol robot charging decision system, characterized in that: The decision-making system is applied to the decision-making method according to any one of claims 1 to 4, and the decision-making system comprises: A data acquisition module, wherein the data acquisition module is used to collect the position encoder value, motor supply voltage, motor load current and motor operation efficiency data of the robot by the robot running forward and reverse on the track; An energy consumption calculation module, the energy consumption calculation module is connected to the data acquisition module, and the energy consumption calculation module is used to calculate the electrical power and energy consumption of the robot; an energy consumption map management module, the energy consumption map management module being connected to the energy consumption calculation module, the energy consumption map management module being used to establish forward and reverse energy consumption maps according to the electric power and the energy consumption, and to calibrate and update the energy consumption map; A charging decision module, the charging decision module is connected to the energy consumption map management module, and the charging decision module is used to determine when the battery level of the robot is lower than a specified threshold, and calculate the distance of the robot moving to the nearest charging pile in the forward direction and the reverse direction, as well as the corresponding energy consumption, according to the position of the fixed charging pile, the position encoder value, and the calibrated and updated energy consumption map, and compare the energy consumption in the forward direction and the reverse direction, and select the charging pile with the lowest energy consumption as the charging target.

6. The system according to claim 5, characterized in that The decision-making system also includes: A motion control module, the motion control module is connected to the charging decision module, and the motion control module is used to plan a moving path according to the charging target to control the movement of the robot; A task management module, the task management module is connected to the robot, and the task management module is used to manage the pause, recording and resumption of the inspection task of the robot; A communication module, wherein the communication module is connected to other modules in the decision-making system, and the communication module is used to establish a communication channel for data interaction and command reception between the robot and the decision-making system.

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