Patrol method of robot in mine well and robot

Abstract

The invention relates to the technical field of robot control, discloses a patrol method of a robot in a mine well and the robot, and aims to solve the problems of poor accuracy and low efficiency of an existing mode, and the scheme mainly comprises the steps that the robot carries out patrol according to an initial patrol path; the surrounding environment is recognized in the patrol process, the advancing direction of the robot is adjusted to avoid danger when encountering abnormal road conditions, and the robot is controlled to advance along the middle line of a roadway when encountering a curved roadway; according to the actual patrol path of the robot, the actual position of the robot is determined through track plotting positioning; generating an adjustment path between the actual position and the nearest path point on the initial patrol path, and controlling the robot to advance along the adjustment path; in the patrol process, the surrounding environment data are automatically detected, and a patrol report is generated in real time according to the corresponding relation between the environment data and the actual position. The accuracy and efficiency of robot patrol are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control, and particularly to a method for a robot to patrol in a mine and the robot. Background Art

[0002] The safety risks in a mine are high. Manual patrol may be dangerous and inefficient. Using a robot to patrol in a mine can replace humans to enter high-risk areas, reduce casualties. The robot can automatically complete routine inspection tasks, liberate human labor. At the same time, the robot can continuously monitor the complex environment through multiple sensors, make up for the omissions of manual inspection, avoid the subjective errors of manual records, and provide objective and continuous monitoring data.

[0003] Traditional patrol robots usually rely on preset fixed paths or single sensors to achieve navigation. Since the mine shaft is underground, and the structure and environment of the mine shaft are complex, and metal brackets and equipment are densely distributed in the mine shaft, affected by signal shielding, electromagnetic interference, dust concentration fluctuations, etc. in the mine shaft, existing positioning methods such as GPS positioning, UWB positioning, and laser positioning all have problems of difficult positioning, unable to achieve accurate positioning of the robot. As a result, the environmental data obtained by the robot patrol does not match the actual position, the environmental data is bound to the wrong position coordinates, the environmental data collected in the dangerous area may come from the safe area, which may lead to misjudgment of the dangerous area, unable to accurately mark the dangerous area, and thus form a monitoring blind area, with poor patrol accuracy; and when the robot travels in the mine shaft, especially in the curved roadway, it is easy to rub and collide, resulting in low patrol efficiency. Summary of the Invention

[0004] The present invention aims to solve the problems of poor accuracy and low efficiency in the existing robot patrol method in a mine shaft, and proposes a method for a robot to patrol in a mine shaft and the robot.

[0005] The technical solutions adopted by the present invention to solve the above technical problems are as follows: In the first aspect, the present invention provides a method for a robot to patrol in a mine shaft, the method including: The robot receives the issued initial patrol path and conducts patrol according to the initial patrol path; During the robot patrol process, multiple laser scanners are used to identify the surrounding environment. When encountering abnormal road conditions, the traveling direction of the robot is adjusted to avoid danger. When encountering a curved roadway, the center line of the roadway is generated in real time, and the robot is controlled to travel along the center line of the roadway; According to the actual traveling route and direction of the robot, the actual patrol path of the robot is determined. According to the actual patrol path of the robot, dead reckoning positioning is used to determine the actual position of the robot; When the actual position of the robot is not on the initial patrol path, determine the actual position of the robot and the nearest path point on the initial patrol path, generate an adjustment path between the actual position and the nearest path point, and control the robot to travel along the adjustment path; During the robot's patrol, automatically detect the surrounding environment data, and generate a patrol report in real time according to the correspondence between the environment data and the actual position of the robot.

[0006] Further, when encountering a curved roadway, generate the roadway center line in real time, specifically including: When the laser scanner recognizes a curved roadway, obtain the point cloud data of the curved roadway in real time. After performing downsampling and outlier removal processing on the point cloud data, segment the ground point cloud to generate a point set of the walls on both sides of the curved roadway; Dynamically fit the curve equations for the point sets of the walls on both sides of the curved roadway respectively. According to the curve equations, sample the boundary points on both sides of the curved roadway at equal intervals along the traveling direction of the robot, and calculate the midpoint sequence; Interpolate the midpoint sequence to generate a smooth roadway center line.

[0007] Further, determining the actual position of the robot includes: Determine the starting position and initial heading angle of the robot, and determine the wheelbase between the left and right wheels of the robot; Detect the displacements of the left and right wheels of the robot according to the preset sampling time interval, and calculate the centroid linear velocity and the change amount of the heading angle of the robot within the preset sampling time interval based on the displacements of the left and right wheels and the wheelbase; Calculate the average heading angle within the sampling time interval according to the initial heading angle and the change amount of the heading angle, and calculate the displacement increment of the robot within the sampling time interval according to the centroid linear velocity and the average heading angle of the robot; Determine the actual position of the robot based on the starting position and initial heading angle of the robot and based on the change amount of the heading angle and the displacement increment.

[0008] Further, the calculation formula for the centroid linear velocity is as follows: ; The calculation formula for the change amount of the heading angle is as follows: ; The calculation formula for the average heading angle is as follows: ; The calculation formula for the displacement increment is as follows: ; ; The calculation formula for the actual position of the robot is as follows: ; ; ; wherein, represents the centroid linear velocity, represents the displacement of the left wheel, represents the displacement of the right wheel, represents the sampling time interval, represents the change in heading angle, represents the distance between the left and right wheels, represents the average heading angle, represents the heading angle at the previous moment, represents the abscissa of the robot at the previous moment, represents the ordinate of the robot at the previous moment, represents the displacement increment of the robot in the direction within the sampling time interval, represents the displacement increment of the robot in the direction within the sampling time interval, represents the abscissa of the actual position of the robot, represents the ordinate of the actual position of the robot, represents the heading angle of the actual position of the robot.

[0009] Furthermore, the environmental data includes image data, gas data, light data, noise data, and ventilation data. The image data is detected by a camera, the gas data is collected and detected by a gas collector, the light data is detected by a light sensor, the noise data is detected by a sound sensor, and the ventilation data is detected by a wind speed sensor.

[0010] Furthermore, the gas data at least includes: gas data of methane, carbon monoxide, carbon dioxide, oxygen, and hydrogen sulfide.

[0011] Furthermore, the method further includes: When the communication connection between the robot and the remote control center is disconnected, the real-time generated inspection report is remotely saved in the local memory; when the communication connection between the robot and the remote control center is restored, the real-time generated inspection report is remotely sent to the control center.

[0012] Furthermore, the method further includes: Using the positioning module to obtain the position information of the robot in real time. If the acquisition is successful, the actual position of the robot is corrected according to the position information.

[0013] Furthermore, the method further includes: Detect the remaining power of the robot in real time. When the remaining power is less than the power threshold, generate a return route according to the actual position of the robot, and control the robot to travel along the return route.

[0014] In a second aspect, the present invention provides a robot, which includes: An autonomous mobile platform for receiving the issued initial inspection path and performing inspections according to the initial inspection path; A plurality of laser scanners for identifying the surrounding environment during the inspection of the robot; A path optimization module for adjusting the traveling direction of the robot to avoid danger when encountering abnormal road conditions, generating the center line of the roadway in real time when encountering a curved roadway, and controlling the robot to travel along the center line of the roadway; and when the actual position of the robot is not on the initial inspection path, determining the nearest path point between the actual position of the robot and the initial inspection path, generating an adjustment path between the actual position and the nearest path point, and controlling the robot to travel along the adjustment path; A dead reckoning positioning module for determining the actual inspection path of the robot according to the actual traveling route and direction of the robot, and determining the actual position of the robot by using dead reckoning positioning according to the actual inspection path of the robot; An inspection module for automatically detecting the surrounding environment data during the inspection of the robot and generating an inspection report in real time according to the corresponding relationship between the environment data and the actual position of the robot.

[0015] The beneficial effects of the present invention are as follows: The inspection method and robot of the robot provided by the present invention in the underground mine identify the environment through a laser scanner, adjust the direction when encountering abnormalities, and control the robot to travel along the center line of the roadway when encountering a curved roadway, realizing real-time environment perception and dynamic path adjustment, thereby reducing the risks of collision and rubbing, and improving the moving efficiency and inspection efficiency of the robot; through dead reckoning positioning, the accurate positioning of the robot can be realized under the complex structure and environment of the underground mine and after the dynamic path adjustment of the robot, improving the accuracy of robot positioning, improving the accuracy of the corresponding relationship between the environment data and the position coordinates, and further improving the inspection accuracy of the robot; when the robot position deviates, an adjustment path is generated, realizing the self-correction of the robot, ensuring the coherence of the inspection, and reducing manual intervention; the robot automatically generates an inspection report according to the collected environment data, realizing real-time data recording and integration, and improving the inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flow chart of the inspection method of the robot provided in the embodiment in the underground mine; Figure 2 It is a schematic structural diagram of the robot provided in the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0017] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in this embodiment will be clearly and completely described below in conjunction with the accompanying drawings in this embodiment.

[0018] In some processes described in the specification of the present invention and the above-mentioned accompanying drawings, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0019] Due to the complex structure and environment of the mine shaft, the robot is prone to rubbing and collisions when traveling in the mine shaft, with low movement efficiency, and it is difficult to accurately locate the robot, resulting in low accuracy and efficiency of robot inspections.

[0020] In order to improve the accuracy and efficiency of robot inspections in the mine, the technical solution of the present invention is proposed. In the present invention, the robot receives the issued initial inspection path and conducts inspections according to the initial inspection path; during the robot inspection process, multiple laser scanners are used to identify the surrounding environment. When encountering abnormal road conditions, the traveling direction of the robot is adjusted for hazard avoidance. When encountering a curved roadway, the center line of the roadway is generated in real time, and the robot is controlled to travel along the center line of the roadway; according to the actual traveling route and direction of the robot, the actual inspection path of the robot is determined. According to the actual inspection path of the robot, dead reckoning positioning is used to determine the actual position of the robot; when the actual position of the robot is not on the initial inspection path, the actual position of the robot and the nearest path point on the initial inspection path are determined, and an adjustment path between the actual position and the nearest path point is generated, and the robot is controlled to travel along the adjustment path; during the robot inspection process, the surrounding environment data is automatically detected, and an inspection report is generated in real time according to the corresponding relationship between the environment data and the actual position of the robot.

[0021] Specifically, first, the initial inspection path is sent to the robot by the remote control center. By setting the initial inspection path, the real-time computing load can be reduced and the startup time can be shortened. The preset path serves as a safety benchmark and provides a reference coordinate system for subsequent dynamic adjustment. Then, during the inspection process according to the initial path, the robot uses multiple laser scanners for environmental perception and dynamic obstacle avoidance to prevent the robot from colliding. When encountering a curved roadway, the robot is controlled to travel along the center line of the roadway to avoid rubbing against the wall, thereby improving the traveling efficiency of the robot. At the same time, during the inspection process of the robot, based on the actual traveling route and direction of the robot, dead reckoning positioning is used to accurately locate the robot, improving the accuracy of robot positioning. After the robot deviates from the initial inspection path, the robot is controlled to correct itself, ensuring the coherence of the inspection and reducing manual intervention, further improving the inspection efficiency. Finally, during the inspection process of the robot, the robot automatically generates an inspection report based on the collected environmental data, realizing real-time data recording and integration, and further improving the inspection efficiency.

[0022] Next, the technical solutions in this embodiment will be clearly and completely described in conjunction with the accompanying drawings in this embodiment. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0023] Figure 1 The flowchart of a method for inspecting a robot in an underground mine is shown. Please refer to Figure 1 , and the method includes the following steps: Step 1: The robot receives the sent initial inspection path and conducts inspections according to the initial inspection path.

[0024] In practical applications, the initial inspection path is generated according to the actual situation of the mine shaft. The remote control center encodes the initial inspection path into an ordered sequence of path points. Each path point includes position coordinates and an expected heading angle, and is encapsulated in JSON format and transmitted to the robot through the 5G network. The main control system of the robot parses the path data, generates an initial motion instruction through the global path planning module, and the drive system of the robot tracks the initial inspection path based on the PID controller, so that the robot can conduct inspections according to the initial inspection path.

[0025] Step 2: During the inspection process of the robot, multiple laser scanners are used to identify the surrounding environment. When encountering abnormal road conditions, the traveling direction of the robot is adjusted for risk avoidance. When encountering a curved roadway, the center line of the roadway is generated in real time, and the robot is controlled to travel along the center line of the roadway.

[0026] In practical applications, by arranging lidars in a ring, environmental point cloud data is collected in real time. Meanwhile, an improved ICP algorithm is used to register the point clouds of multiple lidars, and a local obstacle grid map is constructed. When obstacles or ground potholes are detected, an obstacle avoidance path is calculated in real time; when a curved roadway is detected, the two side boundaries are fitted to generate the center line of the roadway. When an anomaly is encountered, the traveling direction of the robot is adjusted, and when a curved roadway is encountered, the robot is controlled to travel along the center line of the roadway, achieving real-time environmental perception and dynamic path adjustment, thereby reducing the risks of collision and rubbing and improving the moving efficiency and inspection efficiency of the robot.

[0027] In this embodiment, when a curved roadway is encountered, the center line of the roadway is generated in real time, which specifically includes: Step 21: After the lidar scanner recognizes a curved roadway, the point cloud data of the curved roadway is obtained in real time. After downsampling and outlier removal processing of the point cloud data, the ground point cloud is segmented to generate a point set of the two side walls of the curved roadway.

[0028] In practical applications, first, a voxel grid filter can be used to downsample the original point cloud to reduce the subsequent calculation complexity and improve real-time performance; then, a statistical outlier removal algorithm can be used to remove outliers. Calculate the average distance between each point and its 50 nearest neighbors. If the distance exceeds the mean + 3 times the standard deviation, it is determined as an outlier. Removing outliers can filter out abnormal points caused by laser reflection noise or dynamic objects and improve the robustness of subsequent segmentation and fitting; then, the ground can be extracted based on the plane fitting of the random sample consensus algorithm, the ground plane equation is fitted, and the point set that satisfies the plane constraint is extracted. The remaining point cloud (non-ground points) is used as the candidate wall point set, and only this part is processed subsequently to avoid the influence of ground interference on wall fitting; finally, Euclidean clustering is used to separate the left and right wall point clouds. According to the prior knowledge of the roadway structure (such as left-right symmetry), the left and right side point sets of the curved roadway are divided. If a segmentation error occurs (such as occlusion by a temporary obstacle), the boundary can be corrected through the temporal correlation of consecutive frame point clouds.

[0029] Step 22: Dynamically fit the curve equations for the point sets of the two side walls of the curved roadway respectively. According to the curve equations, boundary points on both sides of the curved roadway are sampled at equal intervals along the traveling direction of the robot, and a midpoint sequence is calculated.

[0030] In practical applications, first, a B-spline curve can be used to fit the point sets of the two side walls of the curved roadway. During the fitting process, the least squares method is used to solve the control points, and the control point density is adaptively adjusted according to the curvature change; then along the forward direction of the robot ( Sample the left and right curve equations at fixed intervals (e.g., 20 cm) along the axis, calculate the geometric midpoints of each pair of left and right points to generate a midpoint sequence. During the sampling process, interpolate and align the sampling points to ensure the same number of left and right points and a uniform spacing of the midpoint sequence, which is beneficial for subsequent path tracking control and also supports the generation of the center line of asymmetric roadways.

[0031] Step 23: Interpolate the midpoint sequence to generate a smooth center line of the roadway.

[0032] In practical applications, using the midpoint sequence as the shape value points, construct a cubic uniform B-spline curve, and constrain the maximum curvature according to the minimum turning radius of the robot. If the curvature exceeds the limit, insert additional control points for local smoothing. Finally, generate a smooth center line of the roadway. The generated center line of the roadway has continuous curvature, meets the kinematic constraints of the robot, and has a small lateral tracking error.

[0033] Step 3: Determine the actual inspection path of the robot according to the actual traveling route and direction of the robot. Based on the actual inspection path of the robot, use dead reckoning positioning to determine the actual position of the robot.

[0034] In this embodiment, determining the actual position of the robot includes: Step 31: Determine the starting position and initial heading angle of the robot, and determine the distance between the left and right wheels of the robot.

[0035] In practical applications, the initial global coordinates of the robot can be obtained through an external positioning system (such as laser SLAM, UWB base station). If there is no external positioning, manually place the robot at a known marker point and input the coordinate values. At the same time, use a high-precision IMU (such as MPU-9250) to measure the initial heading angle and measure the distance between the centers of the left and right drive wheels. After calibrating the above data, write it into the configuration file of the robot. For example, starting position: ( , ) = (0, 0); initial heading angle: = 0.0 rad (towards the positive direction of the axis); distance between the left and right wheels: = 0.5 m.

[0036] Step 32: Detect the displacements of the left and right wheels of the robot according to the preset sampling time interval, and calculate the linear velocity of the centroid and the change in heading angle of the robot within the preset sampling time interval based on the displacements of the left and right wheels and the distance between the left and right wheels.

[0037] In practical applications, an incremental photoelectric encoder (such as HEDL-5640) can be used to detect the pulse counts of the left and right wheels of the robot according to a preset sampling time interval, and the left and right wheel displacements can be calculated by combining the wheel radius and the number of encoder pulses corresponding to one revolution of the wheel. Then, the linear velocity of the centroid and the change in the heading angle of the robot within the preset sampling time interval can be calculated based on the left and right wheel displacements and the distance between the left and right wheels.

[0038] In this embodiment, the calculation formula for the linear velocity of the centroid is as follows: ; Where, represents the linear velocity of the centroid (unit: m / s), represents the displacement of the left wheel (unit: m), represents the displacement of the right wheel (unit: m), represents the sampling time interval (unit: s).

[0039] The calculation formula for the change in the heading angle is as follows: ; Where, represents the change in the heading angle (unit: rad).

[0040] Step 33: Calculate the average heading angle within the sampling time interval based on the initial heading angle and the change in the heading angle, and calculate the displacement increment of the robot within the sampling time interval based on the linear velocity of the centroid and the average heading angle of the robot.

[0041] In this embodiment, the calculation formula for the average heading angle is as follows: ; Where, represents the average heading angle (unit: rad), represents the heading angle at the previous moment (unit: rad). At the first sampling time interval, is the initial heading angle.

[0042] The calculation formula for the displacement increment is as follows: ; ; Where, represents the displacement increment of the robot in the direction within the sampling time interval (unit: m), represents the displacement increment of the robot in the direction within the sampling time interval (unit: m), and represent the direction projection coefficients of the heading angle.

[0043] Step 34: Determine the actual position of the robot based on the starting position and initial heading angle of the robot and based on the heading angle change amount and displacement increment.

[0044] Update the actual position of the robot according to the calculated heading angle change amount and displacement increment, that is: ; ; ; where represents the abscissa of the actual position of the robot, represents the ordinate of the actual position of the robot, represents the heading angle of the actual position of the robot, represents the abscissa of the robot at the previous moment, represents the ordinate of the robot at the previous moment.

[0045] In the process of calculating using the above formula, in the first sampling time interval, and are set to the coordinate values corresponding to the starting position of the robot, is set to the initial heading angle. In subsequent sampling time intervals, , and are correspondingly set to the , and calculated in the previous sampling time interval to calculate the actual position of the robot in the next sampling time interval.

[0046] Through the above steps, accurate positioning of the robot can be achieved in the complex structure and environment of the mine shaft and after the dynamic path adjustment of the robot, improving the accuracy of the correspondence between environmental data and position coordinates, and further improving the inspection accuracy of the robot.

[0047] Step 4: When the actual position of the robot is not on the initial inspection path, determine the nearest path point between the actual position of the robot and the initial inspection path, generate an adjustment path between the actual position and the nearest path point, and control the robot to travel along the adjustment path.

[0048] In this embodiment, the robot is preferentially controlled to avoid obstacles automatically and travel along the center line of the curved roadway. When it is detected that there are no obstacles and roadways in front of the robot, the robot makes an automatic correction, so that the robot travels along the initial inspection path to the greatest extent, thus ensuring the coherence of the inspection and reducing manual intervention.

[0049] In practical applications, the actual position of the robot and the sequence of path points corresponding to the initial inspection path are obtained in real time. The line segments formed by adjacent points in the path are traversed. For each line segment, the shortest distance from the actual position of the robot to the line segment and the projection point are calculated. The shortest distances of all line segments are compared, and the smallest projection point is selected as the target point. Then, an adjustment path from the actual position of the robot to the target point is generated, and the robot is controlled to travel along the adjustment path. During the process of the robot traveling along the adjustment path, when encountering abnormal road conditions, the traveling direction of the robot is preferentially adjusted to avoid danger. And when encountering a curved roadway, the robot is preferentially controlled to travel along the center line of the roadway until the robot reaches near the target point. When the robot reaches near the target point, it switches back to tracking the initial inspection path. Through the above process, the robot can automatically detect position deviation in real time and generate a smooth adjustment path, and combine with the control algorithm to quickly and stably return to the initial inspection path.

[0050] Step 5: During the robot inspection process, the surrounding environmental data is automatically detected, and an inspection report is generated in real time according to the corresponding relationship between the environmental data and the actual position of the robot.

[0051] In this embodiment, the environmental data includes image data, gas data, light data, noise data, and ventilation data. The image data is detected by a camera, the gas data is collected and detected by a gas collector, and the gas data at least includes: gas data of methane, carbon monoxide, carbon dioxide, oxygen, and hydrogen sulfide; the light data is detected by a light sensor, the noise data is detected by a sound sensor, and the ventilation data is detected by a wind speed sensor.

[0052] Through the above equipment, a comprehensive environmental detection of the mine shaft can be realized. By integrating multi-source data such as images, gases, noises, lights, and ventilation flow rates, a safety assessment of the mine shaft structure and early warning of potential safety hazards can be realized.

[0053] In this embodiment, the method further includes: when the communication connection between the robot and the remote control center is disconnected, the real-time generated inspection report is remotely saved in the local memory; when the communication connection between the robot and the remote control center is restored, the real-time generated inspection report is remotely sent to the control center.

[0054] Due to the complex structure and environment of the mine shaft and poor network conditions, it is easy to cause unstable network connection between the robot and the remote control center. In this embodiment, local storage during network disconnection can avoid data loss caused by communication interruption, ensure the continuity of the real-time generation process of the inspection report, and automatic retransmission after connection restoration realizes the complete archiving of data.

[0055] In this embodiment, the method further includes: using a positioning module to obtain the position information of the robot in real time. If the acquisition is successful, the actual position of the robot is corrected according to the position information.

[0056] It can be understood that when the robot reaches a position in the mine shaft where positioning conditions are available, the positioning module of the robot can be controlled to perform positioning, and the actual position of the robot is updated according to the position information obtained by the positioning module, so as to further improve the accuracy of robot positioning.

[0057] In this embodiment, the method further includes: detecting the remaining power of the robot in real time. When the remaining power is less than the power threshold, a return route is generated according to the actual position of the robot, and the robot is controlled to travel along the return route.

[0058] It can be understood that real-time power monitoring and the return function can avoid task interruption, shorten the charging interval through intelligent return, improve the effective working time of the robot, and also avoid the risk of out-of-control caused by sudden power-off of sensors or control systems due to insufficient power, significantly improving the reliability and economy of the robot system. At the same time, it provides a data basis for multi-robot collaboration and long-term operation and maintenance.

[0059] In summary, the inspection method of the robot provided in this embodiment in the mine shaft identifies the environment through a laser scanner, adjusts the direction when encountering abnormalities, and controls the robot to travel along the center line of the roadway when encountering a curved roadway, realizing real-time environment perception and dynamic path adjustment, thereby reducing the risks of collision and abrasion, and improving the movement efficiency and inspection efficiency of the robot; through dead reckoning positioning, in the complex structure and environment of the mine shaft, the accuracy of robot positioning is improved, the accuracy of the corresponding relationship between environmental data and position coordinates is improved, and thus the inspection accuracy of the robot is improved; when the robot's position deviates, an adjustment path is generated, realizing self-correction of the robot, ensuring the coherence of the inspection, and reducing manual intervention; the robot automatically generates an inspection report according to the collected environmental data, realizing real-time data recording and integration, and improving the inspection efficiency.

[0060] Based on the above technical solution, this embodiment further proposes a robot. Please refer to Figure 2 , the robot includes: An autonomous mobile platform for receiving the issued initial inspection path and performing inspections according to the initial inspection path; A plurality of laser scanners for identifying the surrounding environment during the inspection process of the robot; A path optimization module, which is used to adjust the traveling direction of the robot to avoid danger when encountering abnormal road conditions, generate the center line of the roadway in real time when encountering a curved roadway, and control the robot to travel along the center line of the roadway; and when the actual position of the robot is not on the initial inspection path, determine the actual position of the robot and the nearest path point on the initial inspection path, generate an adjustment path between the actual position and the nearest path point, and control the robot to travel along the adjustment path. A dead reckoning positioning module, which is used to determine the actual inspection path of the robot according to the actual traveling route and direction of the robot, and determine the actual position of the robot by using dead reckoning positioning according to the actual inspection path of the robot. An inspection module, which is used to automatically detect the surrounding environment data during the inspection process of the robot, and generate an inspection report in real time according to the corresponding relationship between the environment data and the actual position of the robot.

[0061] It can be understood that since the robot described in this embodiment is a device for implementing the inspection method of the robot in the mine shaft in the embodiment, for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, refer to the partial description of the method, and details will not be repeated here.

Claims

1. A patrol method of a robot in an underground mine, characterized in that, The method includes: The robot receives the initially issued inspection path and conducts inspections according to the initially issued inspection path; During the inspection process of the robot, multiple laser scanners are used to identify the surrounding environment. When encountering abnormal road conditions, the traveling direction of the robot is adjusted to avoid danger. When encountering a curved roadway, the center line of the roadway is generated in real time, and the robot is controlled to travel along the center line of the roadway; According to the actual traveling route and direction of the robot, the actual inspection path of the robot is determined. Based on the actual inspection path of the robot, dead reckoning positioning is used to determine the actual position of the robot; When the actual position of the robot is not on the initially issued inspection path, the nearest path point between the actual position of the robot and the initially issued inspection path is determined, an adjustment path between the actual position and the nearest path point is generated, and the robot is controlled to travel along the adjustment path; During the inspection process of the robot, the surrounding environment data is automatically detected, and an inspection report is generated in real time according to the corresponding relationship between the environment data and the actual position of the robot.

2. The inspection method of the robot in the underground mine according to claim 1, wherein When encountering a curved roadway, generating the center line of the roadway in real time specifically includes: After the laser scanner identifies a curved roadway, the point cloud data of the curved roadway is obtained in real time. After downsampling and outlier removal processing of the point cloud data, the ground point cloud is segmented to generate a point set of the walls on both sides of the curved roadway; The curve equations are dynamically fitted to the point sets of the walls on both sides of the curved roadway respectively. According to the curve equations, boundary points on both sides of the curved roadway are sampled at equal intervals along the traveling direction of the robot, and a midpoint sequence is calculated; Interpolation is performed on the midpoint sequence to generate a smooth center line of the roadway.

3. The inspection method of the robot in the underground mine according to claim 1, wherein, Determining the actual position of the robot includes: Determining the starting position and initial heading angle of the robot, and determining the wheelbase between the left and right wheels of the robot; Detecting the displacements of the left and right wheels of the robot according to a preset sampling time interval, and calculating the centroid linear velocity and the change amount of the heading angle of the robot within the preset sampling time interval based on the displacements of the left and right wheels and the wheelbase between the left and right wheels; Calculating the average heading angle within the sampling time interval according to the initial heading angle and the change amount of the heading angle, and calculating the displacement increment of the robot within the sampling time interval based on the centroid linear velocity and the average heading angle of the robot; Determining the actual position of the robot according to the starting position and initial heading angle of the robot and based on the change amount of the heading angle and the displacement increment.

4. The inspection method of the robot in the underground mine according to claim 3, wherein The calculation formula for the centroid linear velocity is as follows: ； The calculation formula for the change amount of the heading angle is as follows: ； The calculation formula for the average heading angle is as follows: ； The calculation formula for the displacement increment is as follows: ； ； The calculation formula for the actual position of the robot is as follows: ； ； ； Among them, represents the centroid linear velocity, represents the left wheel displacement, represents the right wheel displacement, represents the sampling time interval, represents the change in heading angle, represents the distance between the left and right wheels, represents the average heading angle, represents the heading angle at the previous moment, represents the abscissa of the robot at the previous moment, represents the ordinate of the robot at the previous moment, represents the displacement increment of the robot in the direction within the sampling time interval, represents the displacement increment of the robot in the direction within the sampling time interval, represents the abscissa of the actual position of the robot, represents the ordinate of the actual position of the robot, represents the heading angle of the actual position of the robot.

5. The inspection method of the robot in the underground mine according to claim 1, characterized in that, The environment data includes image data, gas data, light data, noise data, and ventilation data. The image data is detected by a camera, the gas data is collected and detected by a gas collector, the light data is detected by a light sensor, the noise data is detected by a sound sensor, and the ventilation data is detected by a wind speed sensor.

6. The inspection method of the robot in the underground mine according to claim 5, wherein, The gas data at least includes: gas data of methane, carbon monoxide, carbon dioxide, oxygen, and hydrogen sulfide.

7. The inspection method of the robot in the underground mine according to claim 1, characterized in that The method further includes: After the communication connection between the robot and the remote control center is disconnected, the inspection reports generated in real time are remotely saved in the local memory; after the communication connection between the robot and the remote control center is restored, the inspection reports generated in real time are remotely sent to the control center.

8. The inspection method of the robot in the underground mine according to claim 1, characterized in that, The method further includes: Using the positioning module to obtain the position information of the robot in real time. If the acquisition is successful, the actual position of the robot is corrected according to the position information.

9. The inspection method of the robot in the underground mine according to claim 1, characterized in that, The method further includes: Detecting the remaining power of the robot in real time. When the remaining power is less than the power threshold, a return route is generated according to the actual position of the robot, and the robot is controlled to travel along the return route.

10. A robot, characterized in that, The robot includes: An autonomous mobile platform for receiving the issued initial inspection path and conducting inspections according to the initial inspection path; Multiple laser scanners for identifying the surrounding environment during the inspection of the robot; A path optimization module for adjusting the traveling direction of the robot to avoid danger when encountering abnormal road conditions, generating the center line of the roadway in real time when encountering a curved roadway, and controlling the robot to travel along the center line of the roadway; and when the actual position of the robot is not on the initial inspection path, determining the nearest path point between the actual position of the robot and the initial inspection path, generating an adjustment path between the actual position and the nearest path point, and controlling the robot to travel along the adjustment path; A dead reckoning positioning module for determining the actual inspection path of the robot according to the actual traveling route and direction of the robot, and determining the actual position of the robot by using dead reckoning positioning according to the actual inspection path of the robot; An inspection module for automatically detecting the surrounding environment data during the inspection of the robot and generating an inspection report in real time according to the corresponding relationship between the environment data and the actual position of the robot.

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