A robot inspection method for underground mines and the robot

Abstract

The present invention relates to the field of robot control technology, and discloses a method and robot for patrolling underground mines. The method aims to address the problems of poor accuracy and low efficiency in existing methods. The solution mainly includes: the robot patrols according to an initial patrol path; during the patrol process, it identifies the surrounding environment, and when encountering abnormal road conditions, it adjusts the robot's travel direction to avoid risks. When encountering a curved roadway, it controls the robot to travel along the centerline of the roadway; based on the robot's actual patrol path, it uses dead reckoning to determine the robot's actual position; it generates an adjusted path between the actual position and the nearest path point on the initial patrol path, and controls the robot to travel along the adjusted path; during the patrol process, it automatically detects surrounding environment data and generates a patrol report in real time based on the corresponding relationship between the environmental data and the actual position. The present invention improves the accuracy and efficiency of robot patrols.

Description

Technical Field

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

[0002] Safety risks are high in underground mines, and manual inspections can be dangerous and inefficient. Using robots for underground inspections can replace manual entry into high-risk areas, reducing casualties. Robots can automatically complete routine inspections, freeing up manpower. Furthermore, robots use multiple sensors to monitor complex environments in real time, compensating for oversights in manual inspections and avoiding subjective errors in manual recording, providing objective and continuous monitoring data.

[0003] Traditional patrol robots usually rely on preset fixed paths or single sensors for navigation. Since mine shafts are located underground, and the structure and environment of mine shafts are complex, and metal supports and equipment are densely distributed in mine shafts, they are affected by signal shielding, electromagnetic interference, dust concentration fluctuations, etc. in the mine shafts. Existing positioning methods such as GPS positioning, UWB positioning, and laser positioning all have positioning difficulties and cannot achieve accurate positioning of the robot, which leads to the environmental data obtained by the robot patrol not matching the actual location, and the environmental data is bound to the wrong location coordinates. The environmental data collected in the dangerous area may come from the safe area, which will lead to misjudgment of the dangerous area and the inability to accurately mark the dangerous area, thus forming a monitoring blind spot and poor patrol accuracy. Moreover, when the robot moves in the mine shaft, especially in curved tunnels, it is prone to scratches and collisions, and the patrol efficiency is low. Summary of the Invention

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

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] In a first aspect, the present invention provides a method for patrolling a mine with a robot, the method comprising:

[0007] The robot receives the initial patrol path and patrols according to the initial patrol path;

[0008] During the robot's inspection process, multiple laser scanners are used to identify the surrounding environment. When encountering abnormal road conditions, the robot's direction of travel is adjusted to avoid danger. When encountering a curved lane, the lane centerline is generated in real time and the robot is controlled to move along the lane centerline.

[0009] Determine the actual patrol path of the robot based on its actual route and direction, and use dead reckoning to determine the actual position of the robot based on its actual patrol path.

[0010] When the actual position of the robot is not on the initial patrol path, the robot's actual position and the nearest path point on the initial patrol path are determined, an adjustment path between the actual position and the nearest path point is generated, and the robot is controlled to move along the adjustment path;

[0011] During the robot's inspection process, the surrounding environment data is automatically detected, and an inspection report is generated in real time based on the correspondence between the environmental data and the actual position of the robot.

[0012] Furthermore, when encountering a curved roadway, the roadway centerline is generated in real time, specifically including:

[0013] When the laser scanner identifies a curved lane, it acquires point cloud data of the curved lane in real time, performs downsampling and outlier removal on the point cloud data, and then segments the ground point cloud to generate point sets of the walls on both sides of the curved lane.

[0014] Dynamically fit the curve equation to the point sets on the walls of the curved lane respectively. According to the curve equation, the boundary points on both sides of the curved lane are sampled at equal intervals along the robot's moving direction to calculate the midpoint sequence.

[0015] The midpoint sequence is interpolated to generate a smooth lane centerline.

[0016] Furthermore, determining the actual position of the robot includes:

[0017] Determine the robot's starting position and initial heading angle, and determine the distance between the robot's left and right wheels;

[0018] Detect the displacement of the left and right wheels of the robot according to a preset sampling time interval, and calculate the center of mass linear velocity and heading angle change of the robot within the preset sampling time interval based on the left and right wheel displacements and the left and right wheel spacing;

[0019] Calculating the average heading angle within the sampling time interval according to the initial heading angle and the heading angle change, and calculating the displacement increment of the robot within the sampling time interval according to the center of mass linear velocity of the robot and the average heading angle;

[0020] The actual position of the robot is determined according to the starting position and initial heading angle of the robot and based on the heading angle change and the displacement increment.

[0021] Furthermore, the calculation formula of the center of mass linear velocity is as follows:

[0022] ;

[0023] The calculation formula of the heading angle change is as follows:

[0024] ;

[0025] The calculation formula of the average heading angle is as follows:

[0026] ;

[0027] The calculation formula of the displacement increment is as follows:

[0028] ;

[0029] ;

[0030] The actual position of the robot is calculated as follows:

[0031] ;

[0032] ;

[0033] ;

[0034] in, represents the linear velocity of the center of mass, Indicates the left wheel displacement, represents the right wheel displacement, represents the sampling time interval, Indicates the change in heading angle, Indicates the distance between left and right wheels. represents the average heading angle, Indicates the heading angle at the previous moment, Indicates the horizontal coordinate of the robot at the last moment, Indicates the vertical coordinate of the robot at the last moment, Indicates the robot's position during the sampling time interval The displacement increment in the direction, Indicates that the robot is in The displacement increment in the direction, The horizontal coordinate representing the actual position of the robot, The vertical coordinate representing the actual position of the robot, The heading angle representing the actual position of the robot.

[0035] 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.

[0036] Furthermore, the gas data at least includes gas data of methane, carbon monoxide, carbon dioxide, oxygen and hydrogen sulfide.

[0037] Furthermore, the method further comprises:

[0038] When the communication connection between the robot and the remote control center is disconnected, the inspection report generated in real time is remotely saved in the local storage; when the communication connection between the robot and the remote control center is restored, the inspection report generated in real time is remotely sent to the control center.

[0039] Furthermore, the method further comprises:

[0040] The positioning module is used 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.

[0041] Furthermore, the method further comprises:

[0042] The remaining power of the robot is detected 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 move along the return route.

[0043] In a second aspect, the present invention provides a robot, comprising:

[0044] An autonomous mobile platform, configured to receive an initial patrol route and conduct patrols according to the initial patrol route;

[0045] Multiple laser scanners are used to identify the surrounding environment during the robot's inspection process;

[0046] The path optimization module is used to adjust the robot's direction of travel to avoid risks when encountering abnormal road conditions. When encountering a curved roadway, it generates the roadway centerline in real time and controls the robot to move along the roadway centerline. Furthermore, when the robot's actual position is not on the initial patrol path, it determines the robot's actual position and the nearest path point on the initial patrol path, generates an adjusted path between the actual position and the nearest path point, and controls the robot to move along the adjusted path.

[0047] The dead reckoning positioning module is used to determine the actual patrol path of the robot based on its actual route and direction, and to determine the actual position of the robot based on its actual patrol path using dead reckoning positioning.

[0048] The inspection module is used to automatically detect the surrounding environment data during the robot inspection process and generate an inspection report in real time based on the correspondence between the environmental data and the actual position of the robot.

[0049] The beneficial effects of the present invention are as follows: the robot patrol method and robot provided by the present invention in underground mines identify the environment through a laser scanner, adjust the direction when encountering anomalies, and control the robot to move along the center line of the tunnel when encountering a curved tunnel, thereby realizing real-time environmental perception and dynamic path adjustment, thereby reducing the risk of collision and scratching, and improving the robot's movement efficiency and patrol efficiency; through track calculation positioning, the robot can be accurately positioned in the complex structure and environment of the mine, as well as after the robot's dynamic path adjustment, thereby improving the accuracy of the robot's positioning, and improving the accuracy of the correspondence between environmental data and position coordinates, thereby improving the robot's patrol accuracy; when the robot's position deviates, an adjustment path is generated, thereby realizing the robot's self-correction, ensuring the continuity of the patrol, and reducing manual intervention; the robot automatically generates a patrol report based on the collected environmental data, realizing real-time data recording and integration, and improving patrol efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of a process flow of a robot inspection method in an underground mine provided in an embodiment;

[0051] Figure 2 A schematic structural diagram of a robot provided in an embodiment. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment.

[0053] Some of the processes described in the specification of the present invention and the figures above include multiple operations that appear in a specific order. However, it should be understood that these operations may not be performed in the order in which they appear herein or may be performed in parallel. The sequence numbers of the operations are merely used to distinguish between different operations and do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be performed sequentially or in parallel.

[0054] Due to the complex structure and environment of mine shafts, robots are prone to scratches and collisions when moving in mine shafts, resulting in low movement efficiency and difficulty in accurately positioning the robots, which in turn leads to low accuracy and efficiency of robot inspections.

[0055] In order to improve the accuracy and efficiency of robot inspections in underground mines, the technical solution of the present invention is proposed. In the present invention, the robot receives an 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 robot's travel direction is adjusted to avoid risks. When encountering a curved tunnel, the tunnel centerline is generated in real time, and the robot is controlled to travel along the tunnel centerline. According to the actual travel route and direction of the robot, the actual inspection path of the robot is determined. According to the actual inspection path of the robot, the actual position of the robot is determined by using track dead reckoning positioning. 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, 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 based on the correspondence between the environment data and the actual position of the robot.

[0056] Specifically, the initial patrol path is first sent to the robot through the remote control center. By setting the initial patrol path, the real-time computing load can be reduced, the startup time can be shortened, and the preset path can be used as a safety benchmark to provide a reference coordinate system for subsequent dynamic adjustments. Then, in the process of patrolling according to the initial path, the robot uses multiple laser scanners to perceive the environment and dynamically avoid obstacles, thereby avoiding robot collisions. When encountering a curved lane, the robot is controlled to move along the center line of the lane to avoid scratches and improve the robot's travel efficiency. At the same time, during the robot's patrol process, the robot is accurately positioned according to its actual route and direction using track-reckoning positioning, thereby improving the accuracy of the robot's positioning. After the robot deviates from the initial patrol path, the robot is controlled to correct itself, ensuring the continuity of the patrol, reducing manual intervention, and further improving patrol efficiency. Finally, during the robot patrol process, the robot automatically generates a patrol report based on the collected environmental data, realizing real-time data recording and integration, and further improving patrol efficiency.

[0057] The technical solution of this embodiment will be clearly and completely described below in conjunction with the drawings in this embodiment. Obviously, the described embodiment is only a part of the embodiments of the present invention, rather than all the embodiments.

[0058] Figure 1 A schematic diagram showing a process flow of a robot inspection method in a mine, see Figure 1 , the method comprises the following steps:

[0059] Step 1: The robot receives the initial patrol path and patrols according to the initial patrol path.

[0060] In actual applications, an initial patrol path is generated based on the actual conditions of the mine. The remote control center encodes the initial patrol path into an ordered sequence of path points. Each path point contains position coordinates and the desired heading angle. The path point is encapsulated in JSON format and transmitted to the robot through the 5G network. The robot's main control system parses the path data and generates initial motion instructions through the global path planning module. The robot's drive system tracks the initial patrol path based on the PID controller, so that the robot can patrol according to the initial patrol path.

[0061] Step 2: During the robot's inspection process, multiple laser scanners are used to identify the surrounding environment. When encountering abnormal road conditions, the robot's direction of travel is adjusted to avoid risks. When encountering a curved lane, the lane centerline is generated in real time, and the robot is controlled to move along the lane centerline.

[0062] In practical applications, a circular array of LiDARs collects environmental point cloud data in real time. An improved ICP algorithm is used to register these multi-radar point clouds and construct a local obstacle grid map. When obstacles or potholes are detected, an obstacle avoidance path is calculated in real time. When a curved roadway is detected, the robot's boundaries are fitted to generate the roadway centerline. The robot adjusts its direction of travel when encountering an anomaly, and controls its movement along the centerline when encountering a curved roadway. This enables real-time environmental perception and dynamic path adjustment, reducing the risk of collisions and scrapes and improving the robot's mobility and inspection efficiency.

[0063] In this embodiment, when encountering a curved lane, the lane centerline is generated in real time, specifically including:

[0064] Step 21: When the laser scanner identifies a curved lane, it acquires point cloud data of the curved lane in real time, performs downsampling and outlier removal on the point cloud data, and then segments the ground point cloud to generate point sets of the walls on both sides of the curved lane.

[0065] In practical applications, first, a voxel filter can be used to downsample the original point cloud to reduce the subsequent computational complexity and improve real-time performance. Then, a statistical outlier removal algorithm can be used to remove outliers. The average distance between each point and its 50 nearest neighbors is calculated. If the distance exceeds the mean + 3 times the standard deviation, it is determined to be an outlier. Removing outliers can filter out abnormal points caused by laser reflection noise or dynamic objects, thereby improving the robustness of subsequent segmentation and fitting. Then, the ground can be extracted by plane fitting based on the random sampling consistency algorithm. The ground plane equation is fitted to extract the point set that meets the plane constraint. 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 tunnel structure (such as left-right symmetry), the point sets on the left and right sides of the curved tunnel are divided. If a segmentation error occurs (such as temporary obstacle occlusion), the boundary can be corrected by temporal association of continuous frame point clouds.

[0066] Step 22: Dynamically fit the curve equation to the point sets on the walls on both sides of the curved lane respectively, sample the boundary points on both sides of the curved lane at equal intervals along the robot's moving direction according to the curve equation, and calculate the midpoint sequence.

[0067] In practical applications, the B-spline curve can be used to fit the point set of the walls on both sides of the curved tunnel. During the fitting process, the least squares method is used to solve the control points, and the density of the control points is adaptively adjusted according to the curvature change. Then, the robot moves in the direction of travel ( The left and right curve equations are sampled at fixed intervals (e.g., 20 cm) along the axis, and the geometric midpoint of each pair of left and right points is calculated to generate a midpoint sequence. During the sampling process, the sampling points are interpolated and aligned to ensure the same number of left and right points and uniform spacing between the midpoint sequences. This facilitates subsequent path tracking control and supports centerline generation in asymmetric lanes.

[0068] Step 23: Interpolate the midpoint sequence to generate a smooth lane centerline.

[0069] In practical applications, a cubic uniform B-spline curve is constructed with the midpoint sequence as the type value point, and the maximum curvature is constrained according to the minimum turning radius of the robot. If the curvature exceeds the limit, additional control points are inserted for local smoothing, and finally a smooth lane centerline is generated. The generated lane centerline has continuous curvature, meets the robot kinematic constraints, and has a small lateral tracking error.

[0070] Step 3: Determine the actual patrol path of the robot based on its actual route and direction. Use dead reckoning to determine the actual position of the robot based on its actual patrol path.

[0071] In this embodiment, determining the actual position of the robot includes:

[0072] Step 31: Determine the robot's starting position and initial heading angle, and determine the distance between the robot's left and right wheels.

[0073] 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 to a known mark point and enter the coordinate value. At the same time, use a high-precision IMU (such as MPU-9250) to measure the initial heading angle and the center distance of the left and right drive wheels. After completing the above data calibration, write it into the robot's configuration file. For example, the starting position: ( , ) = (0, 0); Initial heading angle: =0.0rad (towards Axle positive direction); left and right wheel distance: =0.5m.

[0074] Step 32: Detect the displacement of the left and right wheels of the robot according to a preset sampling time interval, and calculate the center of mass linear velocity and heading angle change of the robot within the preset sampling time interval based on the left and right wheel displacements and the left and right wheel distance.

[0075] In practical applications, an incremental photoelectric encoder (such as HEDL-5640) can be used to detect the pulse counts of the robot's left and right wheels according to a preset sampling time interval, and the left and right wheel displacements can be calculated based on the wheel radius and the number of encoder pulses corresponding to one wheel rotation. The robot's center of mass linear velocity and heading angle change 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.

[0076] In this embodiment, the calculation formula of the center of mass linear velocity is as follows:

[0077] ;

[0078] in, Indicates the linear velocity of the center of mass (unit: m / s), Indicates the left wheel displacement (unit: m), Indicates the right wheel displacement (unit: m), Indicates the sampling time interval (unit: s).

[0079] The calculation formula of the heading angle change is as follows:

[0080] ;

[0081] in, Indicates the heading angle change (unit: rad).

[0082] Step 33: Calculate the average heading angle within the sampling time interval based on the initial heading angle and the heading angle change, and calculate the displacement increment of the robot within the sampling time interval based on the center of mass linear velocity of the robot and the average heading angle.

[0083] In this embodiment, the calculation formula of the average heading angle is as follows:

[0084] ;

[0085] in, Indicates the average heading angle (unit: rad), Indicates the heading angle at the last moment (unit: rad), in the first sampling time interval, is the initial heading angle.

[0086] The calculation formula of the displacement increment is as follows:

[0087] ;

[0088] ;

[0089] in, Indicates the robot's position during the sampling time interval The displacement increment in the direction (unit: m), Indicates that the robot is in The displacement increment in the direction (unit: m), and Directional projection coefficient representing the heading angle.

[0090] Step 34: Determine the actual position of the robot according to the starting position and initial heading angle of the robot and based on the heading angle change and the displacement increment.

[0091] The actual position of the robot is updated according to the calculated heading angle change and displacement increment, namely:

[0092] ;

[0093] ;

[0094] ;

[0095] in, The horizontal coordinate representing the actual position of the robot, The vertical coordinate representing the actual position of the robot, The heading angle representing the actual position of the robot, Indicates the horizontal coordinate of the robot at the last moment, Indicates the vertical coordinate of the robot at the previous moment.

[0096] In the process of calculating using the above formula, at the first sampling time interval, and Set as the coordinate value corresponding to the robot's starting position, Set as the initial heading angle, in subsequent sampling time intervals, 、 and The corresponding value is set to the value calculated at the last sampling time interval. 、 and , to calculate the actual position of the robot at the next sampling time interval.

[0097] Through the above steps, the robot can be accurately positioned in the complex structure and environment of the mine shaft, and after the robot's dynamic path adjustment, which improves the accuracy of the correspondence between environmental data and position coordinates, thereby improving the robot's inspection accuracy.

[0098] Step 4: 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 move along the adjustment path.

[0099] In this embodiment, priority is given to controlling the robot to automatically avoid obstacles and move along the center line of the curved lane. When it is detected that there are no obstacles or lanes in front of the robot, the robot automatically corrects itself so that the robot moves along the initial patrol path to the greatest extent possible, thereby ensuring the continuity of the patrol and reducing manual intervention.

[0100] In practical applications, the robot's actual position and the sequence of path points corresponding to the initial patrol path are acquired in real time. The line segments formed by adjacent points in the path are traversed. For each line segment, the shortest distance from the robot's actual position 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. An adjustment path is then generated from the robot's actual position to the target point, and the robot is controlled to move along the adjusted path. As the robot moves along the adjusted path, if it encounters unusual road conditions, it prioritizes adjusting its direction to avoid danger. When encountering a curved roadway, it prioritizes controlling the robot to move along the roadway's centerline until it reaches the vicinity of the target point. Once the robot reaches the vicinity of the target point, it switches back to tracking the initial patrol path. Through this process, the robot can automatically detect position offsets in real time and generate a smooth adjustment path. Combined with the control algorithm, it can quickly and stably return to the initial patrol path.

[0101] Step 5: During the robot inspection process, the surrounding environment data is automatically detected, and an inspection report is generated in real time based on the correspondence between the environmental data and the actual position of the robot.

[0102] 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 includes at least: 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.

[0103] Through the above-mentioned equipment, comprehensive environmental detection of mine shafts can be achieved. By integrating multi-source data such as images, gas, noise, light, ventilation flow, etc., safety assessment of mine shaft structures and early warning of safety hazards can be achieved.

[0104] In this embodiment, the method also includes: when the communication connection between the robot and the remote control center is disconnected, the real-time inspection report generated is remotely saved in the local storage; when the communication connection between the robot and the remote control center is restored, the real-time inspection report generated is remotely sent to the control center.

[0105] Due to the complex structure and environment of mine shafts and poor network conditions, the network connection between the robot and the remote control center is easily unstable. In this embodiment, local storage when the network is disconnected can avoid data loss caused by communication interruption, ensure the continuity of the real-time generation process of inspection reports, and automatically re-upload after the connection is restored to achieve complete archiving of the data.

[0106] In this embodiment, the method further includes: using a positioning module to acquire position information of the robot in real time, and if the acquisition is successful, correcting the actual position of the robot according to the position information.

[0107] It can be understood that when the robot reaches a position in the mine shaft that meets the positioning conditions, the robot's positioning module can be controlled to perform positioning, and the actual position of the robot can be updated according to the position information obtained by the positioning module to further improve the accuracy of the robot's positioning.

[0108] In this embodiment, the method further includes: detecting the remaining power of the robot in real time, and when the remaining power is less than a power threshold, generating a return route according to the actual position of the robot, and controlling the robot to move along the return route.

[0109] It can be understood that real-time power monitoring and return functions can avoid mission interruptions, shorten the charging interval through intelligent return, increase the effective working time of the robot, and avoid the risk of loss of control caused by sudden power failure of sensors or control systems due to insufficient power, significantly improving the reliability and economy of the robot system, while providing a data basis for multi-machine collaboration and long-term operation and maintenance.

[0110] To sum up, the robot patrol method in the mine provided by this embodiment uses a laser scanner to identify the environment, adjusts the direction when encountering anomalies, and controls the robot to move along the center line of the tunnel when encountering a curved tunnel, realizing real-time environmental perception and dynamic path adjustment, thereby reducing the risk of collision and scratching, and improving the robot's movement efficiency and patrol efficiency; through track calculation positioning, the robot's positioning accuracy is improved in the complex structure and environment of the mine, and the accuracy of the correspondence between environmental data and position coordinates is improved, thereby improving the robot's patrol accuracy; when the robot's position deviates, an adjustment path is generated, which realizes the robot's self-correction, ensures the continuity of the patrol, and reduces manual intervention; the robot automatically generates a patrol report based on the collected environmental data, realizes real-time data recording and integration, and improves patrol efficiency.

[0111] Based on the above technical solution, this embodiment also proposes a robot, please refer to Figure 2 , the robot comprises:

[0112] An autonomous mobile platform, configured to receive an initial patrol route and conduct patrols according to the initial patrol route;

[0113] Multiple laser scanners are used to identify the surrounding environment during the robot's inspection process;

[0114] The path optimization module is used to adjust the robot's direction of travel to avoid risks when encountering abnormal road conditions. When encountering a curved roadway, it generates the roadway centerline in real time and controls the robot to move along the roadway centerline. Furthermore, when the robot's actual position is not on the initial patrol path, it determines the robot's actual position and the nearest path point on the initial patrol path, generates an adjusted path between the actual position and the nearest path point, and controls the robot to move along the adjusted path.

[0115] The dead reckoning positioning module is used to determine the actual patrol path of the robot based on its actual route and direction, and to determine the actual position of the robot based on its actual patrol path using dead reckoning positioning.

[0116] The inspection module is used to automatically detect the surrounding environment data during the robot inspection process and generate an inspection report in real time based on the correspondence between the environmental data and the actual position of the robot.

[0117] It can be understood that since the robot described in this embodiment is a device for implementing the robot inspection method described in the embodiment in the mine, for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant points, please refer to the partial description of the method, and no further details will be given here.

Claims

1. A robot inspection method for underground mines, characterized in that: The method comprises: The robot receives the initial patrol path and patrols according to the initial patrol path; During the robot's inspection process, multiple laser scanners are used to identify the surrounding environment. When encountering abnormal road conditions, the robot's direction of travel is adjusted to avoid danger. When encountering a curved lane, the lane centerline is generated in real time and the robot is controlled to move along the lane centerline. Determine the actual patrol path of the robot based on its actual route and direction, and use dead reckoning to determine the actual position of the robot based on its actual patrol path. When the actual position of the robot is not on the initial patrol path, the robot's actual position and the nearest path point on the initial patrol path are determined, an adjustment path between the actual position and the nearest path point is generated, and the robot is controlled to move along the adjustment path; During the robot's inspection process, it automatically detects the surrounding environment data and generates an inspection report in real time based on the correspondence between the environmental data and the robot's actual position; When encountering a curved roadway, the roadway centerline is generated in real time, including: When the laser scanner identifies a curved lane, it acquires point cloud data of the curved lane in real time, performs downsampling and outlier removal on the point cloud data, and then segments the ground point cloud to generate point sets of the walls on both sides of the curved lane. Dynamically fit the curve equation to the point sets on the walls of the curved lane respectively. According to the curve equation, the boundary points on both sides of the curved lane are sampled at equal intervals along the robot's moving direction to calculate the midpoint sequence. interpolating the midpoint sequence to generate a smooth lane centerline; Determine the actual position of the robot, including: Determine the robot's starting position and initial heading angle, and determine the distance between the robot's left and right wheels; Detect the displacement of the left and right wheels of the robot according to a preset sampling time interval, and calculate the center of mass linear velocity and heading angle change of the robot within the preset sampling time interval based on the left and right wheel displacements and the left and right wheel spacing; Calculating the average heading angle within the sampling time interval according to the initial heading angle and the heading angle change, and calculating the displacement increment of the robot within the sampling time interval according to the center of mass linear velocity of the robot and the average heading angle; Determining the actual position of the robot based on the starting position and initial heading angle of the robot and the heading angle change and the displacement increment; The calculation formula of the center of mass linear velocity is as follows: ； The calculation formula of the heading angle change is as follows: ； The calculation formula of the average heading angle is as follows: ； The calculation formula of the displacement increment is as follows: ； ； The actual position of the robot is calculated as follows: ； ； ； in, represents the linear velocity of the center of mass, Indicates the left wheel displacement, represents the right wheel displacement, represents the sampling time interval, Indicates the change in heading angle, Indicates the distance between left and right wheels. represents the average heading angle, Indicates the heading angle at the previous moment, Indicates the horizontal coordinate of the robot at the last moment, Indicates the vertical coordinate of the robot at the last moment, Indicates the robot's position during the sampling time interval The displacement increment in the direction, Indicates that the robot is in The displacement increment in the direction, The horizontal coordinate representing the actual position of the robot, The vertical coordinate representing the actual position of the robot, The heading angle representing the actual position of the robot.

2. The robot inspection method in an underground mine according to claim 1, characterized in that: 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.

3. The robot inspection method in an underground mine according to claim 2, characterized in that: The gas data at least includes gas data of methane, carbon monoxide, carbon dioxide, oxygen and hydrogen sulfide.

4. The robot inspection method in an underground mine according to claim 1, characterized in that: The method further comprises: When the communication connection between the robot and the remote control center is disconnected, the inspection report generated in real time is remotely saved in the local storage; when the communication connection between the robot and the remote control center is restored, the inspection report generated in real time is remotely sent to the control center.

5. The robot inspection method in underground mines according to claim 1, characterized in that: The method further comprises: The positioning module is used 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.

6. The robot inspection method in an underground mine according to claim 1, characterized in that: The method further comprises: The remaining power of the robot is detected 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 move along the return route.

7. A robot, characterized in that: The robot comprises: An autonomous mobile platform, configured to receive an initial patrol route and conduct patrols according to the initial patrol route; Multiple laser scanners are used to identify the surrounding environment during the robot's inspection process; The path optimization module is used to adjust the robot's direction of travel to avoid risks when encountering abnormal road conditions. When encountering a curved roadway, it generates the roadway centerline in real time and controls the robot to move along the roadway centerline. Furthermore, when the robot's actual position is not on the initial patrol path, it determines the robot's actual position and the nearest path point on the initial patrol path, generates an adjusted path between the actual position and the nearest path point, and controls the robot to move along the adjusted path. The dead reckoning positioning module is used to determine the actual patrol path of the robot based on its actual route and direction, and to determine the actual position of the robot based on its actual patrol path using dead reckoning positioning. The inspection module is used to automatically detect the surrounding environment data during the robot's inspection process and generate an inspection report in real time based on the correspondence between the environmental data and the actual position of the robot; When encountering a curved roadway, the roadway centerline is generated in real time, including: When the laser scanner identifies a curved lane, it acquires point cloud data of the curved lane in real time, performs downsampling and outlier removal on the point cloud data, and then segments the ground point cloud to generate point sets of the walls on both sides of the curved lane. Dynamically fit the curve equation to the point sets on the walls of the curved lane respectively. According to the curve equation, the boundary points on both sides of the curved lane are sampled at equal intervals along the robot's moving direction to calculate the midpoint sequence. interpolating the midpoint sequence to generate a smooth lane centerline; Determine the actual position of the robot, including: Determine the robot's starting position and initial heading angle, and determine the distance between the robot's left and right wheels; Detect the displacement of the left and right wheels of the robot according to a preset sampling time interval, and calculate the center of mass linear velocity and heading angle change of the robot within the preset sampling time interval based on the left and right wheel displacements and the left and right wheel spacing; Calculating the average heading angle within the sampling time interval according to the initial heading angle and the heading angle change, and calculating the displacement increment of the robot within the sampling time interval according to the center of mass linear velocity of the robot and the average heading angle; Determining the actual position of the robot based on the starting position and initial heading angle of the robot and the heading angle change and the displacement increment; The calculation formula of the center of mass linear velocity is as follows: ； The calculation formula of the heading angle change is as follows: ； The calculation formula of the average heading angle is as follows: ； The calculation formula of the displacement increment is as follows: ； ； The actual position of the robot is calculated as follows: ； ； ； in, represents the linear velocity of the center of mass, Indicates the left wheel displacement, represents the right wheel displacement, represents the sampling time interval, Indicates the change in heading angle, Indicates the distance between left and right wheels. represents the average heading angle, Indicates the heading angle at the previous moment, Indicates the horizontal coordinate of the robot at the last moment, Indicates the vertical coordinate of the robot at the last moment, Indicates the robot's position during the sampling time interval The displacement increment in the direction, Indicates that the robot is in The displacement increment in the direction, The horizontal coordinate representing the actual position of the robot, The vertical coordinate representing the actual position of the robot, The heading angle representing the actual position of the robot.

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