A Robot System and Method for Advanced Geological Prediction at Tunnel Faces

Abstract

This invention discloses a robot system and method for advanced geological prediction at tunnel faces, comprising: a collapse monitoring module and a radar detection module mounted on a robot transport module; the collapse monitoring module monitors the stability of the tunnel face by acquiring rock mass images and performing laser vibration measurements; the radar detection module includes a ground-penetrating radar detection arm holding a ground-penetrating radar, a vertical detection arm driving the vertical movement of the ground-penetrating radar detection arm, and a horizontal detection arm driving the horizontal movement of the ground-penetrating radar detection arm; one end of the vertical detection arm is mounted on the horizontal detection arm, and the other end of the vertical detection arm is mounted on the ground-penetrating radar detection arm. Based on the detection mode, when there are no abnormalities in the stability monitoring of the tunnel face, the system controls the ground-penetrating radar detection arm to drive the ground-penetrating radar to perform advanced geological prediction of the tunnel face. Under the premise of ensuring the safety of the tunnel face, this system achieves unmanned advanced geological prediction, solving the problems of difficult manual operation, limited survey line layout, and the risk of surrounding rock collapse associated with traditional ground-penetrating radar prediction.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering advanced geological forecasting and disaster monitoring and early warning technology, and in particular to a tunnel face advanced geological forecasting robot system and method. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Advanced geological prediction technology is essential for tunnel construction in areas with adverse geological conditions. Ground-penetrating radar, as a convenient, fast, non-destructive, short-range geophysical exploration method, has been widely used in advanced geological prediction.

[0004] However, traditional artificial ground-penetrating radar forecasting currently has the following problems in practical operation:

[0005] (1) The equipment is bulky, difficult for personnel to operate, and time-consuming and labor-intensive;

[0006] (2) The layout of survey lines is limited, making it impossible to effectively detect adverse geological hazards;

[0007] (3) The working face is prone to dangers such as falling blocks and collapses, which can easily cause injury to operators and damage to equipment; in particular, current tunnel projects generally pass through extreme geological conditions and extreme construction environments, increasing the risk of sudden disasters and putting construction workers in extreme working environments. Intelligent proactive prevention and control of major geological disasters and unmanned intelligent construction are the mainstream development trends of tunnel construction. Summary of the Invention

[0008] To address the aforementioned problems, this invention proposes a robotic system and method for advanced geological prediction at tunnel faces. It employs a collapse monitoring module to monitor the stability of the tunnel face and a remotely controlled radar detection module for automated geological prediction. This achieves remote, unmanned advanced geological prediction while ensuring the safety of the tunnel face area. It solves the problems of difficulty in manual operation, limited survey line layout, and the risk of surrounding rock collapse associated with traditional geological radar prediction.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] In a first aspect, the present invention provides a robot system for advanced geological prediction at the tunnel face, comprising: a robot transport module, a collapse monitoring module, and a radar detection module;

[0011] The robot's transport module is equipped with a collapse monitoring module and a radar detection module;

[0012] The collapse monitoring module is used to monitor the stability of the working face by acquiring rock mass images and performing laser vibration measurements.

[0013] The radar detection module includes a ground-penetrating radar detection arm that holds the ground-penetrating radar, a vertical detection arm that drives the ground-penetrating radar detection arm to move vertically, and a horizontal detection arm that drives it to move laterally. One end of the vertical detection arm is located on the horizontal detection arm, and the other end of the vertical detection arm is equipped with the ground-penetrating radar detection arm. According to the set detection mode, when there are no abnormalities in the stability monitoring of the working face, the ground-penetrating radar detection arm is controlled to drive the ground-penetrating radar to perform advanced geological prediction of the working face.

[0014] As an optional implementation, the collapse monitoring module includes an image monitoring system and a laser vibration monitoring system; the image monitoring system is used to collect information on the rock mass structure at the working face, identify block locations, and monitor falling rocks in real time, while the laser vibration monitoring system is used to monitor changes in displacement, velocity, and natural vibration frequency in the working face area.

[0015] As an alternative implementation, the bottom end of the vertical detection arm is mounted on the horizontal detection arm via a sliding base. A movable bracket is provided on the base so that when radar detection is not performed, the bottom end of the vertical detection arm can be rotated 90 degrees around the movable bracket and mounted on the top of the robot transport module.

[0016] As an alternative implementation, the top of the robot carrying module is provided with a groove that matches the vertical detection arm. When it reaches the designated position for radar detection, the vertical detection arm rotates 90 degrees around the movable support and stands upright to work.

[0017] As an alternative implementation, a movable bracket is provided at the connection between the vertical detection arm and the ground-penetrating radar detection arm, so that the ground-penetrating radar detection arm is folded when the vertical detection arm is mounted on top of the robot carrying module.

[0018] As an alternative implementation, the front end of the robot carrying module is connected to the middle position of the lateral detection arm via a bracket. The lateral detection arm includes a first track and a second track. The first track includes a parallel first lateral mechanical arm, and the second track includes a parallel second lateral mechanical arm. Both ends of the first lateral mechanical arm are connected to the second lateral mechanical arm.

[0019] The first horizontal robotic arm on one side is movably connected to the corresponding second horizontal robotic arm at both ends, and the first horizontal robotic arm on the other side is detachably connected to the corresponding second horizontal robotic arm at both ends; so that when radar detection is not performed, the second horizontal robotic arm is placed on both sides of the robot carrying module, and the first horizontal robotic arm is placed at the front end of the robot carrying module.

[0020] As an alternative implementation, when the radar detection is performed at the designated location, the second transverse robotic arms on both sides extend to the same horizontal position as the first transverse robotic arm in front. The detachable connection parts are snapped together to form a transverse detection arm that drives the vertical detection arm to move laterally, thereby performing radar detection on the face of the tunnel.

[0021] As an alternative implementation, active obstacle-crossing devices are mounted on both sides of the ground-penetrating radar, and the retraction amount of the ground-penetrating radar is controlled by pressure sensors.

[0022] As an alternative implementation, the detection modes of the radar detection module include: a continuous measurement mode for flat working faces, a spot measurement mode for uneven working faces, and a wheel track measurement mode for detecting the location of abnormal bodies.

[0023] Secondly, the present invention provides a method for operating a tunnel face advanced geological prediction robot system, employing the tunnel face advanced geological prediction robot system described in the first aspect, comprising:

[0024] Driven by a robot transport module, the system moves to the area below the initial support, where a collapse monitoring module collects rock mass images and performs laser vibration measurements to monitor the stability of the tunnel face.

[0025] When the stability monitoring of the working face shows no abnormalities, the robot is driven by the transport module to move below the working face. The second horizontal mechanical arm of the horizontal detection arm is controlled to extend to the same horizontal position as the first horizontal mechanical arm and extend the load-bearing support legs. At the same time, the vertical detection arm is controlled to stand upright, so that the vertical detection arm moves laterally along the horizontal detection arm. According to the set detection mode, the ground radar detection arm is controlled to drive the ground radar to perform advanced geological prediction of the working face.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] This invention proposes a robot system and method for advanced geological forecasting at the tunnel face based on intelligent monitoring. Addressing the challenges of traditional geological radar forecasting, such as difficulties in manual operation, limitations in survey line layout, and the risk of surrounding rock collapse, this invention employs a collapse monitoring module to monitor tunnel face stability and provide early warning of collapses during tunnel construction. After the robot is transported to a designated location by a robot transport module, the radar detection module is remotely controlled to automatically perform geological forecasting. This achieves remote, unmanned advanced geological forecasting while ensuring the safety of the tunnel face area.

[0028] This invention, based on real-time assessment of tunnel face stability, proposes an advanced automated geological forecasting mechanism that integrates "tunnel face stability monitoring → tunnel face collapse early warning → automated geological forecasting → automated interpretation of forecast data," thus revolutionizing the geological forecasting process. Compared with existing research, using robots for tunnel face geological forecasting can effectively avoid damage to personnel and equipment, improve the speed and accuracy of tunnel face geological forecasting, and ensure the safe construction of tunnel projects.

[0029] This invention proposes a robot system and method for advanced geological prediction of tunnel face based on intelligent monitoring, which replaces the traditional manual geological prediction of tunnel face, realizes remote unmanned and precise detection of adverse geological bodies in front of the tunnel face in complex environments, and avoids personnel and equipment losses caused by tunnel face collapse.

[0030] This invention proposes a robot system and method for advanced geological prediction of tunnel faces based on intelligent monitoring. This system enables more rational and uniform survey line layout, sets detection modes according to the conditions of the tunnel face, and achieves stable and uniform continuous measurement, effectively improving the accuracy and efficiency of advanced geological prediction. Even in environments with a large number of workers, it can monitor the entire tunnel face, achieving rapid, real-time, and intelligent stable monitoring of the tunnel face.

[0031] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0032] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0033] Figure 1 This is a schematic diagram of the structure of the tunnel face advanced geological prediction robot system provided in Embodiment 1 of the present invention when performing radar detection;

[0034] Figure 2 This is a schematic diagram of the tunnel face advanced geological prediction robot system provided in Embodiment 1 of the present invention during its movement.

[0035] The components include: 1. Robot transport module; 2. Collapse monitoring module; 3. Radar detection module; 4. Ground-penetrating radar detection arm; 5. Vertical detection arm; 6. Horizontal detection arm; 7. Active obstacle-crossing device; 8. Ground-penetrating radar; 9. Pressure sensor; 10. Load-bearing support leg; 11. First horizontal robotic arm; 12. Second horizontal robotic arm. Detailed Implementation

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0039] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0040] Example 1

[0041] This embodiment provides a tunnel face advanced geological prediction robot system based on intelligent monitoring, including: a robot transport module, a collapse monitoring module, and a radar detection module; addressing the difficulties of manual operation, limited survey line layout, and risks such as surrounding rock collapse in traditional geological radar prediction, the collapse monitoring module is used to monitor the stability of the tunnel face and provide early warning of collapse during tunnel construction. When the stability monitoring of the tunnel face is normal, the robot transport module is remotely controlled to travel to a designated location, and the radar detection module is remotely controlled to automatically perform geological prediction. Under the premise of ensuring the safety of the tunnel face area, remote unmanned advanced geological prediction is achieved.

[0042] like Figure 1 As shown, it specifically includes:

[0043] The robot's transport module is equipped with a collapse monitoring module and a radar detection module;

[0044] The collapse monitoring module is used to monitor the stability of the working face by acquiring rock mass images and performing laser vibration measurements.

[0045] The radar detection module includes a ground-penetrating radar detection arm that holds the ground-penetrating radar, a vertical detection arm that drives the ground-penetrating radar detection arm to move vertically, and a horizontal detection arm that drives it to move laterally. One end of the vertical detection arm is located on the horizontal detection arm, and the other end of the vertical detection arm is equipped with the ground-penetrating radar detection arm. According to the set detection mode, when there are no abnormalities in the stability monitoring of the working face, the ground-penetrating radar detection arm is controlled to drive the ground-penetrating radar to perform advanced geological prediction of the working face.

[0046] In this embodiment, the robot carrying module 1 is electrically driven, with a curb weight of ≤0.5t, a battery life of ≥5 hours, a travel speed of ≥5 km / h, a maximum obstacle crossing height of 220mm, an obstacle crossing width of 600mm, and the ability to stably pass through complex road conditions such as mud and water, and adapt to the harsh environment of tunnels.

[0047] As an alternative implementation, the robot transport module 1 is equipped with ultrasonic, infrared, and laser coupled obstacle avoidance technology, which can realize autonomous walking control and active obstacle avoidance in the terrain environment inside the tunnel.

[0048] In this embodiment, the collapse monitoring module 2 includes an image monitoring system and a laser vibration monitoring system. The image monitoring system is used to collect information on the rock mass structure at the working face, identify block locations, and monitor falling rocks in real time to determine whether the working face area is safe.

[0049] Laser vibration monitoring systems are used to monitor changes in displacement, velocity, and natural vibration frequency throughout the entire working face and surrounding area.

[0050] The collapse monitoring module uses image monitoring and laser vibration monitoring to quickly identify the stability of the tunnel face. If the displacement, velocity, and natural vibration frequency of the tunnel face do not change significantly, it indicates that the tunnel face is relatively stable and advanced geological prediction can be carried out. If the displacement and natural vibration frequency of the tunnel face and dangerous blocks change significantly, it indicates that the tunnel face is unstable. Mechanical removal and local reinforcement measures should be taken before advanced geological prediction can be carried out.

[0051] In this embodiment, the radar detection module 3 includes a vertical detection arm 5, a horizontal detection arm 6, and a ground-penetrating radar detection arm 4; wherein, the vertical detection arm 5 is used to control the up-and-down movement of the ground-penetrating radar detection arm 4, the horizontal detection arm 6 is used to control the left-and-right movement of the ground-penetrating radar detection arm 4, and the ground-penetrating radar 8 is held on the ground-penetrating radar detection arm 4 for controlling the ground-penetrating radar 8 to perform advanced geological prediction.

[0052] As an alternative implementation, the bottom end of the vertical detection arm 5 is mounted on the horizontal detection arm 6 via a sliding base, so that the vertical detection arm 5 can move laterally along the horizontal detection arm 6.

[0053] A movable bracket is provided on the base to control the bottom end of the vertical detection arm 5 to rotate 90 degrees about the movable bracket as an axis, so that the vertical detection arm 5 can be mounted on the top of the robot transport module 1 when not performing radar detection.

[0054] Furthermore, the top of the robot carrying module 1 is provided with a groove that matches the vertical detection arm 5. The vertical detection arm 5 is placed horizontally in the groove during the movement of the advanced geological prediction robot at the tunnel face. When it reaches the designated position for radar detection, the vertical detection arm 5 rotates 90 degrees around the movable support as an axis and stands up to perform prediction work.

[0055] Furthermore, the connection between the vertical detection arm 5 and the ground-penetrating radar detection arm 4 is also equipped with a movable bracket, so that when the vertical detection arm 5 is placed in the groove, the ground-penetrating radar detection arm 4 is also folded up.

[0056] As an alternative implementation, the robot carrying module 1 is connected to the middle position of the transverse detection arm 6 via a bracket at the front end in the direction of travel.

[0057] The lateral detection arm 6 includes a first track and a second track. The first track includes a parallel first lateral mechanical arm 11, and the second track includes a parallel second lateral mechanical arm 12. Both ends of the first lateral mechanical arm 11 are connected to the second lateral mechanical arm 12.

[0058] The first horizontal robotic arm 11 on one side is movably connected to the corresponding second horizontal robotic arm 12 at both ends; the first horizontal robotic arm 11 on the other side is detachably connected to the corresponding second horizontal robotic arm 12 at both ends.

[0059] During the movement, the second lateral robotic arm 12 is positioned on both sides of the robot carrier module 1 via a movable connection on one side and a detachable connection on the other side, while the first lateral robotic arm 11 is positioned at the front end of the robot carrier module 1 in the direction of movement.

[0060] like Figure 2 As shown, during the movement, the lateral detection arm 6 is positioned in a concave shape in front of and on both sides of the robot transport module 1; that is, the second lateral mechanical arm 12 is positioned on both sides of the robot transport module 1, and the first lateral mechanical arm 11 is positioned at the front end of the robot transport module 1 in the direction of movement.

[0061] When the probe reaches the designated position for detection, the second transverse robotic arms 12 on both sides extend to the same horizontal position as the first transverse robotic arm 11 in front. Then, the detachable connection parts are snapped together to form a transverse probe arm 6 that can drive the vertical probe arm 5 to move laterally, thereby performing detection of the entire face of the tunnel.

[0062] As an alternative implementation, a load-bearing support leg 10 is provided on the second horizontal robotic arm 12 to support the horizontal probe arm 6.

[0063] As an alternative implementation, the ground-penetrating radar detection arm 4 is located at the top of the vertical detection arm 5, and the end of the ground-penetrating radar detection arm 4 holds the ground-penetrating radar 8. Active obstacle-crossing devices 7 are mounted on the upper and lower sides of the ground-penetrating radar 8. The retraction amount of the ground-penetrating radar 8 is controlled by the pressure sensor 9 to achieve stable and uniform continuous measurement, and can stably pass through the working face under different conditions.

[0064] As an alternative implementation, the ground-penetrating radar detection arm 4 has a degree of freedom ≥120°, a pitch angle of ±140°, a yaw angle of ±130°, and a compensation range ≥45°.

[0065] In this embodiment, the radar detection module has three detection modes, specifically:

[0066] (1) For relatively flat working surfaces, a continuous measurement mode is adopted, which is efficient, fast and convenient.

[0067] (2) For uneven working surfaces, point measurement mode is adopted, which has deep detection, accurate identification and good effect;

[0068] (3) For the precise detection of the location of anomalies, the wheel track measurement mode is adopted to realize remote adverse geological detection for unmanned tunnel construction in complex environments.

[0069] Example 2

[0070] This embodiment provides a working method for a tunnel face advanced geological prediction robot system based on intelligent monitoring. The system employs the tunnel face advanced geological prediction robot system described in Embodiment 1 and includes the following steps:

[0071] (1) The remotely controlled tunnel face advanced geological prediction robot travels to the initial support with less vibration and a wide field of vision, and the collapse monitoring module is used to monitor the stability of the entire tunnel face area.

[0072] Specifically, the image monitoring system is used to collect information on the rock mass structure at the working face, identify block locations, and monitor falling rocks in real time to determine whether the working face area is safe; the laser vibration monitoring system is used to monitor the changes in displacement, velocity, and natural vibration frequency of the entire working face and surrounding area.

[0073] (2) After monitoring the collapse monitoring module for 15-20 minutes, if the displacement and natural vibration frequency of the working face do not change significantly, it indicates that the working face is relatively stable and advanced geological forecasting can be carried out. If the displacement and natural vibration frequency of the working face and dangerous blocks change significantly, it indicates that the working face is unstable. At this time, mechanical removal and local reinforcement measures should be taken for the working face before advanced geological forecasting work can be carried out.

[0074] (3) After the stability monitoring of the tunnel face is normal, the remote control of the tunnel face advanced geological prediction robot is driven to the bottom of the tunnel face, the second horizontal mechanical arm of the horizontal detection arm is extended to the same horizontal position as the first horizontal mechanical arm and the load-bearing support foot is extended. At the same time, the vertical detection arm is erected, so that the vertical detection arm moves laterally along the horizontal detection arm. The corresponding detection mode is selected according to the condition of the tunnel face, and the geological radar detection arm is controlled to carry out the advanced geological prediction work of the tunnel face.

[0075] (4) After scanning the entire tunnel face with ground-penetrating radar, the advanced geological prediction data is remotely sent to the data processing system outside the tunnel for intelligent data interpretation. The horizontal and vertical probe arms are then retrieved, and the advanced geological prediction robot at the tunnel face is remotely controlled to drive out of the tunnel.

[0076] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A robot system for advanced geological prediction at tunnel faces, characterized in that, include: Robot transport module, collapse monitoring module, and radar detection module; The robot's transport module is equipped with a collapse monitoring module and a radar detection module; The collapse monitoring module is used to monitor the stability of the working face by acquiring rock mass images and performing laser vibration measurements. The radar detection module includes a ground-penetrating radar detection arm that holds the ground-penetrating radar, a vertical detection arm that drives the ground-penetrating radar detection arm to move vertically, and a horizontal detection arm that drives it to move laterally. One end of the vertical detection arm is located on the horizontal detection arm, and the other end of the vertical detection arm is equipped with the ground-penetrating radar detection arm. According to the set detection mode, when there are no abnormalities in the stability monitoring of the working face, the robot transport module is remotely controlled to travel to the designated position, and the ground-penetrating radar detection arm is remotely controlled to drive the ground-penetrating radar to perform advanced geological prediction of the working face. Under the premise of ensuring the safety of the working face area, the remote unmanned operation of advanced geological prediction is realized. The front end of the robot carrying module is connected to the middle position of the lateral detection arm via a bracket. The lateral detection arm includes a first track and a second track. The first track includes a parallel first lateral mechanical arm, and the second track includes a parallel second lateral mechanical arm. Both ends of the first lateral mechanical arm are connected to the second lateral mechanical arm. The bottom end of the vertical detection arm is mounted on the horizontal detection arm via a sliding base. A movable bracket is provided on the base so that when radar detection is not performed, the bottom end of the vertical detection arm can be rotated 90 degrees around the movable bracket and mounted on the top of the robot transport module. Active obstacle-crossing devices are mounted on both sides of the ground-penetrating radar, and the retraction amount of the ground-penetrating radar is controlled by pressure sensors; the detection modes of the radar detection module include: continuous measurement mode for flat working faces, point measurement mode for uneven working faces, and wheel track measurement mode for detecting the location of anomalies.

2. The tunnel face advanced geological prediction robot system as described in claim 1, characterized in that, The collapse monitoring module includes an image monitoring system and a laser vibration monitoring system. The image monitoring system is used to collect information on the rock mass structure at the working face, identify block locations, and monitor falling rocks in real time. The laser vibration monitoring system is used to monitor changes in displacement, velocity, and natural vibration frequency in the working face area.

3. The tunnel face advanced geological prediction robot system as described in claim 1, characterized in that, The top of the robot's transport module is provided with a groove that matches the vertical detection arm. When it reaches the designated position for radar detection, the vertical detection arm rotates 90 degrees around the movable support and stands upright to perform its work.

4. The tunnel face advanced geological prediction robot system as described in claim 1, characterized in that, A movable support is provided at the connection between the vertical detection arm and the ground-penetrating radar detection arm. When the vertical detection arm is mounted on top of the robot transport module, the ground-penetrating radar detection arm is folded.

5. The tunnel face advanced geological prediction robot system as described in claim 1, characterized in that, The two ends of the first horizontal robotic arm on one side are movably connected to the corresponding second horizontal robotic arm, and the two ends of the first horizontal robotic arm on the other side are detachably connected to the corresponding second horizontal robotic arm; so that when radar detection is not performed, the second horizontal robotic arm is placed on both sides of the robot carrying module, and the first horizontal robotic arm is placed at the front end of the robot carrying module.

6. The tunnel face advanced geological prediction robot system as described in claim 5, characterized in that, When the radar reaches the designated position for detection, the second transverse robotic arms on both sides extend to the same horizontal position as the first transverse robotic arm in front. The detachable connection parts are snapped together to form a transverse detection arm that drives the vertical detection arm to move laterally, thereby performing radar detection on the face of the tunnel.

7. A working method for a tunnel face advanced geological prediction robot system, characterized in that, The tunnel face advanced geological prediction robot system according to any one of claims 1-6 includes: Driven by a robot transport module, the system moves to the area below the initial support, where a collapse monitoring module collects rock mass images and performs laser vibration measurements to monitor the stability of the tunnel face. When the stability monitoring of the working face shows no abnormalities, the robot is driven by the transport module to move below the working face. The second horizontal mechanical arm of the horizontal detection arm is controlled to extend to the same horizontal position as the first horizontal mechanical arm and extend the load-bearing support legs. At the same time, the vertical detection arm is controlled to stand upright, so that the vertical detection arm moves laterally along the horizontal detection arm. According to the set detection mode, the ground radar detection arm is controlled to drive the ground radar to perform advanced geological prediction of the working face.

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