Tunnel hidden disease detection method and device controlled by mechanical arm, medium and product

Abstract

The invention discloses a tunnel hidden disease detection method and device controlled by a mechanical arm, a medium and a product, and relates to the technical field of engineering detection.The tunnel hidden disease detection method controlled by the mechanical arm is applied to a tunnel detection platform, the tunnel detection platform is provided with the mechanical arm, and the tunnel hidden disease detection method comprises the steps that tunnel parameters of a to-be-detected tunnel are obtained; performing analysis and path planning according to the tunnel parameters to obtain a moving path of the mechanical arm; and controlling the mechanical arm to move based on the moving path, and detecting the to-be-detected tunnel through a detection sensor of the mechanical arm to obtain a tunnel detection result. According to the tunnel hidden disease detection method and device, tunnel hidden disease detection controlled by the automatic mechanical arm is achieved, the problem that an existing tunnel hidden disease detection method is low in detection efficiency is solved, and the detection efficiency and safety of tunnel detection are improved.

Description

Technical Field

[0001] The present application relates to the field of engineering detection technology, and in particular to a method, equipment, medium and product for detecting hidden defects in tunnels controlled by a robotic arm. Background Art

[0002] With the development of the rail transit transportation industry, tunnel projects are increasing. Due to the influence of terrain, geology, climate conditions, and various factors in the design and construction process, tunnels will have varying degrees of defects during the construction process and even in the later use process. Tunnels need to be inspected at different stages to prevent the impact of defects.

[0003] However, the existing methods for detecting hidden defects in tunnels usually require manually controlling a robotic arm to lift the detection radar to the tunnel surface to complete tunnel detection. When the tunnel contains complex lines or structures such as contact lines and electric wires, it is necessary to repeatedly adjust the posture of the robotic arm for detection, which affects the efficiency of tunnel detection.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present application is to provide a method, device, medium and product for detecting hidden defects in tunnels controlled by a robotic arm, aiming to solve the technical problem of low detection efficiency of existing methods for detecting hidden defects in tunnels.

[0006] To achieve the above-mentioned purpose, the present application proposes a method for detecting hidden defects in tunnels controlled by a robotic arm, the method being applied to a tunnel detection platform, the tunnel detection platform being equipped with a robotic arm, comprising: Obtain tunnel parameters of the tunnel to be detected; Analyzing and planning the path according to the tunnel parameters to obtain the moving path of the robot arm; The movement of the mechanical arm is controlled based on the moving path, and the tunnel to be detected is detected by a detection sensor of the mechanical arm to obtain a tunnel detection result.

[0007] In one embodiment, the step of analyzing and planning the path according to the tunnel parameters to obtain the moving path of the robot arm includes: Determining the target detection position and posture of the robotic arm according to the tunnel parameters; Based on the target detection position and posture of the robotic arm, path planning is performed according to the tunnel parameters to obtain the moving path of the robotic arm.

[0008] In one embodiment, the mechanical arm comprises a telescopic rotating structure and an arc-shaped arm, wherein the arc-shaped arm is rotatably connected to the telescopic rotating structure via a slewing support structure, and the detection sensor is disposed on the arc-shaped arm. The step of determining the target detection position and posture of the robotic arm according to the tunnel parameters comprises: Determining a detection position of the slewing support structure according to the tunnel parameters; Calculating based on the detected position of the slewing support structure to obtain telescopic rotation parameters of the telescopic rotation structure; The target detection position and posture of the robotic arm are determined according to the telescopic rotation parameters of the telescopic rotation structure.

[0009] In one embodiment, the step of calculating based on the detected position of the slewing support structure to obtain the telescopic rotation parameters of the telescopic rotation structure includes: The detection position of the slewing support structure is set as the target position of the end effector, and the component parameters of the telescopic rotating structure are set as the restriction conditions, and a kinematic calculation model is constructed based on the target position of the end effector and the restriction conditions; The kinematic calculation model is solved to obtain the telescopic rotation parameters of the telescopic rotation structure.

[0010] In one embodiment, the tunnel parameters include one or more of tunnel radius, track position and obstacle position, the detection sensor includes one or more of ranging sensors, radar sensors and cameras, the telescopic rotation structure includes at least one telescopic connecting rod structure and at least one rotating joint structure, the telescopic connecting rod structures are connected by the rotating joint structure, and the telescopic rotation parameters include one or more of telescopic length and rotation angle.

[0011] In one embodiment, before the step of controlling the movement of the mechanical arm based on the movement path, the method further includes: According to the moving path, collision detection is performed by using a pre-built collision detection model to obtain a collision detection result, wherein the collision detection model is built based on a target detection position and posture of the robot arm and the tunnel parameters; If the collision detection result indicates that there is a collision risk, the moving path is replanned according to the collision detection result and the tunnel parameters to obtain a replanned moving path, so as to control the movement of the robotic arm based on the replanned moving path.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a tunnel hidden disease detection device controlled by a robot arm, wherein the tunnel hidden disease detection device controlled by the robot arm is configured with a robot arm, including: An acquisition module, used for acquiring tunnel parameters of the tunnel to be detected; A planning module, used for analyzing and planning the path according to the tunnel parameters to obtain the moving path of the robot arm; The detection module is used to control the movement of the mechanical arm based on the movement path, and detect the tunnel to be detected through the detection sensor of the mechanical arm to obtain the tunnel detection result.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a tunnel hidden disease detection device controlled by a robotic arm, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the tunnel hidden disease detection method controlled by a robotic arm as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the tunnel hidden disease detection method controlled by the robot arm as described above are implemented.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the tunnel hidden disease detection method controlled by a robotic arm as described above.

[0016] The present application provides a method for detecting hidden defects in tunnels controlled by a manipulator. The present application first obtains tunnel parameters and environmental identification to provide accurate basic data for path planning. Then, tunnel parameters are used for analysis and path planning to obtain the movement path of the manipulator in the tunnel to ensure that the manipulator moves safely and efficiently in the tunnel. Finally, by controlling the movement of the manipulator and collecting data from the detection sensor, a comprehensive detection of the inner wall of the tunnel is achieved to obtain the tunnel detection results, so as to obtain the health status of the tunnel and provide a basis for maintenance and repair. The automatic control of tunnel detection is realized, the problem of low detection efficiency of the existing method for detecting hidden defects in tunnels is solved, and the detection efficiency and safety of tunnel detection are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 A flow chart of the first embodiment of the tunnel hidden disease detection method controlled by a robot arm of the present application; Figure 2 A flow chart of the second embodiment of the tunnel hidden disease detection method controlled by a robot arm of the present application; Figure 3 A structural schematic diagram of a mechanical arm recovery state provided in the second embodiment of the method for detecting hidden defects in tunnels controlled by a mechanical arm of the present application; Figure 4 A schematic diagram of the structure of the robot arm in the extended state provided in the second embodiment of the method for detecting hidden defects in tunnels controlled by the robot arm of the present application; Figure 5 A schematic diagram of the structure of a tunnel detection by a robotic arm provided in the second embodiment of the tunnel hidden disease detection method controlled by a robotic arm of the present application; Figure 6 This is a schematic diagram of the module structure of the tunnel hidden disease detection device controlled by a mechanical arm according to an embodiment of the present application; Figure 7 Schematic diagram of the equipment structure of the hardware operating environment involved in the method for detecting hidden defects in tunnels controlled by a robot arm in an embodiment of the present application.

[0020] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of the embodiment of the present application is: obtaining the tunnel parameters of the tunnel to be detected; performing analysis and path planning based on the tunnel parameters to obtain the moving path of the robotic arm; controlling the movement of the robotic arm based on the moving path, and detecting the tunnel to be detected through the detection sensor of the robotic arm to obtain the tunnel detection result.

[0024] With the continuous development of the rail transportation industry, the number of tunnel projects continues to increase. However, due to various factors such as terrain, geology, climate conditions, and design and construction processes, tunnels may suffer from different degrees of damage during construction and use.

[0025] Although the existing technology for detecting hidden defects in tunnels is constantly developing, it still has some limitations. First, the traditional method for detecting hidden defects in tunnels mainly relies on manually operated robotic arms. When the tunnel contains complex lines or structures such as contact networks and electric wires, it is necessary to repeatedly adjust the posture of the robotic arm for detection, which affects the efficiency of tunnel detection. In addition, the lighting inside the tunnel is insufficient, and relying solely on car lights will have a great impact on the line of sight of the inspectors, causing the error of the detection data to be too large; the surface of the inner wall of the tunnel is complex, which will affect the detection signal, easily causing damage to the equipment or putting the inspectors in danger; in the case of long tunnels or tunnel groups, long-term traffic control is required, affecting the normal driving of the road. Traditional methods are limited by manpower, time and space, and cannot meet the needs of large-scale and real-time monitoring.

[0026] This application first obtains tunnel parameters and environmental identification to provide accurate basic data for path planning; then uses tunnel parameters for analysis and path planning to obtain the movement path of the robot arm in the tunnel to ensure that the robot arm moves safely and efficiently in the tunnel; finally, by controlling the movement of the robot arm and detecting sensor data collection, a comprehensive inspection of the inner wall of the tunnel is achieved, and the tunnel inspection results are obtained to obtain the health status of the tunnel and provide a basis for maintenance and repair. The automated control of tunnel inspection is realized, which solves the problem of low detection efficiency of existing tunnel hidden disease detection methods and improves the detection efficiency and safety of tunnel hidden disease detection.

[0027] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a tunnel hidden disease detection device controlled by a mechanical arm, etc. The following takes the tunnel hidden disease detection device controlled by a mechanical arm as an example to illustrate this embodiment and the following embodiments.

[0028] Based on this, the embodiment of the present application provides a method for detecting hidden defects in tunnels controlled by a robotic arm, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for detecting hidden defects in tunnels controlled by a robotic arm of the present application.

[0029] In this embodiment, the tunnel hidden disease detection method controlled by the robot arm is applied to a tunnel detection platform, and the tunnel detection platform is equipped with a robot arm, including steps S10 to S30: Step S10, obtaining tunnel parameters of the tunnel to be detected; It should be noted that the tunnel to be inspected is the object of the inspection work, and its parameters are obtained for subsequent targeted inspections. Different tunnels have different structures, sizes and environmental characteristics. Accurately obtaining this information is the basis for achieving precise inspections.

[0030] In addition, it should be noted that tunnel parameters can fully reflect the physical characteristics of the tunnel and provide a detailed basis for path planning and detection plan formulation. Tunnel parameters can include the length, width, height, curvature, slope, lining type, internal facility layout, etc. For example, the curvature and slope of the tunnel will affect the movement of the robot arm and the installation angle of the sensor; the lining type is related to the key content and method selection of the detection. Among them, some tunnel parameters can be obtained by installing sensors, and some tunnel parameters need to be manually entered into the system in advance.

[0031] It is understandable that in order to provide basic data support for subsequent path planning and detection work, ensure that the movement and detection operations of the robot arm can be carried out accurately and efficiently, avoid waste of resources or detection errors caused by blind operation, and collect a series of basic information related to the tunnel to be inspected through various measurement means and techniques, this information can reflect the physical characteristics and environmental conditions of the tunnel, enable the detection platform to "recognize" the tunnel, understand its basic outline, structural characteristics, etc., and thus provide a data basis for formulating a reasonable detection plan. It is the starting point and key prerequisite of the entire detection process.

[0032] Step S20, analyzing and planning the path according to the tunnel parameters to obtain the moving path of the robot arm; It should be noted that the movement path of the robot arm is the final result of path planning. It is the route of the robot arm's movement detection in the tunnel. It is necessary to comprehensively consider factors such as the robot arm's range of motion, the internal structure of the tunnel, detection efficiency and safety, and clarify the specific movement sequence and trajectory of the robot arm from the starting point to each detection point and then to the end point. The planned movement path should ensure that the robot arm can smoothly reach each detection point while minimizing the movement time and energy consumption. For example, the shortest path algorithm or obstacle avoidance algorithm is used, combined with the three-dimensional model of the tunnel, to generate a safe and efficient movement path.

[0033] It is understandable that in order to optimize the movement process of the robotic arm and ensure that it can complete the comprehensive inspection of the tunnel in the shortest time and with the minimum energy consumption, while avoiding accidents such as collisions between the robotic arm and the tunnel structure during movement, computer algorithms and data analysis techniques are used, combined with tunnel parameters, to conduct a comprehensive analysis of the spatial layout inside the tunnel, the distribution of key inspection points, etc., and then plan a reasonable and efficient robotic arm movement path, so that the robotic arm can reach each inspection position in an orderly and efficient manner, thereby converting the acquired tunnel parameters into a specific action guide for the robotic arm, and providing clear path guidance for the movement of the robotic arm. It is the key link connecting the acquisition of tunnel parameters and the actual inspection operation, and determines the efficiency and quality of the inspection work.

[0034] Step S30: Control the movement of the robot arm based on the moving path, and detect the tunnel to be detected by the detection sensor of the robot arm to obtain a tunnel detection result.

[0035] It should be noted that the control of the robot arm refers to the precise driving of the robot arm to move along a predetermined trajectory and speed according to the instructions of the control system based on the planned movement path. For example, the use of advanced servo motor control technology combined with real-time feedback positioning information can achieve precise control of the robot arm.

[0036] In addition, it should be noted that the detection sensor is installed on the robot arm and is a key device for measuring various indicators of the tunnel and collecting data. The tunnel detection result is the final result obtained after analyzing and processing the data collected by the detection sensor. It can include information on the tunnel's structural condition, disease conditions, environmental parameters, and other aspects, which can provide a scientific basis for tunnel maintenance decisions, safety assessments, and repair plan formulation. For example, if the test results show that there are large cracks in the lining of a certain part of the tunnel and there is water leakage, targeted repair measures can be formulated based on this.

[0037] It is understandable that in order to achieve comprehensive and accurate detection of tunnels, timely discover various problems and hidden dangers in tunnels, and provide accurate data basis for tunnel maintenance, repair and safe operation, the planned moving path is used to accurately control the movement of the robotic arm so that it carries the detection sensor to the predetermined detection position, and then uses the detection sensor to perform real-time measurement and data collection on various indicators of the tunnel. Finally, the collected data is analyzed and processed to form a complete tunnel detection result. The collaborative work of the robotic arm and the detection sensor completes the tunnel detection task. It is the final execution link of the entire detection process, ensuring the validity and reliability of the detection results.

[0038] In a feasible implementation manner, before the step of controlling the movement of the mechanical arm based on the movement path, the step further includes: Step S301, performing collision detection according to the moving path through a pre-built collision detection model to obtain a collision detection result, wherein the collision detection model is built based on the target detection position and posture of the robot arm and the tunnel parameters; It should be noted that the collision detection model is a virtual simulation model built based on the target detection position and posture of the robot arm and the tunnel parameters. It can simulate the movement of the robot arm in the tunnel and calculate the spatial distance and relative position relationship between the robot arm and the tunnel structure in real time, so as to determine whether there is a collision risk. The accuracy of the collision detection model is directly related to the reliability of the collision detection results and is a key technical means to achieve safety detection.

[0039] In addition, it should be noted that the collision detection result refers to the conclusion drawn by the collision detection model after analyzing the moving path, indicating whether there is a risk of collision when the robot arm moves along the current path. It is usually presented as a Boolean value (there is a collision risk or there is no collision risk) or specific collision location, time and other detailed information, providing clear guidance for path replanning.

[0040] It is understandable that before the actual movement of the robot arm, it is necessary to predict and identify potential collision hazards in advance to ensure that the movement process of the robot arm is safe and reliable, and to avoid equipment damage, tunnel structure damage, and detection task failure caused by collision. The pre-built collision detection model, which comprehensively considers the target detection position and posture of the robot arm and the tunnel parameters, simulates and detects possible collisions during the movement of the robot arm along the planned path. By calculating the spatial relationship between the robot arm and the tunnel structure at different positions and postures, judging whether there is a collision risk, and providing a basis for the optimization and adjustment of path planning, it is a key link to ensure the safe operation of the robot arm and improve the feasibility and success rate of the detection work.

[0041] For example, the movement path of the robot arm can be preliminarily screened for collisions using collision detection based on a geometric model to quickly exclude path segments with obvious collision risks. For some complex or uncertain areas, in-depth analysis can be performed in combination with collision detection based on physical simulation to ensure the accuracy of collision detection. At the same time, the results of each collision detection are used as data samples to train a collision detection model based on machine learning. After continuous learning and optimization, the machine learning model can gradually improve the prediction accuracy and speed of collision risks, so that it can quickly and accurately detect collision risks in the movement path of the robot arm under different tunnel environments and detection tasks, providing a reliable basis for path replanning and effectively ensuring the safe operation of the robot arm.

[0042] Step S302: If the collision detection result indicates that there is a collision risk, the moving path is replanned according to the collision detection result and the tunnel parameters to obtain a replanned moving path, so as to control the movement of the robotic arm based on the replanned moving path.

[0043] It should be noted that path replanning is the process of redesigning and optimizing the original moving path after the collision risk is detected. Path replanning needs to comprehensively consider factors such as collision detection results, tunnel parameters, and the motion characteristics of the robot arm to find a new, safe and feasible moving path. The purpose of path replanning is to minimize the impact of path adjustment on detection efficiency while ensuring safety, so that the robot arm can complete the detection task quickly and smoothly. The replanned moving path is a new path obtained through path replanning. It has avoided the collision area in the original path and can ensure the safe movement of the robot arm. The replanned moving path also needs to be re-collision-checked to verify its safety before it can be used to control the actual movement of the robot arm.

[0044] It is understandable that in order to eliminate potential collision risks, ensure that the robot arm can smoothly and safely reach each detection position, successfully complete the detection task, and minimize the reduction in detection efficiency caused by path adjustment. When the collision detection model detects the existence of a collision risk, the movement path is replanned in combination with the collision detection results and tunnel parameters. By adjusting the movement trajectory, speed or posture of the robot arm to avoid the collision area, a new safe and feasible movement path is generated to guide the subsequent movement of the robot arm, which enhances the robustness and adaptability of the entire detection process, enabling the system to flexibly adjust strategies in the face of complex and changeable tunnel environments to ensure the smooth progress of detection work.

[0045] Exemplarily, a local path adjustment method is first used to quickly handle minor collision risks found by collision detection. If local adjustments cannot solve the problem or the collision risk is more complex, the global path replanning program is started, and a safe and efficient moving path is redesigned using the path planning algorithm and obstacle avoidance strategy. At the same time, during the path replanning process, the adjusted path can be fed back to the collision detection model in real time for re-detection to ensure the safety of the new path. It can flexibly and effectively adjust the moving path of the robot arm under different collision risk conditions to maximize the safe operation of the robot arm, while minimizing the impact of path adjustment on detection efficiency to ensure the smooth completion of the detection task.

[0046] In this embodiment, the collision detection model is used to conduct a safety assessment of the planned path before the robot moves, and potential collision risks are discovered in advance. If a collision risk is detected, the path is replanned according to the risk situation and tunnel parameters to generate a new safe path, so that the robot can fully estimate and avoid collision risks before the actual movement detection, ensuring that the robot can find a safe and feasible movement path in various complex situations and perform detection according to the optimized path, which not only ensures the safety of itself and the tunnel, but also improves the detection efficiency and quality.

[0047] This embodiment provides a method for detecting hidden defects in tunnels controlled by a manipulator. First, accurate basic data is provided for path planning by obtaining tunnel parameters and environmental identification. Then, the tunnel parameters are used for analysis and path planning to obtain the movement path of the manipulator in the tunnel, so as to ensure that the manipulator moves safely and efficiently in the tunnel. Finally, by controlling the movement of the manipulator and collecting data from the detection sensors, a comprehensive detection of the inner wall of the tunnel is achieved, and the tunnel detection results are obtained to obtain the health status of the tunnel and provide a basis for maintenance and repair. The tunnel detection is realized by automated control, which solves the problem of low detection efficiency of the existing method for detecting hidden defects in tunnels and improves the detection efficiency and safety of tunnel detection.

[0048] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can refer to the above introduction, and will not be repeated in the following. Figure 2 , Figure 2 This is a flow chart of the second embodiment of the method for detecting hidden defects in tunnels controlled by a robotic arm of the present application.

[0049] In this embodiment, the step of analyzing and planning the path according to the tunnel parameters to obtain the moving path of the robot arm includes: Step S201, determining the target detection position and posture of the robotic arm according to the tunnel parameters; It should be noted that the target detection position refers to the specific area or point that the robot arm needs to detect in the tunnel. The posture refers to the spatial orientation and angle of the robot arm when it is at the target detection position, including pitch angle, yaw angle and roll angle. The correct posture can ensure that the relative position and direction of the detection sensor and the detected part meet the detection requirements, thereby obtaining accurate detection data.

[0050] It is understandable that in order to provide accurate positioning and direction guidance for path planning, ensure that the robot arm can accurately reach the key detection parts, and perform detection in the most suitable posture, thereby improving the accuracy and effectiveness of detection. In-depth tunnel parameters, including structural characteristics, dimensional data, potential disease areas and other information of the tunnel, use engineering knowledge and detection experience to determine the optimal position and posture of the robot arm when performing detection in the tunnel.

[0051] For example, when inspecting whether there are hidden defects in a certain section of a tunnel, it is necessary to analyze the inspection position and posture that the robot arm needs to reach to ensure that the detection sensor can correctly face the inspection point that needs to be inspected, and maintain a distance with the monitoring point for the best data collection effect, so as to accurately collect data.

[0052] Step S202 , based on the target detection position and posture of the robotic arm, path planning is performed according to the tunnel parameters to obtain a moving path of the robotic arm.

[0053] It should be noted that the moving path is the specific walking route of the robot arm from the starting point to each target detection position and then returning to the end point. It can be presented in the form of a series of coordinate points or instruction sequences to guide the actual movement of the robot arm in the tunnel, enabling the robot arm to avoid obstacles, reduce movement time and energy consumption, and successfully complete the detection task.

[0054] It is understandable that in order to combine the determined target detection position and posture with the actual environment of the tunnel, a feasible moving path is generated, so that the robot arm can move smoothly during the detection process, avoid collisions and unnecessary detours, and improve the detection efficiency. After determining the target detection position and posture of the robot arm, combined with the detailed parameters of the tunnel, such as the internal space layout, obstacle location, ventilation duct and cable tray distribution, etc., comprehensively considering factors such as moving distance and obstacle avoidance requirements, the optimal moving path from the starting point to each target detection position is planned for the robot arm to ensure that the robot arm can complete the detection task safely and efficiently.

[0055] In a feasible implementation manner, the mechanical arm comprises a telescopic rotating structure and an arc-shaped arm, the arc-shaped arm is rotatably connected to the telescopic rotating structure via a slewing support structure, and the detection sensor is disposed on the arc-shaped arm. The step of determining the target detection position and posture of the robotic arm according to the tunnel parameters comprises: Step S2011, determining the detection position of the slewing support structure according to the tunnel parameters; It should be noted that the slewing support structure is a structure used to support the arc-shaped arm and enable it to rotate relative to the telescopic rotating structure. The determination of its detection position directly affects the working range and detection effect of the detection sensor on the arc-shaped arm in the tunnel. The detection position of the slewing support structure refers to the specific position of the slewing support structure when performing detection operations in the tunnel. It enables the slewing support structure to stably support the mechanical arm, determines the rotation center of the arc-shaped arm, and thus affects the tunnel area that the sensor on the arc-shaped arm can cover, and provides a good foundation for the rotation and detection operations of the arc-shaped arm. At the same time, since the arc-shaped arm presents a certain curvature, the arc-shaped arm can be evenly fitted to the inner wall of the tunnel, improving the uniformity and comprehensiveness of the detection.

[0056] It is understandable that in order to provide basic position information for the subsequent telescopic rotation structure parameter calculation and robot arm posture determination, ensure that the slewing support structure can stably and effectively support the robot arm's detection operation in the tunnel, and enable the arc arm to rotate within a suitable range to cover the key detection area. In-depth analysis of tunnel parameters, such as the tunnel's cross-sectional shape, size, internal structure layout, etc., combined with the structural characteristics of the robot arm, to determine the optimal position of the slewing support structure during detection in the tunnel can make the robot arm's detection operation more flexible and efficient, and improve the accessibility and accuracy of detection.

[0057] Step S2012, performing calculation based on the detected position of the slewing support structure to obtain telescopic rotation parameters of the telescopic rotation structure; It should be noted that the telescopic rotating structure is the driving part of the robot arm, which is responsible for realizing the telescopic and rotating movements, thereby driving the arc arm and the detection sensor to move to the target detection position. The telescopic length and rotation angle of the telescopic rotating structure are its key parameters, which directly affect the detection range and posture adjustment of the robot arm. For example, the telescopic length of the telescopic rotating structure determines the tunnel depth that the arc arm can reach, and the rotation angle determines the orientation and detection direction of the arc arm.

[0058] In addition, it should be noted that the telescopic rotation parameters can include two aspects: telescopic length and rotation angle. The telescopic length refers to the specific length value reached by the telescopic rotation structure during the telescopic movement, which determines the extension range of the robot arm in the tunnel; the rotation angle refers to the specific angle value reached by the telescopic rotation structure during the rotational movement, which determines the direction and posture of the robot arm. Accurate telescopic rotation parameters can enable the robot arm to accurately reach the target detection position and perform detection at a suitable angle.

[0059] It is understandable that in order to ensure that the telescopic rotating structure can accurately move the detection sensor on the arc arm to the target detection position and perform detection at a suitable angle, while avoiding collision or interference with the tunnel structure. After determining the detection position of the slewing support structure, the telescopic length and rotation angle of the telescopic rotating structure are calculated based on the tunnel parameters and the structural relationship of the robotic arm using geometric calculations and kinematic principles. For example, if it is necessary to detect a crack somewhere on the side wall of the tunnel, the corresponding parameters of the telescopic rotating structure are calculated based on the distance and angle relationship between the crack position and the slewing support structure, so that the arc arm can be accurately aligned with the crack. By accurately calculating the telescopic rotation parameters, the various parts of the robotic arm can work together to achieve accurate detection of complex structures and different positions inside the tunnel.

[0060] Step S2013, determining the target detection position and posture of the robotic arm according to the telescopic rotation parameters of the telescopic rotation structure.

[0061] It should be noted that the target detection position refers to the specific position of the robot arm when the robot arm performs detection operations in the tunnel. The determination of the target detection position is calculated based on the telescopic rotation parameters of the telescopic rotation structure and the motion characteristics of the arc arm, ensuring that the detection sensor can accurately align with the detected part and improve the accuracy and reliability of the detection. Posture refers to the spatial orientation and angle of the robot arm when it is at the target detection position, including pitch angle, yaw angle and roll angle, etc. The determination of the posture is calculated based on the telescopic rotation parameters of the telescopic rotation structure and the shape of the arc arm, ensuring that the relative position and direction of the detection sensor and the detected part meet the detection requirements, thereby obtaining accurate detection data. For example, when detecting the tunnel vault, the posture of the robot arm needs to be adjusted so that the detection sensor is perpendicular to the vault surface.

[0062] It is understandable that in order to achieve accurate positioning and posture adjustment of the robot arm from the initial position to the target detection position, it is necessary to ensure that the detection sensor can be aligned with the detected part in the best state and improve the accuracy and reliability of the detection. The telescopic length, rotation angle, shape and motion characteristics of the telescopic rotating structure and the arc arm are comprehensively considered to determine the final target detection position and posture of the robot arm in the tunnel. For example, according to the parameters of the telescopic rotating structure, the specific coordinate position and orientation angle of the detection sensor at the end of the arc arm in the tunnel are calculated to determine the detection posture of the robot arm so that it can be perpendicular or parallel to the surface of the detected part of the tunnel to obtain accurate detection data, which provides clear goals and instructions for subsequent path planning and robot arm control.

[0063] For example, the detection position of the slewing support structure can be determined based on the cross-sectional shape and size of the tunnel by using geometric knowledge for analysis and calculation. For example, for a rectangular cross-sectional tunnel, the detection position of the slewing support structure can be set on the tunnel centerline, at a certain height from the ground, so that the arc arm can cover the entire tunnel cross-section; for a circular cross-sectional tunnel, the detection position can be set on the tunnel center axis so that the arc arm evenly covers the inner wall of the tunnel. It can be accurately calculated based on the specific geometric shape of the tunnel, which is suitable for determining the detection position of a regular cross-sectional tunnel to ensure the comprehensiveness and uniformity of the detection.

[0064] In this embodiment, first, the detection position of the slewing support structure is accurately determined according to the tunnel parameters, which provides basic position information for the subsequent calculation of telescopic rotation parameters and determination of the robot arm posture, ensuring that the slewing support structure can stably and effectively support the detection operation of the robot arm in the tunnel. Next, based on the detection position of the slewing support structure, the telescopic rotation parameters of the telescopic rotation structure are calculated, so that the telescopic rotation structure can accurately move the arc arm and the detection sensor to the target detection position and perform detection at a suitable angle while avoiding collision or interference with the tunnel structure. Finally, based on the telescopic rotation parameters of the telescopic rotation structure, the final target detection position and posture of the robot arm are determined to ensure that the detection sensor can be aligned with the detected part in the best state, thereby improving the accuracy and reliability of the detection.

[0065] In this embodiment, the step of calculating based on the detected position of the slewing support structure to obtain the telescopic rotation parameters of the telescopic rotation structure includes: Step S20121, setting the detected position of the slewing support structure as the target position of the end effector, and setting the component parameters of the telescopic rotating structure as constraint conditions, and constructing a kinematic calculation model based on the target position of the end effector and the constraint conditions; It should be noted that the kinematic calculation model is a mathematical model used to describe the motion relationship and parameter changes of the robot arm. It is constructed based on the target position of the end effector of the robot arm and the component parameters of the robot arm. By establishing equations and constraints, the motion problem of the robot arm is converted into a solvable mathematical problem.

[0066] In addition, it should be noted that in this embodiment, the end effector refers to the frontmost working component of the robot arm, which is used to directly contact or approach the object to be detected for data collection. The target position of the end effector, that is, the detection position of the slewing support structure, determines the specific spatial position that the robot arm needs to reach, and is the core target of the kinematic calculation. Component parameters refer to the various physical parameters and motion restrictions of the telescopic rotation structure, such as the telescopic range (maximum extension length and minimum contraction length), rotation angle limit (maximum rotation angle and rotation direction limit), etc. These parameters serve as constraints for kinematic calculations to ensure that the calculated telescopic rotation parameters are consistent with the actual structure and motion capabilities of the robot arm, and to avoid exceeding the range of motion of the robot arm or causing mechanical damage.

[0067] It is understandable that an accurate mathematical calculation framework is provided to accurately calculate the telescopic rotation parameters of the telescopic rotation structure, ensuring that the calculation process conforms to the actual structure and motion limitations of the robot arm, so as to obtain feasible and accurate parameter values. The detection position of the slewing support structure is used as the target position that the end effector (i.e., the detection sensor at the end of the arc arm) needs to reach, and the component parameters of the telescopic rotation structure (such as telescopic range, rotation angle limit, etc.) are used as constraints for kinematic calculations. Using this information, a kinematic calculation model is constructed, which can describe the motion relationship and parameter changes of the telescopic rotation structure in the process of reaching the target position, converting complex robot arm motion problems into solvable mathematical problems, and ensuring the accuracy and practicality of the calculation results.

[0068] Step S20122, solving the kinematic calculation model to obtain the telescopic rotation parameters of the telescopic rotation structure.

[0069] It should be noted that the telescopic rotation parameters are the results obtained after solving the kinematic calculation model, including the telescopic length and rotation angle of the telescopic rotation structure. The telescopic length determines the movement distance of the robot arm in the telescopic direction, and the rotation angle determines the movement direction and amplitude of the robot arm in the rotation direction. It enables the robot arm to accurately move the end effector to the target detection position and detect at a suitable angle, which is the key data for realizing the motion control of the robot arm.

[0070] It is understandable that in order to obtain the specific motion parameters of the telescopic rotation structure, these parameters will directly guide the actual motion control of the robot arm, so that the robot arm can accurately move the end effector to the target detection position and complete the detection task. Use mathematical methods and calculation tools to solve the constructed kinematic calculation model. According to the equations and constraints in the model, the parameters such as the telescopic length and rotation angle required for the telescopic rotation structure to reach the target position of the end effector are calculated. By accurately solving the kinematic calculation model, the telescopic rotation parameters of the telescopic rotation structure can be obtained, providing clear instructions for the motion control of the robot arm, ensuring that the robot arm can move according to the predetermined target position and posture, and improving the accuracy and reliability of detection.

[0071] In this embodiment, the detection position of the slewing support structure is set as the target position of the end effector, and the component parameters of the telescopic rotation structure are set as the restriction conditions. Based on this information, a kinematic calculation model is constructed to provide an accurate mathematical calculation framework for the calculation of the telescopic rotation parameters, ensuring that the calculation process conforms to the actual structure and motion restrictions of the robot arm. Then, according to the complexity of the kinematic calculation model and the solution accuracy requirements, the model is solved to obtain the telescopic rotation parameters of the telescopic rotation structure, so as to directly guide the actual motion control of the robot arm, so that the robot arm can accurately move the end effector to the target detection position and complete the detection task.

[0072] In a feasible embodiment, the tunnel parameters include one or more of tunnel radius, track position and obstacle position, the detection sensor includes one or more of ranging sensors, radar sensors and cameras, the telescopic rotation structure includes at least one telescopic connecting rod structure and at least one rotating joint structure, the telescopic connecting rod structures are connected by the rotating joint structure, and the telescopic rotation parameters include one or more of telescopic length and rotation angle.

[0073] It should be noted that the tunnel radius refers to the inner diameter of the tunnel, which is a key parameter for determining the working range and detection path of the robot arm. For circular cross-section tunnels, the radius directly affects the detection position of the slewing support structure and the telescopic length of the telescopic link structure. For example, a larger tunnel radius may require a longer telescopic link to cover the entire tunnel section for detection. The track position refers to the specific position of the track in the tunnel used to support and guide the movement of the robot arm, including the horizontal position and height of the track. The obstacle position refers to the specific position and size of various obstacles inside the tunnel, such as ventilation ducts, cable trays, support structures, etc. Understanding the obstacle position is crucial for path planning, which can avoid collisions between the robot arm and obstacles during movement and ensure the safety and smooth progress of the detection process.

[0074] The distance sensor is used to measure the distance between the robot arm and the inner wall of the tunnel or other objects. The radar sensor uses radio waves to detect the internal structure and objects of the tunnel. It can penetrate materials of a certain thickness, such as concrete, and can detect hidden diseases inside the tunnel, such as cavities and water bags behind the lining. The camera is used to obtain image information of the inner wall of the tunnel. It can be an ordinary visible light camera or an infrared camera. Among them, the visible light camera can take intuitive images of cracks, water leakage, peeling and other diseases on the tunnel surface; the infrared camera can detect the temperature distribution on the tunnel surface and find potential water leakage areas or structural damage areas.

[0075] The telescopic link structure is one of the main moving parts of the robot arm, responsible for realizing the telescopic movement. The telescopic link structure is usually composed of multiple sections of telescopic links, and the telescopic movement is realized by driving devices such as hydraulic cylinders, air cylinders or electric push rods. The telescopic length is a key parameter of the telescopic link structure, which determines the distance of the detection position that the robot arm can reach. A longer telescopic link can enable the robot arm to cover a larger detection range, but it also increases the complexity of the structure and the difficulty of control.

[0076] The rotary joint structure is a component that connects the telescopic link structure and is responsible for realizing the rotary motion. The rotary joint structure can realize the rotary motion by means of slewing bearing or gear transmission. The rotation angle is a key parameter of the rotary joint structure, which determines the direction and posture of the robot arm. A reasonable rotation angle can make the detection sensor better align with the detected part, thereby improving the accuracy and reliability of the detection.

[0077] For example, Figure 3 and Figure 4 As shown, Figure 3 A schematic diagram of the structure of the robot arm recovery state provided in the second embodiment of the method for detecting hidden defects in tunnels controlled by a robot arm of the present application, Figure 4 A schematic diagram of the structure of the mechanical arm in the extended state provided for the second embodiment of the tunnel hidden disease detection method controlled by the mechanical arm of the present application. The mechanical arm in the figure is mainly composed of structural components such as a linear slide rail, a fixed seat, a rotating base, an electric push rod, a slewing support and an arc arm. The linear slide rail and the electric push rod belong to the telescopic connecting rod structure of the mechanical arm, and the rotating base and the slewing support belong to the rotating joint structure of the mechanical arm.

[0078] The linear guide rail enables the robot arm to move parallel to the cross section of the tunnel so as to approach the inner wall of the tunnel for detection. The fixed seat can fix the connection between the robot arm and the tunnel detection platform. The rotating base can support the horizontal rotation of the robot arm. The electric push rod can raise the angle of other structures of the robot arm. The slewing support can make the arc arm rotate 360 ​​degrees relative to the slewing support as the axis. The arc arm is also equipped with a telescopic connecting rod structure to change the detection position of the detection sensor through the telescopic connecting rod.

[0079] like Figure 5 As shown, Figure 5 A schematic diagram of the structure of the mechanical arm tunnel detection provided for the second embodiment of the method for detecting hidden defects in tunnels controlled by the mechanical arm of the present application. The mechanical arm can be installed on the tunnel detection vehicle through a fixed base, and can perform detection operations inside tunnels such as high-speed railways and subways. By accurately controlling the telescopic length of the telescopic connecting rod structure and the rotation angle of the rotating joint structure, and by adjusting the working range and posture of the mechanical arm, the mechanical arm can accurately align the detection sensor with the target position, adapt to tunnels of different diameters, shapes and structures, and meet the needs of various tunnel detections.

[0080] In this embodiment, the target detection position and posture of the robot arm are accurately determined according to the tunnel parameters, and refined path planning is performed accordingly, achieving seamless connection and efficient coordination from tunnel parameter acquisition to robot arm precise detection. This not only improves the accuracy and efficiency of detection, but also enhances the intelligence level and adaptability of the entire detection process, providing a more reliable and efficient technical means for tunnel safety detection and maintenance.

[0081] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the tunnel hidden disease detection method controlled by the robot arm of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0082] This application also provides a tunnel hidden disease detection device controlled by a robotic arm, please refer to Figure 6 The tunnel hidden disease detection device controlled by the mechanical arm is equipped with a mechanical arm, including: An acquisition module 10 is used to acquire tunnel parameters of the tunnel to be detected; A planning module 20, used for analyzing and planning the path according to the tunnel parameters to obtain the moving path of the robot arm; The detection module 30 is used to control the movement of the robot arm based on the movement path, and detect the tunnel to be detected through the detection sensor of the robot arm to obtain a tunnel detection result.

[0083] Optionally, the planning module 20 is further configured to: Determining the target detection position and posture of the robotic arm according to the tunnel parameters; Based on the target detection position and posture of the robotic arm, path planning is performed according to the tunnel parameters to obtain the moving path of the robotic arm.

[0084] Optionally, the mechanical arm includes a telescopic rotating structure and an arc-shaped arm, the arc-shaped arm is rotatably connected to the telescopic rotating structure through a slewing support structure, the detection sensor is placed on the arc-shaped arm, and the planning module 20 is further used for: Determining a detection position of the slewing support structure according to the tunnel parameters; Calculating based on the detected position of the slewing support structure to obtain telescopic rotation parameters of the telescopic rotation structure; The target detection position and posture of the robotic arm are determined according to the telescopic rotation parameters of the telescopic rotation structure.

[0085] Optionally, the planning module 20 is further configured to: The detection position of the slewing support structure is set as the target position of the end effector, and the component parameters of the telescopic rotating structure are set as the restriction conditions, and a kinematic calculation model is constructed based on the target position of the end effector and the restriction conditions; The kinematic calculation model is solved to obtain the telescopic rotation parameters of the telescopic rotation structure.

[0086] Optionally, the tunnel parameters include one or more of tunnel radius, track position and obstacle position, the detection sensor includes one or more of ranging sensors, radar sensors and cameras, the telescopic rotation structure includes at least one telescopic connecting rod structure and at least one rotating joint structure, the telescopic connecting rod structures are connected by the rotating joint structure, and the telescopic rotation parameters include one or more of telescopic length and rotation angle.

[0087] Optionally, the detection module 30 is further used for: According to the moving path, collision detection is performed by using a pre-built collision detection model to obtain a collision detection result, wherein the collision detection model is built based on a target detection position and posture of the robot arm and the tunnel parameters; If the collision detection result indicates that there is a collision risk, the moving path is replanned according to the collision detection result and the tunnel parameters to obtain a replanned moving path, so as to control the movement of the robotic arm based on the replanned moving path.

[0088] The mechanical arm-controlled tunnel hidden disease detection device provided in the present application adopts the mechanical arm-controlled tunnel hidden disease detection method in the above embodiment, which can solve the technical problem of low detection efficiency of the existing tunnel hidden disease detection method. Compared with the prior art, the beneficial effects of the mechanical arm-controlled tunnel hidden disease detection device provided in the present application are the same as the beneficial effects of the mechanical arm-controlled tunnel hidden disease detection method provided in the above embodiment, and the other technical features of the mechanical arm-controlled tunnel hidden disease detection device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0089] The present application provides a tunnel hidden disease detection device controlled by a robot arm, and the tunnel hidden disease detection device controlled by a robot arm includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the tunnel hidden disease detection method controlled by the robot arm in the above-mentioned first embodiment.

[0090] Reference below Figure 7 , which shows a schematic diagram of the structure of a tunnel hidden disease detection device controlled by a mechanical arm suitable for implementing the embodiment of the present application. The tunnel hidden disease detection device controlled by a mechanical arm in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The tunnel hidden disease detection equipment controlled by a robotic arm shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0091] like Figure 7As shown, the tunnel hidden disease detection device controlled by the robot arm may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the tunnel hidden disease detection device controlled by the robot arm are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the tunnel hidden disease detection device controlled by the robot arm to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a tunnel hidden disease detection device controlled by a robot arm with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0092] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0093] The mechanical arm-controlled tunnel hidden disease detection device provided by the present application adopts the mechanical arm-controlled tunnel hidden disease detection method in the above embodiment, which can solve the technical problem of low detection efficiency of the existing tunnel hidden disease detection method. Compared with the prior art, the beneficial effects of the mechanical arm-controlled tunnel hidden disease detection device provided by the present application are the same as the beneficial effects of the mechanical arm-controlled tunnel hidden disease detection method provided by the above embodiment, and the other technical features of the mechanical arm-controlled tunnel hidden disease detection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0094] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0095] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0096] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for detecting hidden defects in tunnels controlled by a robot arm in the above-mentioned embodiment.

[0097] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0098] The above-mentioned computer-readable storage medium may be included in the tunnel hidden disease detection device controlled by a robotic arm; or it may exist independently without being assembled into the tunnel hidden disease detection device controlled by a robotic arm.

[0099] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the tunnel hidden disease detection device controlled by the robotic arm, the tunnel hidden disease detection device controlled by the robotic arm: obtains tunnel parameters of the tunnel to be detected; performs analysis and path planning based on the tunnel parameters to obtain the moving path of the robotic arm; controls the movement of the robotic arm based on the moving path, and detects the tunnel to be detected through the detection sensor of the robotic arm to obtain the tunnel detection result.

[0100] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The modules involved in the embodiments of the present application may be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.

[0103] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned mechanical arm-controlled tunnel hidden disease detection method, which can solve the technical problem of low detection efficiency of the existing tunnel hidden disease detection method. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the mechanical arm-controlled tunnel hidden disease detection method provided in the above-mentioned embodiment, and will not be repeated here.

[0104] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for detecting hidden defects in tunnels controlled by a robotic arm.

[0105] The computer program product provided by the present application can solve the technical problem of low detection efficiency of the existing tunnel hidden disease detection method. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the tunnel hidden disease detection method controlled by the mechanical arm provided in the above embodiment, which will not be described in detail here.

[0106] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for detecting hidden defects in tunnels controlled by a robotic arm, characterized in that: The method is applied to a tunnel detection platform, which is equipped with a mechanical arm and includes: Obtain tunnel parameters of the tunnel to be detected; Analyzing and planning the path according to the tunnel parameters to obtain the moving path of the robot arm; The movement of the mechanical arm is controlled based on the moving path, and the tunnel to be detected is detected by a detection sensor of the mechanical arm to obtain a tunnel detection result.

2. The method according to claim 1, characterized in that: The step of analyzing and planning the path according to the tunnel parameters to obtain the moving path of the robot arm comprises: Determining the target detection position and posture of the robotic arm according to the tunnel parameters; Based on the target detection position and posture of the robotic arm, path planning is performed according to the tunnel parameters to obtain the moving path of the robotic arm.

3. The method according to claim 2, characterized in that: The mechanical arm comprises a telescopic rotating structure and an arc-shaped arm, wherein the arc-shaped arm is rotatably connected to the telescopic rotating structure via a slewing support structure, and the detection sensor is disposed on the arc-shaped arm. The step of determining the target detection position and posture of the robotic arm according to the tunnel parameters comprises: Determining a detection position of the slewing support structure according to the tunnel parameters; Calculating based on the detected position of the slewing support structure to obtain telescopic rotation parameters of the telescopic rotation structure; The target detection position and posture of the robotic arm are determined according to the telescopic rotation parameters of the telescopic rotation structure.

4. The method according to claim 3, characterized in that: The step of calculating based on the detected position of the slewing support structure to obtain the telescopic rotation parameters of the telescopic rotation structure comprises: The detection position of the slewing support structure is set as the target position of the end effector, and the component parameters of the telescopic rotating structure are set as the restriction conditions, and a kinematic calculation model is constructed based on the target position of the end effector and the restriction conditions; The kinematic calculation model is solved to obtain the telescopic rotation parameters of the telescopic rotation structure.

5. The method according to any one of claims 3 to 4, characterized in that: The tunnel parameters include one or more of tunnel radius, track position and obstacle position; the detection sensor includes one or more of ranging sensors, radar sensors and cameras; the telescopic rotation structure includes at least one telescopic connecting rod structure and at least one rotating joint structure; the telescopic connecting rod structures are connected by the rotating joint structure; and the telescopic rotation parameters include one or more of telescopic length and rotation angle.

6. The method according to claim 1, characterized in that: Before the step of controlling the movement of the mechanical arm based on the movement path, the method further includes: According to the moving path, collision detection is performed by using a pre-built collision detection model to obtain a collision detection result, wherein the collision detection model is built based on a target detection position and posture of the robot arm and the tunnel parameters; If the collision detection result indicates that there is a collision risk, the moving path is replanned according to the collision detection result and the tunnel parameters to obtain a replanned moving path, so as to control the movement of the robotic arm based on the replanned moving path.

7. A tunnel hidden disease detection device controlled by a robotic arm, characterized in that: The device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for detecting hidden defects in a tunnel controlled by a robot arm as claimed in any one of claims 1 to 6.

8. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for detecting hidden defects in a tunnel controlled by a robot arm as described in any one of claims 1 to 6 are implemented.

9. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for detecting hidden defects in a tunnel controlled by a robot arm as claimed in any one of claims 1 to 6 are implemented.

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