Tunnel hidden disease obstacle avoidance detection method, device, equipment, medium and product

Abstract

The invention discloses a tunnel hidden disease obstacle avoidance detection method, device, equipment, medium and product, and relates to the technical field of engineering detection, the tunnel hidden disease obstacle avoidance detection method is applied to a tunnel hidden disease detection vehicle, and the tunnel hidden disease detection vehicle is provided with an obstacle avoidance assembly and a detection assembly. The detection assembly is connected with the obstacle avoidance assembly, and the method comprises the steps that when the tunnel hidden disease detection vehicle carries out walking operation in a tunnel, inner wall data of the tunnel are obtained; and according to the inner wall data and the detection position of the detection assembly, a target obstacle to be avoided in the tunnel is identified, and the position of the detection assembly is adjusted through the obstacle avoidance assembly, so that the detection assembly avoids the target obstacle to perform detection operation. According to the invention, the real-time obstacle avoidance walking detection operation of the tunnel hidden disease detection vehicle is realized, and the obstacle avoidance accuracy during the walking detection operation in the tunnel is improved.

Description

Technical Field

[0001] The present application relates to the technical field of engineering detection, and particularly to a method, device, equipment, medium and product for detecting hidden diseases in tunnels with obstacle avoidance. Background Art

[0002] Due to the influence of terrain, geology, climate conditions and various factors in the design and construction process, tunnels will have defects to varying degrees during the construction process and even in the later use process. Since there are obstacles such as catenaries and wires in the tunnel, the detection will avoid the obstacles in the tunnel.

[0003] However, in the ground penetrating radar detection scenario of the existing tunnel hidden disease detection method, the ground penetrating radar needs to be in close contact with the inner wall of the tunnel for detection operations. Usually, sensors on the detection equipment are used to detect obstacles in the tunnel environment, and the accuracy is relatively low, and it is impossible to accurately avoid obstacles in the tunnel environment to complete the detection.

[0004] The above content is only used to assist in understanding the technical solution of the present application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of the present application is to provide a method, device, equipment, medium and product for detecting hidden diseases in tunnels with obstacle avoidance, aiming to improve the accuracy of obstacle avoidance during the detection operation of traveling in the tunnel.

[0006] To achieve the above object, the present application proposes a method for detecting hidden diseases in tunnels with obstacle avoidance. The method is applied to a tunnel hidden disease detection vehicle, and the tunnel hidden disease detection vehicle is configured with an obstacle avoidance component and a detection component. The detection component is connected to the obstacle avoidance component. The method includes:

[0007] When the tunnel hidden disease detection vehicle is performing a traveling operation in the tunnel, obtain the inner wall data of the tunnel;

[0008] According to the inner wall data and the detection position of the detection component, identify the target obstacles to be avoided in the tunnel, and through the obstacle avoidance component, adjust the position of the detection component so that the detection component avoids the target obstacles for detection operations.

[0009] In one embodiment, the detection component includes a binocular camera and a plurality of ranging sensors. The step of obtaining the inner wall data of the tunnel includes:

[0010] Collect the three-dimensional image data of the inner wall of the tunnel through the binocular camera;

[0011] Collect the distance data between the detection component and the inner wall of the tunnel through the plurality of ranging sensors;

[0012] Based on the three-dimensional inner wall image data and the distance data, the inner wall data of the tunnel is calculated.

[0013] In one embodiment, the step of identifying the target obstacles to be avoided in the tunnel according to the inner wall data and the detection positions of the detection components includes:

[0014] Based on the inner wall data, identify the obstacles in the tunnel;

[0015] Based on the detection positions of the detection components, perform a collision prediction on the obstacles in the tunnel to obtain a collision prediction result;

[0016] Based on the collision prediction result, identify the target obstacles to be avoided in the tunnel.

[0017] In one embodiment, the step of performing a collision prediction on the obstacles in the tunnel based on the detection positions of the detection components to obtain a collision prediction result includes:

[0018] Based on the detection positions of the detection components and the traveling speed of the tunnel hidden disease detection vehicle, calculate the traveling detection trajectory of the detection components;

[0019] Based on the traveling detection trajectory of the detection components, perform a collision prediction on the obstacles in the tunnel to obtain a collision prediction result.

[0020] In one embodiment, the obstacle avoidance component is a collaborative robot, and the step of adjusting the position of the detection component through the obstacle avoidance component so that the detection component avoids the target obstacle for detection operations includes:

[0021] Determine the relative position relationship between the target obstacle and the detection component;

[0022] Based on the relative position relationship, generate an obstacle avoidance strategy;

[0023] According to the obstacle avoidance strategy, adjust the position and posture of the collaborative robot to adjust the position of the detection component so that the detection component avoids the target obstacle for detection operations.

[0024] In one embodiment, the collaborative robot includes several levels of articulated arms, and the step of determining the relative position relationship between the target obstacle and the detection component includes:

[0025] Obtain the posture information of the several levels of articulated arms;

[0026] According to the posture information of the several levels of articulated arms, calculate the relative position relationship between the target obstacle and the detection component.

[0027] In addition, to achieve the above object, the present application further provides a tunnel hidden disease obstacle avoidance detection device, which is applied to a tunnel hidden disease detection vehicle. The tunnel hidden disease detection vehicle is configured with an obstacle avoidance component and a detection component, and the detection component is connected to the obstacle avoidance component. The device includes:

[0028] An acquisition module, configured to acquire inner wall data of the tunnel when the tunnel hidden disease detection vehicle is performing a running operation in the tunnel;

[0029] An adjustment module, configured to identify a target obstacle to be avoided in the tunnel according to the inner wall data and the detection position of the detection component, and adjust the position of the detection component through the obstacle avoidance component, so that the detection component avoids the target obstacle for detection operations.

[0030] In addition, to achieve the above object, the present application further provides a tunnel hidden disease obstacle avoidance detection device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the tunnel hidden disease obstacle avoidance detection method as described above.

[0031] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of the tunnel hidden disease obstacle avoidance detection method as described above.

[0032] In addition, to achieve the above object, the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the tunnel hidden disease obstacle avoidance detection method as described above.

[0033] The present application provides a tunnel hidden disease obstacle avoidance detection method. First, when the tunnel hidden disease detection vehicle is performing a running operation in the tunnel, the present application acquires inner wall data of the tunnel to provide basic data for subsequent obstacle identification and disease detection. Then, the inner wall data of the tunnel is used to identify a target obstacle to be avoided, and the position of the detection component is adjusted through the obstacle avoidance component, so that the detection component avoids the target obstacle, completes the detection operation, avoids the detection component from colliding with the obstacle, and ensures the smooth progress of the detection operation. The running detection operation of the tunnel hidden disease detection vehicle with real-time obstacle avoidance is realized, and the accuracy of obstacle avoidance during the running detection operation in the tunnel is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application and, together with the specification, are used to explain the principles of this application.

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0036] Figure 1 It is a schematic flowchart provided for the first embodiment of the method for detecting and avoiding obstacles of hidden diseases in tunnels of this application;

[0037] Figure 2 It is a schematic flowchart provided for the second embodiment of the method for detecting and avoiding obstacles of hidden diseases in tunnels of this application;

[0038] Figure 3 It is a schematic flowchart provided for the third embodiment of the method for detecting and avoiding obstacles of hidden diseases in tunnels of this application;

[0039] Figure 4 It is a schematic diagram of the module structure of the device for detecting and avoiding obstacles of hidden diseases in tunnels in the embodiments of this application;

[0040] Figure 5 It is a schematic diagram of the device structure of the hardware operating environment involved in the method for detecting and avoiding obstacles of hidden diseases in tunnels in the embodiments of this application.

[0041] The achievement of the objectives, functional features, and advantages of this application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0042] It should be understood that the specific embodiments described here are only used to explain the technical solutions of this application and are not used to limit this application.

[0043] To better understand the technical solutions of this application, the following will be described in detail in combination with the drawings in the specification and specific embodiments.

[0044] The main solution of the embodiments of this application is: when the vehicle for detecting hidden diseases in tunnels performs a traveling operation in the tunnel, the inner wall data of the tunnel is acquired; according to the inner wall data and the detection positions of the detection components, the target obstacles to be avoided in the tunnel are identified, and the positions of the detection components are adjusted through the obstacle avoidance components so that the detection components avoid the target obstacles for detection operations.

[0045] In the prior art, in the scenario of ground penetrating radar detection in tunnels, to meet the requirement of the radar closely contacting the inner wall of the tunnel for detection operations, the advantage of the degree of freedom of the lifting component reaching any position of the tunnel cross-section is utilized to assist the ground penetrating radar in avoiding the irregular parts of the inner wall of the tunnel. However, this kind of lifting component cannot accurately avoid obstacles in the tunnel during the tunnel walking detection.

[0046] In this application, first, when the tunnel hidden disease detection vehicle is performing a walking operation in the tunnel, the inner wall data of the tunnel is acquired to provide basic data for subsequent obstacle recognition and disease detection. Then, the inner wall data of the tunnel is used to identify the target obstacles that need to be avoided, and the position of the detection component is adjusted through the obstacle avoidance component, so that the detection component avoids the target obstacles, completes the detection operation, avoids the collision between the detection component and the obstacles, and ensures the smooth progress of the detection operation. The walking detection operation of real-time obstacle avoidance for the tunnel hidden disease detection vehicle is realized, and the accuracy of obstacle avoidance during the walking detection operation in the tunnel is improved.

[0047] 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, a tunnel hidden disease obstacle avoidance detection device, etc. that can implement the above functions. Hereinafter, taking the tunnel hidden disease obstacle avoidance detection device as an example, this embodiment and the following embodiments will be described.

[0048] Based on this, the embodiment of this application provides a method for detecting tunnel hidden diseases and avoiding obstacles, referring to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the method for detecting tunnel hidden diseases and avoiding obstacles in this application.

[0049] In this embodiment, the method for detecting tunnel hidden diseases and avoiding obstacles is applied to a tunnel hidden disease detection vehicle. The tunnel hidden disease detection vehicle is configured with an obstacle avoidance component and a detection component, and the detection component is connected to the obstacle avoidance component, including steps S10 to S20:

[0050] Step S10, when the tunnel hidden disease detection vehicle is performing a walking operation in the tunnel, acquire the inner wall data of the tunnel;

[0051] It should be noted that the tunnel hidden disease detection vehicle is a special device for tunnel internal detection, usually equipped with a variety of detection components, and can simultaneously have the functions of walking and detection.

[0052] It can be understood that when the tunnel hidden disease detection vehicle is driving in the tunnel, the inner wall data of the tunnel is acquired to ensure that the tunnel hidden disease detection vehicle can comprehensively and accurately obtain the detailed information of the tunnel inner wall, providing data support for subsequent obstacle avoidance and disease detection.

[0053] Step S20: Based on the inner wall data and the detection position of the detection component, identify the target obstacles to be avoided in the tunnel, and through the obstacle avoidance component, adjust the position of the detection component so that the detection component avoids the target obstacles for detection operations.

[0054] It should be noted that the obstacle avoidance component is used to adjust the position of the detection component to avoid collisions with obstacles inside the tunnel. It can be an active obstacle avoidance structure or a mechanical passive obstacle avoidance structure. The detection component is used to obtain data of the tunnel inner wall and can complete the running detection operation of the tunnel by avoiding obstacles in the tunnel according to the adjustment of the obstacle avoidance component.

[0055] It can be understood that by real-time monitoring the data of the tunnel inner wall, combining the detection position of the detection component, identifying the obstacles to be avoided, and using the obstacle avoidance component to adjust the position of the detection component, the detection component can avoid obstacles to complete the detection operation, prevent the detection component from colliding with obstacles, ensure the smooth progress of the detection operation, improve the safety and reliability of the detection, and reduce the risks of equipment damage and detection interruption caused by collisions.

[0056] In a feasible implementation manner, the detection component includes a binocular camera and a plurality of ranging sensors. The step of obtaining the data of the tunnel inner wall includes:

[0057] Step S101: Through the binocular camera, collect the three-dimensional image data of the tunnel inner wall;

[0058] It should be noted that a binocular camera is an imaging device that simulates the visual principle of the human eye. By equipping two lenses to capture images of the scene, it is similar to humans obtaining depth information and three-dimensional sense through both eyes. By taking pictures of the same scene from different angles with two lenses, the binocular camera can obtain depth information about the scene, thereby performing tasks such as 3D reconstruction and object detection.

[0059] It can be understood that by using two cameras of the binocular camera to take pictures of the same scene from different angles, three-dimensional depth information is obtained through parallax calculation, thereby obtaining three-dimensional image data of the tunnel inner wall, which can provide stereoscopic visual information of the tunnel inner wall to help identify diseases such as cracks and deformations as well as avoid obstacles.

[0060] Step S102: Through the plurality of ranging sensors, collect the distance data between the detection component and the inner wall of the tunnel;

[0061] It should be noted that a ranging sensor is a device used to measure the distance between an object and the sensor. For example, a laser ranging sensor can calculate the distance by emitting a laser beam and measuring the time it takes for the laser to return.

[0062] It can be understood that the ranging sensor emits laser light and receives the reflected signal, calculates the distance to the target object, and can collect the real-time distance between the overall detection component and the tunnel inner wall through multiple ranging sensors, providing data support for obstacle avoidance to ensure that the detection component can adjust its position in time when approaching an obstacle and avoid collisions.

[0063] Step S103, calculate the inner wall data of the tunnel based on the three-dimensional image data and distance data of the inner wall.

[0064] It can be understood that the three-dimensional image data of the binocular camera is fused with the distance data of the ranging sensor to generate complete tunnel inner wall data, integrating the data of multiple sensors, improving the accuracy and reliability of detection, providing comprehensive tunnel inner wall information, and providing a basis for subsequent disease analysis and obstacle avoidance operations.

[0065] In this embodiment, the binocular camera is used to provide high-precision three-dimensional image data, and the laser ranging sensor is used to provide real-time distance data. The combination of the two can comprehensively and accurately obtain tunnel inner wall information, not only improving the detection efficiency, but also enhancing the safety and reliability of detection, and can effectively reduce the risk of equipment damage and detection interruption caused by collisions.

[0066] This embodiment provides a method for detecting hidden diseases and avoiding obstacles in a tunnel. First, when the tunnel hidden disease detection vehicle is traveling in the tunnel, the inner wall data of the tunnel is obtained to provide basic data for subsequent obstacle recognition and disease detection; then, the inner wall data of the tunnel and the detection position of the detection component are used to identify the target obstacles that need to be avoided, and the position of the detection component is adjusted through the obstacle avoidance component so that the detection component avoids the target obstacles, completing the detection operation, avoiding collisions between the detection component and the obstacles, and ensuring the smooth progress of the detection operation. The traveling detection operation of real-time obstacle avoidance of the tunnel hidden disease detection vehicle is realized, and the accuracy of obstacle avoidance during the traveling detection operation in the tunnel is improved.

[0067] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as the above first embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 2 , Figure 2 which is the flow schematic diagram of the second embodiment of the method for detecting hidden diseases and avoiding obstacles in the tunnel of the present application.

[0068] In this embodiment, the step of identifying the target obstacles to be avoided in the tunnel according to the inner wall data and the detection position of the detection component includes:

[0069] Step S201, identify the obstacles in the tunnel according to the inner wall data;

[0070] It should be noted that an obstacle refers to an object or structure in the tunnel that may affect the normal operation of the detection component, such as protrusions, cracks, water seepage, etc. Image processing techniques (such as edge detection, feature extraction, etc.) can be used to identify obstacles from the image data in the inner wall data.

[0071] It can be understood that by using the inner wall data, obstacles in the tunnel are identified through image processing and data analysis algorithms, and all obstacles in the tunnel are accurately identified from the obtained inner wall data to ensure that abnormal structures or obstacles in the tunnel can be discovered in time, providing a basis for subsequent collision prediction and obstacle avoidance operations.

[0072] Step S202: Based on the detection position of the detection component, perform a collision prediction on the obstacles in the tunnel to obtain a collision prediction result.

[0073] It should be noted that the detection position of the detection component refers to the optimal detection position of the detection component in the tunnel cross-section, which is the reference basis for collision prediction and is used to judge the relative position and movement trajectory between the detection component and the obstacle. Collision detection can calculate the spatial relationship between the two based on the geometric models of the detection component and the obstacle, predict the collision risk, and provide accurate collision prediction results, enabling the avoidance of obstacles with regular shapes; it can also combine the movement trajectory and speed information of the detection component to predict its relative movement with the obstacle, judge the collision risk, consider the dynamic characteristics of the detection component, and be able to adapt to complex movement scenarios.

[0074] It can be understood that according to the detection position of the detection component on the inner wall of the tunnel, combined with the position and shape of the obstacle, a collision detection algorithm is used to predict whether there is a collision risk, so as to pre-judge whether the detection component will collide with the obstacle during the continued detection process, providing a decision-making basis for the obstacle avoidance operation, and ensuring that the detection component can adjust its position in time when approaching the obstacle to avoid collision.

[0075] Step S203: According to the collision prediction result, identify the target obstacle to be avoided in the tunnel.

[0076] It can be understood that according to the collision prediction result, the obstacles that pose a collision risk to the detection component are screened out as the target obstacles to be avoided, providing a specific target for the adjustment of the obstacle avoidance component, ensuring the pertinence and effectiveness of the obstacle avoidance operation, avoiding unnecessary obstacle avoidance actions, and improving the detection efficiency.

[0077] In a feasible implementation manner, the step of performing a collision prediction on the obstacles in the tunnel based on the detection position of the detection component to obtain a collision prediction result includes:

[0078] Step S2021: Calculate the running detection trajectory of the detection component based on the detection position of the detection component and the running speed of the tunnel hidden disease detection vehicle.

[0079] It should be noted that the running speed of the tunnel hidden disease detection vehicle represents the driving speed of the tunnel hidden disease detection vehicle, which is a key parameter for calculating the running detection trajectory of the detection component and determines the extension speed and direction of the trajectory. The running detection trajectory of the detection component represents the movement path of the detection component in the future period of time, which is the reference path for collision prediction and is used to judge whether the detection component will collide with obstacles.

[0080] It can be understood that according to the detection position of the detection component, the driving speed and driving direction of the tunnel hidden disease detection vehicle, the kinematic formula is used to calculate the movement trajectory of the detection component in the future period of time, and the movement path of the detection component during the detection operation in the tunnel can be obtained, providing a basis for collision prediction and ensuring that the movement trend of the detection component can be understood in advance so as to discover potential collision risks in time.

[0081] Step S2022: Perform collision prediction on the obstacles in the tunnel based on the running detection trajectory of the detection component to obtain a collision prediction result.

[0082] It can be understood that the running detection trajectory of the detection component is compared with the recognized obstacle position, and it is judged whether there is an intersection between the two through spatial geometric analysis or collision detection algorithm, so as to judge whether the detection component will collide with the obstacle during the future movement process, provide a collision prediction result, and provide a decision basis for subsequent obstacle avoidance operations.

[0083] In this embodiment, based on the detection position of the detection component and the running speed of the tunnel hidden disease detection vehicle, the running detection trajectory of the detection component is accurately calculated, and it is judged whether the detection component will collide with the obstacle through the collision prediction algorithm, which can adapt to complex tunnel environments and driving conditions.

[0084] In this embodiment, the tunnel hidden disease detection vehicle can accurately identify the obstacles in the tunnel from the inner wall data, perform collision prediction on the obstacles according to the real-time position and movement state of the detection component, and finally screen out the target obstacles that need to be avoided, which not only improves the accuracy and reliability of obstacle avoidance, but also optimizes the efficiency of obstacle avoidance operations, ensuring that the detection vehicle can complete the detection task safely and efficiently in a complex tunnel environment.

[0085] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar content as the above first embodiment and second embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 3 , Figure 3It is a schematic flowchart of the third embodiment of the method for detecting and avoiding obstacles of hidden diseases in tunnels in this application.

[0086] In this embodiment, the obstacle avoidance component is a collaborative robot. The step of adjusting the position of the detection component through the obstacle avoidance component includes:

[0087] Step S204, determining the relative position relationship between the target obstacle and the detection component;

[0088] It should be noted that the relative position relationship represents the spatial relationship between the target obstacle and the detection component, including distance, angle, etc., which can be used to verify whether the obstacle avoidance operation is in place. The relative position relationship affects the motion parameters of the detection component and the obstacle avoidance component during detection.

[0089] It can be understood that determining the relative position (such as distance, angle, etc.) between the target obstacle and the detection component to clarify the spatial relationship between the target obstacle and the detection component provides a basis for generating an obstacle avoidance strategy, ensures the pertinence and accuracy of the obstacle avoidance operation, and avoids unnecessary obstacle avoidance actions.

[0090] Step S205, generating an obstacle avoidance strategy based on the relative position relationship;

[0091] It should be noted that as an obstacle avoidance component, the collaborative robot can adjust the position and posture of the detection component through the motion control of its joints, can provide its own position and posture information as the basis for generating an obstacle avoidance strategy, and can adjust its own position and posture according to the obstacle avoidance strategy to drive the detection component to avoid obstacles. The obstacle avoidance strategy can include the obstacle avoidance path and direction of the obstacle avoidance component and the traveling speed of the tunnel hidden disease detection vehicle during obstacle avoidance, etc.

[0092] In addition, it should be noted that the tunnel hidden disease detection vehicle can also be configured with a lifting component. The detection component is connected to the lifting component through the obstacle avoidance component. The lifting component can include a multi-stage telescopic structure, and can adjust the height of the detection component by controlling the parameters of each stage of the telescopic structure, so that it can cover different areas of the tunnel inner wall. The obstacle avoidance strategy can also include the parameters related to the telescopic degree of each stage of the telescopic structure when the lifting component avoids obstacles. When the collaborative robot cannot completely avoid the obstacles on the tunnel inner wall, the height of the detection component can be reduced by changing the length of the lifting component to achieve the obstacle avoidance operation of the detection component.

[0093] It can be understood that according to the relative position relationship between the target obstacle and the detection component, combined with the preset obstacle avoidance rules and algorithms, a specific obstacle avoidance strategy is generated, and a reasonable obstacle avoidance plan is formulated to ensure that the detection component can safely and efficiently avoid the target obstacle, and at the same time provide clear instructions for the motion control of the collaborative robot to ensure the smooth progress of the obstacle avoidance operation.

[0094] Step S206: According to the obstacle avoidance strategy, adjust the position and posture of the collaborative robot to adjust the position of the detection component, so that the detection component avoids the target obstacle for detection operations.

[0095] It can be understood that the collaborative robot adjusts its own position and posture through the motion control of its joints according to the generated obstacle avoidance strategy, thereby driving the detection component to move to a safe position. Through the flexible movement of the collaborative robot, it is ensured that the detection component can avoid the target obstacle and continue to complete the detection task, thereby realizing the specific execution of the obstacle avoidance operation and ensuring that the detection component can remain stable during the obstacle avoidance process and continue the detection operation.

[0096] In a feasible implementation manner, the collaborative robot includes several levels of joint arms. The step of determining the relative position relationship between the target obstacle and the detection component includes:

[0097] Step S2041: Obtain the posture information of the several levels of joint arms;

[0098] It should be noted that the joint arm is a component of the collaborative robot, and the position and posture of the detection component are adjusted through the movement of the joints. The collaborative robot usually realizes its functions through the coordinated movement of three levels of joint arms. The posture information represents the current state of the joint arm, including angles, positions, etc., and can be obtained by configuring posture sensors.

[0099] It can be understood that by installing sensors on each level of the joint arms of the collaborative robot, the posture information of the joint arms is obtained in real time to determine the current state of each level of the joint arms of the collaborative robot, ensuring that the posture of the collaborative robot can be accurately understood.

[0100] Step S2042: Calculate the relative position relationship between the target obstacle and the detection component according to the posture information of the several levels of joint arms.

[0101] It can be understood that by combining the posture information of the joint arms and the position information of the target obstacle, the relative position (such as distance, angle, etc.) between the detection component and the target obstacle is calculated to clarify the spatial relationship between the target obstacle and the detection component, providing a basis for generating the obstacle avoidance strategy, ensuring the pertinence and accuracy of the obstacle avoidance operation of the collaborative robot, and avoiding unnecessary obstacle avoidance actions.

[0102] In this implementation manner, the posture information of the joint arms is measured by an encoder or a gyroscope and an accelerometer to ensure the acquisition of high-precision and real-time posture data, and the relative position relationship is calculated by using a kinematic model or sensor fusion technology to ensure the accuracy and reliability of the calculation results.

[0103] In this embodiment, first, the relative position relationship between the target obstacle and the detection component is obtained to ensure the pertinence and accuracy of the obstacle avoidance operation; then, an obstacle avoidance strategy is generated according to the relative position relationship, and a reasonable obstacle avoidance plan is formulated by using a preset rule or a path planning algorithm; finally, the collaborative robot adjusts its own position and posture according to the obstacle avoidance strategy, drives the detection component to avoid the obstacle, and continues to complete the detection task, which not only improves the accuracy and reliability of obstacle avoidance, but also can adapt to complex tunnel environments and dynamic conditions.

[0104] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the tunnel hidden disease obstacle avoidance detection method of this application. Based on this technical concept, more forms of simple transformations are within the protection scope of this application.

[0105] This application also provides a tunnel hidden disease obstacle avoidance detection device. Please refer to Figure 4 , the tunnel hidden disease obstacle avoidance detection device is applied to a tunnel hidden disease detection vehicle. The tunnel hidden disease detection vehicle is configured with an obstacle avoidance component and a detection component. The detection component is connected to the obstacle avoidance component. The device includes:

[0106] An acquisition module 10, configured to obtain the inner wall data of the tunnel when the tunnel hidden disease detection vehicle is performing a running operation in the tunnel;

[0107] An adjustment module 20, configured to identify a target obstacle to be avoided in the tunnel according to the inner wall data and the detection position of the detection component, and adjust the position of the detection component through the obstacle avoidance component so that the detection component avoids the target obstacle for detection operations.

[0108] Optionally, the detection component includes a binocular camera and several distance sensors. The acquisition module 10 is further configured to:

[0109] Collect three-dimensional image data of the inner wall of the tunnel through the binocular camera;

[0110] Collect distance data between the detection component and the inner wall of the tunnel through the several distance sensors;

[0111] Calculate the inner wall data of the tunnel according to the three-dimensional image data of the inner wall and the distance data.

[0112] Optionally, the adjustment module 20 is further configured to:

[0113] Identify obstacles in the tunnel according to the inner wall data;

[0114] Perform a collision prediction on the obstacles in the tunnel based on the detection position of the detection component to obtain a collision prediction result;

[0115] Based on the collision prediction result, identify the target obstacle to be avoided in the tunnel.

[0116] Optionally, the adjustment module 20 is further configured to:

[0117] Calculate the running detection trajectory of the detection component based on the detection position of the detection component and the running speed of the tunnel hidden disease detection vehicle.

[0118] Based on the running detection trajectory of the detection component, perform collision prediction on the obstacles in the tunnel to obtain a collision prediction result.

[0119] Optionally, the obstacle avoidance component is a collaborative robot, and the adjustment module 20 is further configured to:

[0120] Determine the relative position relationship between the target obstacle and the detection component.

[0121] Generate an obstacle avoidance strategy based on the relative position relationship.

[0122] According to the obstacle avoidance strategy, adjust the position and posture of the collaborative robot to adjust the position of the detection component, so that the detection component avoids the target obstacle for detection operations.

[0123] Optionally, the collaborative robot includes several levels of articulated arms, and the adjustment module 20 is further configured to:

[0124] Obtain the posture information of the several levels of articulated arms.

[0125] Calculate the relative position relationship between the target obstacle and the detection component according to the posture information of the several levels of articulated arms.

[0126] The tunnel hidden disease obstacle avoidance detection device provided by the present application adopts the tunnel hidden disease obstacle avoidance detection method in the above embodiment, and can improve the accuracy of obstacle avoidance during running detection operations in the tunnel. Compared with the prior art, the beneficial effects of the tunnel hidden disease obstacle avoidance detection device provided by the present application are the same as those of the tunnel hidden disease obstacle avoidance detection method provided by the above embodiment, and other technical features in the tunnel hidden disease obstacle avoidance detection device are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0127] The present application provides a tunnel hidden disease obstacle avoidance detection device, and the tunnel hidden disease obstacle avoidance detection device 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 to enable the at least one processor to execute the tunnel hidden disease obstacle avoidance detection method in the above first embodiment.

[0128] Refer to the following Figure 5 , which shows a schematic structural diagram of a tunnel hidden disease avoidance detection device suitable for implementing the embodiments of the present application. The tunnel hidden disease avoidance detection device in the embodiments 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 Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The shown tunnel hidden disease avoidance detection device is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0129] As Figure 5 shown, the tunnel hidden disease avoidance detection device 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 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the tunnel hidden disease avoidance detection device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the tunnel hidden disease avoidance detection device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a tunnel hidden disease avoidance detection device with various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be alternatively implemented or had.

[0130] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. 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 a processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0131] The tunnel hidden disease obstacle avoidance detection device provided by the present application adopts the tunnel hidden disease obstacle avoidance detection method in the above-mentioned embodiment, and can improve the accuracy of obstacle avoidance during the walking detection operation in the tunnel. Compared with the prior art, the beneficial effects of the tunnel hidden disease obstacle avoidance detection device provided by the present application are the same as those of the tunnel hidden disease obstacle avoidance detection method provided by the above-mentioned embodiment, and other technical features in the tunnel hidden disease obstacle avoidance detection device are the same as the features disclosed in the method of the previous embodiment, which will not be elaborated here.

[0132] It should be understood that the various parts disclosed in the present 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 a suitable manner in any one or more embodiments or examples.

[0133] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0134] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the tunnel hidden disease obstacle avoidance detection method in the above-mentioned embodiment.

[0135] The computer-readable storage medium provided by the present application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0136] The above computer-readable storage medium can be included in the tunnel hidden disease obstacle avoidance detection device; or it can exist separately and not be assembled into the tunnel hidden disease obstacle avoidance detection device.

[0137] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the tunnel hidden disease obstacle avoidance detection device, the tunnel hidden disease obstacle avoidance detection device is enabled to: when the tunnel hidden disease detection vehicle is performing a running operation in the tunnel, obtain the inner wall data of the tunnel; based on the inner wall data and the detection position of the detection component, identify the target obstacle to be avoided in the tunnel, and through the obstacle avoidance component, adjust the position of the detection component so that the detection component avoids the target obstacle for detection operations.

[0138] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent 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 can be connected to the user's computer through any type of network, including a local area network (LAN: Local Area Network) or a wide area network (WAN: Wide Area Network), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0140] The modules involved in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0141] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned tunnel hidden disease obstacle avoidance detection method, which can improve the accuracy of obstacle avoidance during the detection operation of traveling in the tunnel. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the tunnel hidden disease detection method provided by the above embodiments, and will not be elaborated here.

[0142] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the tunnel hidden disease obstacle avoidance detection method as described above.

[0143] The computer program product provided by the present application can improve the accuracy of obstacle avoidance during the walking detection operation in a tunnel. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the tunnel hidden disease obstacle avoidance detection method provided by the above embodiments, and will not be elaborated herein.

[0144] The above are only partial embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the specification and drawings of the present application under the technical concept of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A method for detecting hidden defects in tunnels, characterized in that: The method is applied to a tunnel hidden defect detection vehicle, the tunnel hidden defect detection vehicle is equipped with an obstacle avoidance component and a detection component, the detection component is connected to the obstacle avoidance component, and the method comprises: When the tunnel hidden disease detection vehicle is running in the tunnel, the inner wall data of the tunnel is obtained; According to the inner wall data and the detection position of the detection component, the target obstacle to be avoided in the tunnel is identified, and the position of the detection component is adjusted through the obstacle avoidance component so that the detection component avoids the target obstacle to perform the detection operation.

2. The method according to claim 1, characterized in that The detection component includes a binocular camera and a plurality of distance measuring sensors, and the step of obtaining the inner wall data of the tunnel includes: Collecting three-dimensional image data of the inner wall of the tunnel by the binocular camera; By means of the plurality of distance measuring sensors, distance data between the detection component and the inner wall of the tunnel is collected; The inner wall data of the tunnel is calculated based on the inner wall three-dimensional image data and the distance data.

3. The method according to claim 1, characterized in that The step of identifying the target obstacle to be avoided in the tunnel according to the inner wall data and the detection position of the detection component comprises: identifying obstacles in the tunnel according to the inner wall data; Based on the detection position of the detection component, a collision prediction is performed on the obstacle in the tunnel to obtain a collision prediction result; According to the collision prediction result, a target obstacle to be avoided in the tunnel is identified.

4. The method according to claim 3, characterized in that The step of performing collision prediction on obstacles in the tunnel based on the detection position of the detection component to obtain a collision prediction result comprises: Calculating a running detection trajectory of the detection component based on the detection position of the detection component and the running speed of the tunnel hidden disease detection vehicle; Based on the travel detection track of the detection component, collision prediction is performed on obstacles in the tunnel to obtain a collision prediction result.

5. The method according to claim 1, characterized in that The obstacle avoidance component is a collaborative robot, and the step of adjusting the position of the detection component by the obstacle avoidance component so that the detection component avoids the target obstacle to perform the detection operation includes: Determining the relative position relationship between the target obstacle and the detection component; Based on the relative position relationship, generating an obstacle avoidance strategy; According to the obstacle avoidance strategy, the position and posture of the collaborative robot are adjusted to adjust the position of the detection component so that the detection component avoids the target obstacle to perform the detection operation.

6. The method according to claim 5, characterized in that The collaborative robot includes a plurality of articulated arms, and the step of determining the relative position relationship between the target obstacle and the detection component includes: Acquiring posture information of the plurality of levels of articulated arms; The relative position relationship between the target obstacle and the detection component is calculated based on the posture information of the plurality of levels of articulated arms.

7. A tunnel hidden disease obstacle avoidance detection device, characterized in that: The device is applied to a tunnel hidden disease detection vehicle, the tunnel hidden disease detection vehicle is equipped with an obstacle avoidance component and a detection component, the detection component is connected to the obstacle avoidance component, and the device includes: An acquisition module, used for acquiring the inner wall data of the tunnel when the tunnel hidden disease detection vehicle is running in the tunnel; The adjustment module is used to identify the target obstacle to be avoided in the tunnel according to the inner wall data and the detection position of the detection component, and adjust the position of the detection component through the obstacle avoidance component so that the detection component avoids the target obstacle to perform the detection operation.

8. A tunnel hidden disease obstacle avoidance detection device, 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 tunnel hidden hazard obstacle avoidance detection method as claimed in any one of claims 1 to 6.

9. 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 tunnel hidden hazard obstacle avoidance detection method as described in any one of claims 1 to 6 are implemented.

10. 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 and avoiding obstacles in tunnels as claimed in any one of claims 1 to 6 are implemented.

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