Bridge steel box girder active inspection equipment based on parent-child robots and method thereof

Abstract

This invention discloses an active inspection equipment and method for bridge steel box girders based on a mother-daughter robot system, aiming to solve the problems of high risk and low efficiency of manual inspection, as well as the incomplete coverage and positioning difficulties of existing automation technologies. The equipment includes: a mother robot that travels along an existing maintenance track; a daughter robot that can be released by the mother robot and autonomously move within the complex internal structure of the steel box girder; a spatial network and positioning system employing a hybrid network of industrial WIFI and 5G and integrating ultra-wideband positioning technology; intelligent non-destructive testing equipment mounted on the daughter robot; and a control system for remote control and data analysis. This invention achieves comprehensive, accurate, and intelligent inspection of the steel box girder by scheduling and deploying the daughter robot through the mother robot. The daughter robot autonomously collects inspection data under high-precision positioning and stable communication, and uploads the data to the control system for intelligent analysis.

Description

Technical Field

[0001] This invention relates to the field of bridge inspection technology, and in particular to an automated inspection equipment and method for the interior of bridge steel box girders. Background Technology

[0002] As a key component of modern transportation infrastructure, the structural health and safety of long-span steel box girder bridges are of paramount importance. Under long-term exposure to vehicle loads and environmental erosion, steel box girder structures are prone to various defects such as weld fatigue cracking and steel plate corrosion. Failure to detect and address these defects in a timely manner can affect the bridge's service life.

[0003] Currently, the inspection of the interior of steel box girders mainly relies on a combination of inspection trolleys and manual labor. Inspectors must work for extended periods in the harsh environment of the confined, narrow space inside the steel box girder, characterized by high temperatures, noise, and dust. This not only results in high labor intensity but also poses significant safety risks. The efficiency of this method is limited by the physiological limits of personnel, making it difficult to achieve continuous, comprehensive inspection. Furthermore, the inspection results largely depend on the inspector's personal experience, making it prone to missed inspections or misjudgments. Simultaneously, the complex structures within the steel box girder, such as numerous U-ribs and transverse diaphragms, are difficult for manual access, creating blind spots and preventing the acquisition of complete structural condition information. In addition, manually recorded paper-based defect information is difficult to digitize and effectively trace.

[0004] To address these issues, some automated inspection technologies have emerged, such as mounting robotic arms on inspection vehicles. However, these solutions have limited intelligence; the robotic arm's trajectory is typically preset, lacking the ability to make autonomous decisions and plan paths based on complex and changing on-site environments. The robotic arm's operating radius and degrees of freedom are also limited, making it difficult to penetrate narrow spaces such as U-rib gaps, resulting in incomplete inspection coverage. Furthermore, retrofitting existing inspection vehicles in this way is costly and not conducive to widespread application on existing bridges.

[0005] Another key bottleneck restricting the development of automated inspection technology lies in the wireless communication and positioning issues within steel box girders. The metallic structure of steel box girders severely obstructs and attenuates wireless signals, resulting in uneven coverage of conventional wireless networks and failing to meet the real-time data transmission and precise positioning requirements of inspection robots. Simultaneously, traditional non-destructive testing equipment is typically large and complex to operate, making effective integration with miniaturized inspection robots difficult, thus hindering near-unmanned rapid inspection. Therefore, developing an automated inspection solution that adapts to the unique environment inside steel box girders, is cost-effective, and intelligently efficient is a pressing technical challenge in the field of bridge maintenance. Summary of the Invention

[0006] To overcome the existing problems and shortcomings, this invention proposes: 1. An active inspection equipment for bridge steel box girders based on a mother-daughter robot, characterized in that it includes:

[0007] The mother robot is adapted to and mounted on the existing maintenance trolley track of the steel box girder for long-distance transport;

[0008] The sub-robot, which is a multi-legged or humanoid robot, is released by the parent robot and configured to move autonomously between the U-ribs and transverse diaphragms of the steel box girder;

[0009] Intelligent non-destructive testing equipment mounted on the sub-robot;

[0010] A spatial network and positioning system deployed inside the steel box girder provides wireless communication for the mother and child robots using a hybrid networking mode of industrial WIFI and 5G, and provides centimeter-level positioning for the child robot using multi-source fusion positioning technology of ultra-wideband, inertial navigation, and visual odometry; and

[0011] The control system is configured to communicate with the mother-daughter robot and the space network and positioning system to receive and process the detection data collected by the flaw detection equipment.

[0012] Furthermore, the parent robot is also equipped with an edge computing unit for preprocessing the detection data before it is uploaded to the control system.

[0013] Furthermore, the parent robot is also equipped with a wireless charging module for charging the child robot after it returns from completing the detection task.

[0014] Furthermore, the intelligent non-destructive testing equipment is modularly designed and includes at least two of the following: a lightweight electromagnetic flaw detection sensor, a miniature ultrasonic probe, a high-definition camera, and a multispectral camera.

[0015] Furthermore, the sub-robot is also configured to automatically invoke a preset differentiated detection scheme to adjust the detection angle and parameters of the intelligent non-destructive testing equipment according to the different parts to be detected.

[0016] Furthermore, the control system is also configured to correlate the received detection data with a preset digital twin model of the bridge steel box girder in real time, so as to assess the health status of the steel box girder and predict the trend of disease development.

[0017] This invention also provides an active inspection method for bridge steel box girders based on a mother-daughter robot, comprising the following steps:

[0018] Scheduling steps: The parent robot mounted on the existing maintenance trolley track of the steel box girder is scheduled to travel to the target area;

[0019] Deployment steps: Release the daughter robot through the parent robot;

[0020] Data Acquisition Steps: Based on the wireless communication provided by the industrial WIFI and 5G hybrid networking mode, and according to the positioning information generated by the fusion positioning technology of ultra-wideband, inertial navigation and visual odometry, the sub-robot is controlled to move autonomously in the internal structure of the steel box girder, and the intelligent non-destructive testing equipment on it is used to collect detection data.

[0021] Analysis steps: The detection data is transmitted to the control system for analysis to assess the health status of the steel box girder.

[0022] Furthermore, prior to the analysis step, the method includes a step of preprocessing the detection data using an edge computing unit deployed on the parent robot.

[0023] Furthermore, the acquisition step includes: automatically calling a preset differentiated detection scheme to adjust the detection angle and parameters according to the different parts to be detected.

[0024] Furthermore, the analysis steps include: associating the detection data with a preset bridge digital twin model in real time, and automatically identifying and quantifying defects using deep learning algorithms.

[0025] Beneficial effects:

[0026] This invention employs a near-unmanned inspection mode using a mother-daughter robot collaborative operation. Inspection tasks are primarily completed by the robots, while personnel only need to perform remote monitoring without entering the steel box girder. This avoids safety risks for workers in harsh environments such as high temperatures and high noise levels, significantly reducing labor intensity. The equipment enables continuous inspection, increasing the frequency and efficiency of inspections.

[0027] To address the blind spot problem in existing technologies, the sub-robot of this invention adopts a configuration with high mobility, enabling it to flexibly traverse areas such as U-ribs and transverse diaphragms that are difficult for manual or traditional equipment to reach. Combined with autonomous path planning capabilities, it can achieve full-area coverage detection of key structural parts inside the steel box girder, thus solving the problem of incomplete detection range.

[0028] To address the issues of weak signals and difficult positioning inside steel box girders, this invention proposes a specific hybrid networking mode and multi-source fusion positioning technology. This provides reliable communication and positioning guarantees for the stable operation of inspection equipment, ensuring the accuracy of inspection paths and the precise traceability of defect locations.

[0029] Furthermore, this invention utilizes lightweight, modular non-destructive testing equipment deeply integrated with the sub-robot, combined with a deep learning-based defect identification model. This enables automated and quantitative analysis of typical defects such as weld cracking and steel plate corrosion, improving the accuracy and efficiency of defect identification. The design of this invention is based on existing bridge maintenance tracks, requiring no large-scale modifications, thus reducing implementation costs and possessing significant potential for widespread application. Moreover, by constructing a comprehensive digital management system, it provides data support for the full lifecycle health management and scientific maintenance decision-making of bridges. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is an overall schematic diagram of the inspection equipment described in this invention deployed inside a steel box girder;

[0032] Figure 2 This is a functional block diagram of the mother robot described in this invention;

[0033] Figure 3 This is a schematic diagram of the sub-robot and its multi-sensor integration described in this invention;

[0034] Figure 4 This is a complete workflow diagram of the inspection method described in this invention, from task assignment to report generation. Detailed Implementation

[0035] The present application will be described below with reference to specific embodiments:

[0036] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] Example 1:

[0038] This embodiment provides an active inspection equipment for bridge steel box girders based on a mother-daughter robot, and its overall deployment scheme is as follows: Figure 1 As shown, the equipment is deployed inside the steel box girder of a bridge. This girder has existing maintenance trolley tracks laid along its length, and a complex internal structure consisting of a top plate, bottom plate, U-ribs, and diaphragms. Specifically, the equipment includes: a parent robot, daughter robots, intelligent non-destructive testing equipment, a space network and positioning system, and a control system.

[0039] Reference Figure 2 The parent robot is a transport platform that autonomously travels along the existing maintenance trolley track. Its bottom is equipped with a drive and track-walking mechanism, which includes guide wheels adapted to the track and motor-driven walking wheels to achieve smooth movement on the track. The parent robot integrates a power management module and a charging dock, which is designed with an alignment guide structure and a wireless charging coil for automatic docking and energy replenishment of the child robots. Simultaneously, the parent robot also integrates an edge computing unit and a wireless communication gateway. The edge computing unit is a high-performance embedded computing module used for real-time preliminary processing of data transmitted back from the child robots; the wireless communication gateway supports 5G and industrial Wi-Fi dual-mode communication and is responsible for data interaction with the child robots and external control systems.

[0040] Reference Figure 1 and Figure 3 The sub-robot is specifically a quadruped robot (in the form of a robotic dog). Its quadrupedal structure gives it high terrain adaptability, allowing it to walk between the U-ribs inside the steel box girder and pass through manholes in the transverse diaphragms to reach areas that are difficult for the parent robot or humans to access. The sub-robot's torso integrates multimodal sensors for autonomous navigation, including a LiDAR deployed at the front end to construct a 3D point cloud map of the surrounding environment and achieve obstacle avoidance; and multiple high-definition cameras distributed throughout the body to collect visual information to assist in odometry calculations.

[0041] The intelligent non-destructive testing equipment is modularly mounted on the back or abdomen of the sub-robot. In this embodiment, the equipment integrates a lightweight electromagnetic flaw detection sensor for detecting fatigue damage inside welds, a miniature ultrasonic probe that can extend into confined spaces, and a high-definition camera for identifying rust and cracks on steel structures. These flaw detection devices are all connected to the sub-robot's central processing unit, which invokes them according to the testing task instructions.

[0042] The aforementioned spatial network and positioning system ensures the stable operation of the entire equipment. This system comprises multiple wireless access points / positioning base stations deployed at 20-30 meter intervals along the internal top or side walls of the steel box girder. Each base station integrates an industrial Wi-Fi module, a 5G micro base station module, and a UWB (Ultra-Wideband) positioning anchor point module. Through the collaborative work of multiple base stations, wireless network signal coverage is achieved throughout the internal space of the steel box girder. Simultaneously, a UWB tag is installed on the sub-robot, which communicates with the distributed UWB anchor points. Combining the sub-robot's own inertial measurement unit (IMU) and visual odometry, a multi-source fusion positioning algorithm deployed in the parent robot or base station edge computing nodes performs the calculations, ultimately achieving centimeter-level high-precision positioning of the sub-robot.

[0043] The control system is a cloud-based server running a control system software platform. This platform communicates with both the parent and child robots via a wireless network, receiving detection data collected by the child robots and pre-processed by the parent robot. The control system internally constructs a digital twin model of the bridge's steel box girder, accurately annotating and visualizing the type, size, and location information of uploaded defect data (such as cracks and corrosion) on this 3D model. This enables remote monitoring of equipment, task assignment, and unified management and in-depth analysis of detection data.

[0044] Example 2:

[0045] This embodiment provides an active inspection method for bridge steel box girders based on the equipment described in Embodiment 1. The method is performed according to the following steps, and its overall process can also be referred to. Figure 4 .

[0046] 1. Scheduling Steps

[0047] Inspection tasks are initiated by the remote control system based on a pre-set inspection plan or manually by maintenance personnel. Upon receiving the instruction, the control system uses the bridge's digital twin model to plan an overall inspection path covering the target area. Subsequently, the system transmits driving instructions for the parent robot via the 5G network.

[0048] After receiving the command, the parent robot activates its drive and track-walking mechanisms, autonomously moving along the existing maintenance trolley track laid inside the steel box girder. By recognizing the positioning markers beside the track and combining them with its own odometer, the parent robot precisely moves to the target compartment area specified in the mission command, preparing for the subsequent deployment of the sub-robots.

[0049] 2. Deployment Steps

[0050] Once the parent robot precisely docks in the target area, the deployment steps are executed. The parent robot activates its integrated release and retrieval mechanism, smoothly releasing the safely stored child robot onto the base plate of the steel box girder. After release, a high-bandwidth, low-latency internal wireless communication link is immediately established between the parent and child robots via industrial Wi-Fi, ensuring the stability of subsequent data transmission and collaborative control.

[0051] 3. Data Collection Steps

[0052] This step is performed entirely autonomously by the sub-robot.

[0053] In terms of communication and positioning, the sub-robot operates entirely within a spatial network utilizing a hybrid industrial Wi-Fi and 5G network, ensuring seamless communication with the parent robot and the cloud-based control system. Simultaneously, its movement and operational position are provided with centimeter-level positioning information through multi-source fusion positioning technology combining ultra-wideband (UWB), inertial navigation (IMU), and visual odometry, ensuring path accuracy and traceability of fault locations. Furthermore, regarding autonomous movement, the sub-robot utilizes sensors such as LiDAR to construct local 3D maps. Combined with the aforementioned high-precision positioning information, it autonomously plans and moves within the complex internal structure comprised of U-ribs and transverse diaphragms to approach and reach various pre-set detection points.

[0054] In terms of data acquisition, the sub-robot automatically utilizes its onboard intelligent non-destructive testing equipment based on the specific area to be inspected. For example, it captures images of rust on the surface of a steel plate using a high-definition camera, or scans weld seams using a lightweight electromagnetic flaw detection sensor to detect internal fatigue damage. All acquired raw inspection data is cached in real time and ready for transmission.

[0055] 4. Analysis Steps

[0056] The collected detection data is transmitted to the control system for analysis to assess the health condition of the steel box girder. This step can be divided into two levels:

[0057] (1) Edge preprocessing: The raw detection data collected by the sub-robot, especially large volumes of data such as high-definition images and flaw detection signals, will be uploaded to the edge computing unit on the parent robot via the internal Wi-Fi network. The edge computing unit performs preliminary screening, noise reduction and compression on these data to form structured and effective data, thereby reducing the bandwidth pressure on cloud network transmission.

[0058] (2) Cloud-based in-depth analysis: After preprocessing, the data is uploaded by the parent robot to the cloud-based control system via a 5G network. The cloud platform calls a deep learning-based disease identification model to intelligently analyze the uploaded data, automatically identify the type of disease, and quantify key indicators such as size and severity. The analysis results are linked in real time to the digital twin model of the bridge for three-dimensional visualization and compared with historical data to predict disease development trends. Finally, an assessment report is generated and necessary early warnings are triggered.

[0059] Example 3:

[0060] This embodiment further describes the execution details of the method described in Embodiment 2 in a specific application scenario, which is to conduct a focused fatigue damage investigation on a hard-to-access U-rib butt weld inside a steel box girder.

[0061] Task setting: On the digital twin model of the control system, the maintenance engineer clicked to select a U-rib butt weld section located between a specific numbered diaphragm and the top plate, and issued the "focus flaw detection" command.

[0062] Equipment Response and Deployment: The parent robot travels along the track to the compartment closest to the U-rib, docks, and releases the child robot. After being activated, the child robot receives specific mission instructions from the parent robot, namely, "navigate to the weld at coordinates (X, Y, Z) and execute the electromagnetic flaw detection procedure."

[0063] Navigation and Approach: The sub-robot is quadrupedal and first needs to traverse two transverse partitions. It uses a vision camera to identify the location and size of the manholes on the partitions, autonomously plans its traversal posture, and successfully passes through. Subsequently, it enters an area with numerous U-ribs, using LiDAR and a depth camera to perceive its surroundings, navigating flexibly through the narrow gaps between the U-ribs, and finally confirms its arrival below the target weld seam through a multi-source fusion positioning system.

[0064] Differential inspection scheme invocation: Upon reaching the target location, the sub-robot automatically invokes the preset "U-rib butt weld fatigue inspection" scheme. This scheme includes the following sub-steps:

[0065] a) Activate the high-definition camera to photograph the weld surface to identify the presence of visible surface cracks.

[0066] b) Adjust its own posture to accurately align the lightweight electromagnetic flaw detection sensor on board with the center line of the weld.

[0067] c) Activate the electromagnetic flaw detection sensor and slowly move it 50 cm along the weld direction to complete the scanning of the entire weld section and collect electromagnetic signal data.

[0068] Data Analysis and Result Presentation: The acquired high-definition images and electromagnetic signal data are wirelessly transmitted to the parent robot. The edge computing unit of the parent robot performs preliminary filtering and noise reduction on the signals before uploading them to the cloud control system via a 5G network. The defect identification model on the cloud server analyzes the electromagnetic signals, identifies anomaly areas, and calculates the presence of an internal fatigue microcrack measuring 2.1 cm in length and approximately 0.8 mm in depth within that area. Almost simultaneously, on the digital twin model in front of the maintenance engineer, this weld section is highlighted in red, and an information box pops up displaying the precise three-dimensional location, size parameters, and a high-definition photograph of the crack. Based on its built-in maintenance standards, the system automatically sets the warning level to "medium," recommending handling it within the next maintenance cycle.

[0069] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

Claims

1. An active inspection equipment for bridge steel box girders based on a mother-daughter robot, characterized in that, include: The mother robot is adapted to and mounted on the existing maintenance trolley track of the steel box girder for long-distance transport; The sub-robot, which is a multi-legged or humanoid robot, is released by the parent robot and configured to move autonomously between the U-ribs and transverse diaphragms of the steel box girder; Intelligent non-destructive testing equipment mounted on the sub-robot; A spatial network and positioning system deployed inside the steel box girder provides wireless communication for the mother and child robots using a hybrid networking mode of industrial WIFI and 5G, and provides centimeter-level positioning for the child robot using multi-source fusion positioning technology of ultra-wideband, inertial navigation and visual odometry. as well as The control system is configured to communicate with the mother-daughter robot and the space network and positioning system to receive and process the detection data collected by the flaw detection equipment.

2. The equipment according to claim 1, characterized in that, The parent robot is also equipped with an edge computing unit for preprocessing the detection data before it is uploaded to the control system.

3. The equipment according to claim 1, characterized in that, The parent robot is also equipped with a wireless charging module for charging the child robot after it returns from completing the detection task.

4. The equipment according to claim 1, characterized in that, The intelligent non-destructive testing equipment is modularly designed and includes at least two of the following: a lightweight electromagnetic flaw detection sensor, a miniature ultrasonic probe, a high-definition camera, and a multispectral camera.

5. The equipment according to claim 1 or 4, characterized in that, The sub-robot is also configured to automatically call a preset differentiated detection scheme according to the different parts to be detected, so as to adjust the detection angle and parameters of the intelligent non-destructive testing equipment.

6. The equipment according to claim 1, characterized in that, The control system is also configured to associate the received detection data with a preset digital twin model of the bridge steel box girder in real time, so as to assess the health status of the steel box girder and predict the trend of disease development.

7. A method for active inspection of bridge steel box girders based on a mother-daughter robot, characterized in that, Includes the following steps: Scheduling steps: The parent robot mounted on the existing maintenance trolley track of the steel box girder is scheduled to travel to the target area; Deployment steps: Release the daughter robot through the parent robot; Data Acquisition Steps: Based on the wireless communication provided by the industrial WIFI and 5G hybrid networking mode, and according to the positioning information generated by the fusion positioning technology of ultra-wideband, inertial navigation and visual odometry, the sub-robot is controlled to move autonomously in the internal structure of the steel box girder, and the intelligent non-destructive testing equipment on it is used to collect detection data. Analysis steps: The detection data is transmitted to the control system for analysis to assess the health status of the steel box girder.

8. The method according to claim 7, characterized in that, Prior to the analysis step, the method further includes a step of preprocessing the detection data using an edge computing unit deployed on the parent robot.

9. The method according to claim 7, characterized in that, The acquisition steps include: automatically calling a preset differentiated detection scheme to adjust the detection angle and parameters according to the different parts to be detected.

10. The method according to claim 7, characterized in that, The analysis steps include: linking the detection data with a preset bridge digital twin model in real time, and automatically identifying and quantifying defects using deep learning algorithms.

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