Internal detection system and method for nest bridge steel box girder of self-walking unmanned aerial vehicle

Abstract

The invention discloses a self-walking unmanned aerial vehicle nest bridge steel box girder internal detection system and method. The system is composed of a track traveling chassis, a laser radar subunit, an image monitoring subunit, an unmanned aerial vehicle cable laying and collecting mechanism, one or more unmanned aerial vehicle carrying tables, a communication unit, a storage unit, a calculation unit, a control unit and a power supply unit. The laser radar subunit realizes position positioning of the self-walking nest in a bridge, and realizes position positioning and attitude calculation of the unmanned aerial vehicle in a steel box girder; through the cooperation of the self-walking nest and the mooring unmanned aerial vehicle, crack and defect detection by using the unmanned aerial vehicle in the steel box girder has establishment conditions; and tracking and monitoring of the unmanned aerial vehicle and image and video acquisition of the internal environment of the steel box girder are realized through the image monitoring subunit. According to the system and the method, autonomous driving of the nest in the bridge and autonomous flight of the unmanned aerial vehicle are ensured, and the internal patrol and crack and defect detection efficiency of the steel box girder is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned vehicles, unmanned aerial vehicles and intelligent detection technology, and in particular to a self-propelled unmanned aerial vehicle machine nest bridge steel box girder internal detection system and method. Background Art

[0002] At present, there are more and more cable-stayed bridges, cross-river and cross-sea bridges in the world. The workload and labor intensity of inspecting and testing the inside of steel box girders are increasing. The daily maintenance and repair of the web of steel box girders are still mainly based on manual inspection, which has a poor working environment, low efficiency and highly subjective inspection results. With the development of unmanned driving, artificial intelligence, and intelligent unmanned equipment technology, it is feasible to use unmanned intelligent detection to detect cracks and defects inside bridge steel box girders.

[0003] The patent "A wall-climbing robot for internal inspection of steel box girders of long-span bridges" (publication number CN202010157815) and the patent "A steel box girder inspection method using a wall-climbing robot" (publication number CN202010159891) provide methods for using a wall-climbing robot to crawl on the surface of a steel box girder for defect detection. However, due to the influence of the U-rib partition of the steel box girder, the obstacle crossing is not flexible, the working time is short due to the weight of the battery carried, and the autonomy is difficult due to the influence of the top mechanism of the steel box girder. The inspection robot described in the patent "Method, system, device and electronic equipment for steel box girder inspection" (publication number CN202410491846) adopts the method of adding a mechanical arm to the inspection robot body, and the mechanical arm is equipped with sensing devices such as visible light cameras and infrared imaging modules to collect images of surface defects of the steel box girder. However, due to the influence of the length of the mechanical arm, the working range is small and it is difficult to detect the entire surface area of ​​the steel box girder. In addition, the mechanical arm has to change its posture frequently, so the action rhythm is slow and the work efficiency is low. The patent "A method for detecting welding of U ribs of steel box girders" (publication number CN202311040708), the patent "A method for detecting U ribs of steel box girders" (publication number CN202311041328) and the patent "A method for detecting U ribs of steel box girders" (publication number CN202311045367) use complex transverse, longitudinal, suspension, telescopic and other mechanical mechanisms to realize sensing equipment to detect internal defects of steel box girders. There are problems such as large equipment size, numerous movements, slow speed, complex structure, high failure rate, and inconvenient inspection and maintenance. Summary of the invention

[0004] The purpose of the present invention is to provide a self-propelled UAV machine nest bridge steel box girder internal detection system and method to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The present invention provides a self-propelled unmanned aerial vehicle (UAV) machine nest bridge steel box girder internal detection system, comprising a track-running chassis, wherein the track-running chassis is movably arranged on a maintenance manhole track inside the bridge steel box girder, and the track-running chassis is integrated with a laser radar subunit, an image monitoring subunit, a UAV cable-releasing and cable-receiving mechanism, a UAV platform, a communication unit, a storage unit, a computing unit, a control unit and a power supply unit, a UAV platform is parked with a UAV, the UAV is connected to the UAV cable-releasing and cable-receiving mechanism through a mooring cable, and establishes communication with the control unit, the laser radar subunit, the image monitoring subunit, the UAV cable-releasing and cable-receiving mechanism, the communication unit, the storage unit, the computing unit and the power supply unit all establish communication with the control unit, and the communication unit also establishes communication with a host computer.

[0007] Preferably, the laser radar subunit includes a forward-looking laser radar, a rear-looking laser radar and a top-looking laser radar.

[0008] Preferably, the image monitoring subunit includes a front-view camera, a rear-view camera and an optoelectronic tracking ball. The image monitoring subunit realizes tracking and monitoring of the UAV and image and video acquisition of the internal environment of the steel box girder.

[0009] Preferably, the drone is a tethered drone.

[0010] The present invention also provides a method for detecting the interior of a steel box girder of a self-propelled drone nest bridge, comprising the following steps:

[0011] S1. The self-propelled machine nest autonomously walks and positions itself on the inspection manhole track inside the steel box girder of the bridge according to the task requirements of the host computer, and stops when it reaches the middle position of the starting steel box girder required by the host computer;

[0012] S2. Use the UAV cable releasing and collecting mechanism to launch the UAV to detect and locate the fatigue cracks inside the steel box girder;

[0013] S3. After the drone completes the inspection of the current steel box girder, the drone returns to the drone platform, and the self-propelled machine nest autonomously drives to the next steel box girder for inspection;

[0014] S4. After the self-propelled crane nest completes all the steel box girder inspection tasks required by the host computer, it drives to the parking point required by the host computer.

[0015] Preferably, in step S1, the positioning method of the self-propelled machine nest is: the positioning method of the self-propelled machine nest is: use the point cloud data of the forward-looking laser radar and the rear-looking laser radar and the model point cloud of the steel box girder maintenance manhole pre-stored in the system to calculate, and obtain the position of the maintenance manhole, and obtain the position of the self-propelled machine nest inside the bridge by counting the number of maintenance manholes passed through; according to the distance between the forward-looking laser radar, the rear-looking laser radar and the maintenance manhole, the position of the self-propelled machine nest in the current steel box girder is obtained.

[0016] Preferably, the method for calculating the point cloud data of the laser radar and the model point cloud of the steel box girder maintenance manhole pre-stored in the system uses the normal distribution algorithm and the iterative closest point algorithm implemented in the open source point cloud library PCL for coarse matching and fine matching respectively.

[0017] Preferably, in step S2, the flight control method of the UAV is: the computing unit uses a top-view laser radar to detect the position and posture of the UAV in real time, and the control unit sends a flight control command to the UAV through a tether according to the position and posture of the UAV inside the steel box girder.

[0018] Preferably, in step S2, the positioning method of the UAV is: the self-propelled machine nest pre-stores the three-dimensional model point cloud of the UAV, the top-view laser radar collects the point cloud data of the steel box girder scene, and the computing unit calculates the point cloud data of the top-view laser radar with the three-dimensional model point cloud of the UAV pre-stored in the self-propelled machine nest, and obtains the three-dimensional spatial position and posture of the UAV in the steel box girder in real time.

[0019] Preferably, the method for calculating the point cloud data of the top-view laser radar and the three-dimensional model point cloud of the drone pre-stored in the self-propelled vehicle nest uses the normal distribution algorithm and the iterative closest point algorithm implemented in the open source point cloud library PCL for coarse matching and fine matching respectively.

[0020] Compared with the prior art, the present invention has achieved the following beneficial technical effects: the present invention provides a self-propelled drone nest bridge steel box girder internal detection system and method, the system consists of a track driving chassis, a laser radar subunit, an image monitoring subunit, a drone cable laying and collecting mechanism, one or more drone platforms, a communication unit, a storage unit, a computing unit, a control unit and a power supply unit; the laser radar subunit realizes the position positioning of the self-propelled nest in the bridge, and realizes the position positioning and posture calculation of the drone in the steel box girder; through the cooperation of the self-propelled nest and the tethered drone, the conditions for using the drone to detect cracks and defects inside the steel box girder are met; the image monitoring subunit realizes the tracking and monitoring of the drone and the acquisition of images and videos of the internal environment of the steel box girder. The system and method ensure the autonomous driving of the nest inside the bridge and the autonomous flight of the drone. If it can be promoted and used, it will greatly reduce the intensity of manual labor while greatly improving the efficiency of internal inspections and crack and defect detection of steel box girders, providing reliable protection for the safe operation of bridges. It is outstanding in:

[0021] (1) The use of laser radar can realize the position positioning of the self-propelled machine nest in the bridge and the position positioning and attitude calculation of the drone in the steel box girder, solving the positioning problem of the machine nest and the drone when there is no GPS signal inside the steel box girder. The positioning and attitude determination are accurate and not affected by the lighting conditions inside the steel box girder, realizing the precise navigation of the autonomous driving of the machine nest inside the bridge and the autonomous flight of the drone.

[0022] (2) The use of tethered drones and a machine nest to power the drones solves the problem that drones cannot work for a long time when the bridge is very long. The working time of the drones meets the requirements for large bridge inspections.

[0023] (3) The use of drones to detect cracks and defects inside the steel box girder of the bridge fully utilizes the fact that there is no wind inside the steel box girder, the environment is relatively spacious, the width of the steel box girder is very small, the mooring rope is short, and the total weight of the mooring rope is light. The drone is precisely controlled, and the control accuracy meets the requirements for crack and defect detection in the steel box girder.

[0024] (4) The UAV can be controlled with multiple degrees of freedom. The UAV has a large working range and a variable detection field of view, which can realize blind spot detection inside the steel box girder. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1A schematic diagram of a control method for an internal detection system for a steel box girder of a self-propelled drone nest bridge provided by the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The purpose of the present invention is to provide a self-propelled UAV machine nest bridge steel box girder internal detection system and method to solve the problems existing in the prior art.

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] Embodiment 1:

[0031] This embodiment provides a self-propelled drone nest bridge steel box girder internal detection system, such as Figure 1 As shown, it includes a track-running chassis 1, which is movably arranged on the maintenance manhole track inside the steel box girder of the bridge. The track-running chassis 1 is integrated with a laser radar subunit 2, an image monitoring subunit 3, an unmanned aerial vehicle platform 4, a unmanned aerial vehicle cable-releasing and cable-receiving mechanism 5, a communication unit 6, a storage unit 7, a computing unit 8, a control unit 9 and a power supply unit 10. A drone 11 is parked on the unmanned aerial vehicle platform, and the drone 11 is connected to the unmanned aerial vehicle cable-releasing and cable-receiving mechanism 5 through a mooring cable, and establishes communication with the control unit 9. The laser radar subunit 2, the image monitoring subunit 3, the unmanned aerial vehicle cable-releasing and cable-receiving mechanism 5, the communication unit 6, the storage unit 7, the computing unit 8 and the power supply unit 10 all establish communication with the control unit 9. The computing unit 8 and the control unit 9 adopt the Tianjue GEAC90L controller, and the communication unit 6 also establishes communication with the host computer 12.

[0032] As an implementation mode, the laser radar subunit 2 includes a forward-looking laser radar, a rear-looking laser radar and two top-looking laser radars, wherein the forward-looking laser radar and the rear-looking laser radar both adopt the Sagitar E1 series laser radar, the No. 1 top-looking laser radar adopts the DJI mid-360 series laser radar, and the No. 2 top-looking laser radar adopts the DJI mid-70 series laser radar.

[0033] As an implementation method, the image monitoring subunit 3 includes a front view camera, a rear view camera and a photoelectric tracking ball. The front view camera and the rear view camera adopt the GMSL camera SG2-IMX390C-5200-G2A of Senyun Intelligence.

[0034] As an implementation mode, the drone 11 is a cage-type drone. The self-propelled drone nest supplies power and sends control instructions to the cage-type drone through a tethered rope, and receives the perception data of the cage-type drone on the steel box girder.

[0035] This embodiment also provides a method for detecting the interior of a steel box girder of a self-propelled drone nest bridge, comprising the following steps:

[0036] S1. The self-propelled machine nest autonomously walks and positions itself on the inspection manhole track inside the steel box girder of the bridge according to the task requirements of the host computer, and stops when it reaches the middle position of the starting steel box girder required by the host computer;

[0037] Among them, the positioning method of the self-propelled machine nest is as follows: the positioning method of the self-propelled machine nest is as follows: the point cloud data of the forward-looking laser radar and the rear-looking laser radar are used to calculate with the model point cloud of the steel box girder maintenance manhole pre-stored in the system to obtain the position of the maintenance manhole, and the position of the self-propelled machine nest inside the bridge is obtained by counting the number of maintenance manholes passed; the position of the self-propelled machine nest in the current steel box girder is obtained according to the distance between the forward-looking laser radar, the rear-looking laser radar and the maintenance manhole;

[0038] The method for calculating the point cloud data of the laser radar and the model point cloud of the steel box girder maintenance manhole pre-stored in the system uses the normal distribution algorithm and iterative closest point algorithm implemented in the open source point cloud library PCL for coarse matching and fine matching respectively.

[0039] S2. Use the UAV cable releasing and collecting mechanism to launch the UAV to detect and locate the fatigue cracks inside the steel box girder;

[0040] The flight control method of the UAV is as follows: the computing unit uses the top-view laser radar to detect the position and attitude of the UAV in real time, and the control unit sends a flight control command to the UAV through the tether according to the position and attitude of the UAV inside the steel box girder;

[0041] The positioning method of the UAV is as follows: the self-propelled machine nest stores the three-dimensional model point cloud of the UAV in advance, the top-view laser radar collects the point cloud data of the steel box girder scene, and the calculation unit calculates the point cloud data of the top-view laser radar and the three-dimensional model point cloud of the UAV stored in the self-propelled machine nest, and obtains the three-dimensional spatial position and posture of the UAV in the steel box girder in real time;

[0042] The method for calculating the point cloud data of the top-view lidar and the three-dimensional model point cloud of the pre-stored UAV in the self-propelled vehicle nest uses the normal distribution algorithm and iterative closest point algorithm implemented in the open source point cloud library PCL for coarse matching and fine matching respectively.

[0043] S3. After the drone completes the inspection of the current steel box girder, the drone returns to the drone platform, and the self-propelled machine nest autonomously drives to the next steel box girder for inspection;

[0044] S4. After the self-propelled crane nest completes all the steel box girder inspection tasks required by the host computer, it drives to the parking point required by the host computer.

[0045] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A self-propelled drone nest bridge steel box girder internal inspection system, characterized by: It includes a track-running chassis, which is movably arranged on the maintenance manhole track inside the steel box girder of the bridge. The track-running chassis is integrated with a laser radar subunit, an image monitoring subunit, a UAV cable-releasing and cable-receiving mechanism, a UAV platform, a communication unit, a storage unit, a computing unit, a control unit and a power supply unit. A UAV is parked on the UAV platform, and the UAV is connected to the UAV cable-releasing and cable-receiving mechanism through a mooring cable, and establishes communication with the control unit. The laser radar subunit, the image monitoring subunit, the UAV cable-releasing and cable-receiving mechanism, the communication unit, the storage unit, the computing unit and the power supply unit all establish communication with the control unit, and the communication unit also establishes communication with a host computer.

2. The self-propelled drone nest bridge steel box girder internal detection system according to claim 1 is characterized by: The laser radar subunit includes a forward-looking laser radar, a rear-looking laser radar and a top-looking laser radar.

3. The self-propelled drone nest bridge steel box girder internal detection system according to claim 1 is characterized by: The image monitoring subunit includes a front-view camera, a rear-view camera and an optoelectronic tracking ball. The image monitoring subunit realizes tracking and monitoring of the UAV and image and video acquisition of the internal environment of the steel box girder.

4. The self-propelled drone nest bridge steel box girder internal detection system according to claim 1 is characterized by: The drone is a tethered drone.

5. A self-propelled drone nest bridge steel box girder internal inspection method, characterized in that: The following steps are involved: S1. The self-propelled machine nest autonomously walks and positions itself on the inspection manhole track inside the steel box girder of the bridge according to the task requirements of the host computer, and stops when it reaches the middle position of the starting steel box girder required by the host computer; S2. Use the UAV cable releasing and collecting mechanism to launch the UAV to detect and locate the fatigue cracks inside the steel box girder; S3. After the drone completes the inspection of the current steel box girder, the drone returns to the drone platform, and the self-propelled machine nest autonomously drives to the next steel box girder for inspection; S4. After the self-propelled crane nest completes all the steel box girder inspection tasks required by the host computer, it drives to the parking point required by the host computer.

6. The method for detecting the interior of a steel box girder of a self-propelled drone nest bridge according to claim 5 is characterized in that: In step S1, the positioning method of the self-propelled machine nest is: use the point cloud data of the forward-looking laser radar and the rear-looking laser radar and the model point cloud of the steel box girder maintenance manhole pre-stored in the system to calculate, get the position of the maintenance manhole, and get the position of the self-propelled machine nest inside the bridge by counting the number of maintenance manholes passed; get the position of the self-propelled machine nest inside the current steel box girder according to the distance between the forward-looking laser radar, the rear-looking laser radar and the maintenance manhole.

7. The method for detecting the interior of a steel box girder of a self-propelled drone nest bridge according to claim 5 is characterized in that: In step S2, the flight control method of the UAV is as follows: the computing unit uses a top-view laser radar to detect the position and posture of the UAV in real time, and the control unit sends a flight control command to the UAV through a tether according to the position and posture of the UAV inside the steel box girder.

8. The method for detecting the interior of a steel box girder of a self-propelled drone nest bridge according to claim 7 is characterized in that: In step S2, the positioning method of the UAV is as follows: the self-propelled machine nest pre-stores the three-dimensional model point cloud of the UAV, the top-view laser radar collects the point cloud data of the steel box girder scene, and the computing unit detects the three-dimensional spatial position and posture of the UAV in the steel box girder in real time in the point cloud data of the top-view laser radar.

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