CN113791074A - Unmanned aerial vehicle bridge crack inspection system and method based on multi-sensor fusion - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle bridge crack inspection system and method based on multi-sensor fusion.A positioning module is used for acquiring position information of an unmanned aerial vehicle, an inertial navigation module is used for acquiring state information of the unmanned aerial vehicle, a radar module is used for acquiring obstacle information, an information fusion module is connected with the positioning module, the inertial navigation module and the radar module and is used for fusing received information, and a central control module receives the fusion information and generates an obstacle avoidance and path planning strategy; the crack information acquisition module is used for acquiring crack information and sending the crack information to the edge calculation module, the edge calculation module processes, calculates and analyzes the crack information, and the analysis result is sent to the terminal through the data transmission module. The intelligent crack acquisition and detection is realized through the edge calculation module and the crack information acquisition module, and the real-time crack identification and the quantitative analysis and monitoring of the crack width and length information are realized.
Description
Technical Field
The invention relates to the technical field of bridge crack inspection, in particular to an unmanned aerial vehicle bridge crack inspection system and method based on multi-sensor fusion.
Background
At present, when the traditional manual detection bridge cracks, auxiliary equipment such as a bridge detection support and a special detection vehicle is generally used, and the small-sized crack width measuring instrument, a steel ruler, a camera and other tools are matched to manually observe and measure crack distribution characteristics including information such as length and width at a short distance. However, the manual detection is greatly influenced by environment and severe conditions, and has the disadvantages of high consumption, high risk, low accuracy, low efficiency and the like, and thus cannot meet the requirements of the increasing development. In order to better and more effectively patrol the bridge, the unmanned aerial vehicle bridge crack patrol system based on multi-sensor fusion is provided and designed, and the system has the advantages of automation, convenience, quantification and accuracy, so that the detection and extraction of cracks are more objective and reliable, and the form and parameters of the cracks can be accurately recorded.
Therefore, reducing overhead operation and improving efficiency, and realizing automatic inspection of bridge structure cracks and real-time identification and monitoring of diseases are problems which need to be solved by technical personnel in the field.
Disclosure of Invention
In view of the above, the invention provides an unmanned aerial vehicle bridge crack inspection system and method based on multi-sensor fusion, which generate an unmanned aerial vehicle control strategy through a positioning module, an inertial navigation module, a radar module, an information fusion module and a central control module, realize intelligent unmanned aerial vehicle inspection, realize intelligent crack acquisition and detection through an edge calculation module and a crack information acquisition module, and realize real-time crack identification and quantitative analysis and monitoring of crack width and length information.
In order to achieve the purpose, the invention adopts the following technical scheme:
the utility model provides an unmanned aerial vehicle bridge crack system of patrolling and examining based on multisensor fuses, includes: the system comprises an unmanned aerial vehicle, a positioning module, an inertial navigation module, a radar module, an information fusion module, a central control module, an edge calculation module, a crack information acquisition module, a data transmission module and a terminal;
the positioning module is used for acquiring position information of the unmanned aerial vehicle, the inertial navigation module is used for acquiring state information of the unmanned aerial vehicle, the radar module is used for acquiring obstacle information, the information fusion module is connected with the positioning module, the inertial navigation module and the radar module and is used for fusing the received information, and the central control module receives the fusion information and generates an obstacle avoidance and path planning strategy;
the crack information acquisition module is used for acquiring crack information and sending the crack information to the edge calculation module, the edge calculation module processes, calculates and analyzes the crack information, and an analysis result is sent to the terminal through the data transmission module.
Preferably, the positioning module comprises a real-time differential positioning module and an ultra-bandwidth module; the radar module comprises a laser radar and a millimeter wave radar; the crack information acquisition module comprises a camera module and a laser ranging module.
Preferably, the position information includes longitude, latitude, altitude, speed information and three-dimensional coordinate information of the unmanned aerial vehicle; the state information includes acceleration and attitude angle of the unmanned aerial vehicle.
An unmanned aerial vehicle bridge crack inspection method based on multi-sensor fusion comprises the following steps:
s1, establishing a bridge three-dimensional model, and planning an unmanned aerial vehicle routing inspection route;
s2, acquiring unmanned aerial vehicle position information, state information and obstacle information;
s3, preprocessing and fusing the information to generate a control strategy;
s4, the unmanned aerial vehicle collects crack information according to the control strategy, and the crack information is processed through the crack detection model to obtain a detection result.
Preferably, the step S2 specifically includes: the positioning module acquires the position information of the unmanned aerial vehicle, the inertial navigation module acquires the state information of the unmanned aerial vehicle, and the radar module acquires the obstacle information.
Preferably, the step S3 specifically includes:
s31, acquiring real-time differential positioning information and ultra-wideband positioning information, preprocessing the real-time differential positioning information and the ultra-wideband positioning information, judging whether the positioning of the real-time differential positioning module is a floating point solution, if so, outputting the real-time differential positioning information, and if not, outputting the ultra-wideband positioning information;
s32, fusing unmanned aerial vehicle position information, state information and obstacle information. D1、D2And the distance information of the unmanned aerial vehicle and the obstacle is measured and output by the millimeter wave radar and the laser radar. Combining unmanned aerial vehicle attitude angle information measured by the inertial navigation module with preprocessed differential positioning module and unmanned aerial vehicle position information measured by the ultra-wide module, and calculating the nearest distance information between the unmanned aerial vehicle and the barrier to be D through the position attitude angle of the unmanned aerial vehicle and the three-dimensional coordinate of the surrounding environment space3，
Wherein (x)1,y1,z1) Unmanned aerial vehicle spatial position coordinates (x) for inertial navigation and positioning module positioning2,y2,z2)…(xn,yn,zn) Is a three-dimensional coordinate of the space environment.
The three are weighted and averaged to finally calculate the distance Dis between the unmanned aerial vehicle and the surrounding environment,
where n is ∈ {1,2,3}, k1,k2,k3Is not less than 0, and k1+k2+k3＝1；
And S33, comparing the distance value Dis with the safety distance, performing decision analysis, and generating a control strategy.
According to the technical scheme, compared with the prior art, the invention discloses an unmanned aerial vehicle bridge crack inspection system and method based on multi-sensor fusion.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic diagram of a system structure provided by the invention.
FIG. 2 is a schematic flow chart of the method provided by the present invention.
FIG. 3 is a schematic diagram of multi-sensor data fusion provided by the present invention.
Wherein, 1 is orientation module, 2 is inertial navigation module, 3 is the radar module, 4 is information fusion module, 5 is the center control module, 6 is marginal calculation module, 7 is crack information acquisition module, 8 is the data transmission module, 9 is the terminal, 11 is real-time difference orientation module, 12 is super bandwidth module, 31 is laser radar, 32 is the millimeter wave radar, 71 is camera module, 72 is laser range module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses an unmanned aerial vehicle bridge crack inspection system based on multi-sensor fusion, which comprises: the system comprises an unmanned aerial vehicle, a positioning module 1, an inertial navigation module 2, a radar module 3, an information fusion module 4, a central control module 5, an edge calculation module 6, a crack information acquisition module 7, a data transmission module 8 and a terminal 9;
the positioning module 1 is used for acquiring position information of the unmanned aerial vehicle, the inertial navigation module 2 is used for acquiring state information of the unmanned aerial vehicle, the radar module 3 is used for acquiring obstacle information, the information fusion module 4 is connected with the positioning module 1, the inertial navigation module 2 and the radar module 3 and is used for fusing the received information, and the central control module 5 is used for receiving the fusion information and generating an obstacle avoidance and path planning strategy;
the crack information acquisition module 7 is used for acquiring crack information and sending the crack information to the edge calculation module 6, the edge calculation module 6 processes, calculates and analyzes the crack information, and the analysis result is sent to the terminal 9 through the data transmission module 8.
In order to further optimize the technical scheme, the positioning module 1 comprises a real-time differential positioning module 11 and an ultra-bandwidth module 12; the radar module 3 includes a laser radar 31 and a millimeter wave radar 32; the crack information acquisition module 7 includes a camera module 71 and a laser ranging module 72.
In order to further optimize the technical scheme, the position information comprises longitude, latitude, altitude, speed information and three-dimensional coordinate information of the unmanned aerial vehicle; the state information includes the acceleration and attitude angle of the unmanned aerial vehicle.
An unmanned aerial vehicle bridge crack inspection method based on multi-sensor fusion comprises the following steps:
s1, establishing a bridge three-dimensional model, and planning an unmanned aerial vehicle routing inspection route;
s2, acquiring unmanned aerial vehicle position information, state information and obstacle information;
s3, preprocessing and fusing the information to generate a control strategy;
s4, the unmanned aerial vehicle collects crack information according to a control strategy, and the crack information is processed through a crack detection model to obtain a detection result; a large number of positive and negative sample models of a convolutional neural network are trained, and the trained models are placed in embedded hardware equipment with calculation power to realize real-time detection and identification of cracks.
To further optimize the above technical solution, step S2 specifically includes: the position information of the unmanned aerial vehicle is acquired through the positioning module, the state information of the unmanned aerial vehicle is acquired through the inertial navigation module, and the obstacle information is acquired through the radar module.
To further optimize the above technical solution, step S3 specifically includes:
s31, acquiring real-time differential positioning information and ultra-wideband positioning information, preprocessing the real-time differential positioning information and the ultra-wideband positioning information, judging whether the positioning of the real-time differential positioning module is a floating point solution, if so, outputting the real-time differential positioning information, and if not, outputting the ultra-wideband positioning information;
s32, fusing unmanned aerial vehicle position information, state information and obstacle information. D1、D2And the distance information of the unmanned aerial vehicle and the obstacle is measured and output by the millimeter wave radar and the laser radar. Combining unmanned aerial vehicle attitude angle information measured by the inertial navigation module with preprocessed differential positioning module and unmanned aerial vehicle position information measured by the ultra-wide module, and calculating the nearest distance information between the unmanned aerial vehicle and the barrier to be D through the position attitude angle of the unmanned aerial vehicle and the three-dimensional coordinate of the surrounding environment space3，
Wherein (x)1,y1,z1) Unmanned aerial vehicle spatial position coordinates (x) for inertial navigation and positioning module positioning2,y2,z2)…(xn,yn,zn) Is a three-dimensional coordinate of the space environment.
The three are weighted and averaged to finally calculate the distance Dis between the unmanned aerial vehicle and the surrounding environment,
where n is ∈ {1,2,3}, k1,k2,k3Is not less than 0, and k1+k2+k3＝1；
And S33, comparing the distance value Dis with the safety distance, performing decision analysis, and generating a control strategy.
And establishing a three-dimensional model of the bridge space by manually remotely controlling the unmanned aerial vehicle inspection system through the ground station to obtain a high-precision three-dimensional map. According to the task requirement of routing inspection, a routing inspection route of the unmanned aerial vehicle is preset through a ground control station; open RTK module, super bandwidth UWB module, IMU, lidar, millimeter wave radar, RTK module, super bandwidth UWB module, IMU, lidar, millimeter wave radar can receive perception information in real time to send and merge the module for the multisensor, the multisensor merges the module and carries out the preliminary treatment of some information at first, carries out the multisensor and fuses afterwards, as shown in FIG. 3, specific information fusion step is as follows:
the RTK information and the UWB information are fused, the RTK precision is generally horizontal +/-1 cm, the high-level +/-2 cm, the UWB positioning precision is generally +/-10 cm, and the fusion strategy is to switch between the two output results according to whether the RTK output is a floating solution or not. When the occlusion is serious, UWB positioning data is selected, and seamless connection of the positioning data can be realized; the unmanned aerial vehicle attitude angle information measured by the inertial navigation module IMU is combined with the preprocessed differential positioning module and the unmanned aerial vehicle position information measured by the ultra-wideband module, the nearest distance information between the unmanned aerial vehicle and the barrier is calculated through the position attitude angle of the unmanned aerial vehicle and the three-dimensional coordinate of the surrounding environment space, and finally the laser radar, the millimeter wave radar, the positioning module and the inertial navigation module are fused. The scanning range of the laser radar is 30-40 m, and the distance measurement error is +/-25 mm; the scanning range of the millimeter radar is within 30 meters, and the distance error is plus or minus 10 mm. The fusion mode adopts a weighted average algorithm, a fusion system kn of each sensor, wherein n belongs to {1,2,3}, and the distance Dis between the unmanned aerial vehicle and the surrounding environment is finally calculated as follows:
wherein k is1,k2,k3Is not less than 0, and k1+k2+k3＝1；
The central control unit carries out analysis and decision through comparison between the barrier distance calculated by the multi-sensor fusion module and a preset safety distance, and finally sends a control strategy to the flight control module to control the unmanned aerial vehicle to carry out obstacle avoidance operation; the edge calculation module is internally provided with a trained crack detection model, so that real-time detection of the bridge structure cracks collected by the holder camera can be realized, and a detection result can be sent to a server through the data transmission module or displayed on a ground control station in real time through the data transmission module; the server runs crack identification, extraction, calculation, splicing and display programs, combines crack images, identification targets and laser ranging information acquired by a cloud deck camera to realize quantitative analysis and monitoring of the length and the width of the crack, and finally displays the crack inspection result by matching a three-dimensional or two-dimensional expression mode with quantitative data.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. The utility model provides an unmanned aerial vehicle bridge crack system of patrolling and examining based on multisensor fuses which characterized in that includes: the system comprises an unmanned aerial vehicle, a positioning module (1), an inertial navigation module (2), a radar module (3), an information fusion module (4), a central control module (5), an edge calculation module (6), a crack information acquisition module (7), a data transmission module (8) and a terminal (9);
the positioning module (1) is used for acquiring position information of the unmanned aerial vehicle, the inertial navigation module (2) is used for acquiring state information of the unmanned aerial vehicle, the radar module (3) is used for acquiring obstacle information, the information fusion module (4) is connected with the positioning module (1), the inertial navigation module (2) and the radar module (3) and is used for fusing received information, and the central control module (5) receives the fused information and generates an obstacle avoidance and path planning strategy;
the crack information acquisition module (7) is used for acquiring crack information and sending the crack information to the edge calculation module (6), the edge calculation module (6) processes, calculates and analyzes the crack information, and an analysis result is sent to the terminal (9) through the data transmission module (8).
2. The unmanned aerial vehicle bridge crack inspection system based on multi-sensor fusion of claim 1, wherein the positioning module (1) comprises a real-time differential positioning module (11) and an ultra-bandwidth module (12); the radar module (3) comprises a laser radar (31) and a millimeter wave radar (32); the crack information acquisition module (7) comprises a camera module (71) and a laser ranging module (72).
3. The unmanned aerial vehicle bridge crack inspection system based on multi-sensor fusion of claim 1, wherein the position information comprises longitude, latitude, altitude, speed information and three-dimensional coordinate information of the unmanned aerial vehicle; the state information includes acceleration and attitude angle of the unmanned aerial vehicle.
4. The unmanned aerial vehicle bridge crack inspection method based on multi-sensor fusion is characterized by comprising the following steps of:
s1, establishing a bridge three-dimensional model, and planning an unmanned aerial vehicle routing inspection route;
s2, acquiring unmanned aerial vehicle position information, state information and obstacle information;
s3, preprocessing and fusing the information to generate a control strategy;
s4, the unmanned aerial vehicle collects crack information according to the control strategy, and the crack information is processed through the crack detection model to obtain a detection result.
5. The unmanned aerial vehicle bridge crack inspection method based on multi-sensor fusion of claim 4, wherein the step S2 specifically comprises: the positioning module acquires the position information of the unmanned aerial vehicle, the inertial navigation module acquires the state information of the unmanned aerial vehicle, and the radar module acquires the obstacle information.
6. The unmanned aerial vehicle bridge crack inspection method based on multi-sensor fusion of claim 4, wherein the step S3 specifically comprises:
s31, acquiring real-time differential positioning information and ultra-wideband positioning information, preprocessing the real-time differential positioning information and the ultra-wideband positioning information, judging whether the positioning of the real-time differential positioning module is a floating point solution, if so, outputting the real-time differential positioning information, and if not, outputting the ultra-wideband positioning information;
s32, fusing unmanned aerial vehicle position information, state information and obstacle information, D1、D2The distance information of the unmanned aerial vehicle and the obstacle, which is output for the millimeter wave radar and the laser radar, is measured, the attitude angle information of the unmanned aerial vehicle, which is measured by the inertial navigation module, is combined with the preprocessed differential positioning module and the unmanned aerial vehicle position information, which is measured by the ultra-wideband module, and the nearest distance information of the unmanned aerial vehicle and the obstacle is calculated to be D through the position attitude angle of the unmanned aerial vehicle and the three-dimensional coordinate of the surrounding environment space3，
Wherein (x)1,y1,z1) Unmanned aerial vehicle spatial position coordinates (x) for inertial navigation and positioning module positioning2,y2,z2)…(xn,yn,zn) Three-dimensional coordinates of a space environment;
the three are weighted and averaged to finally calculate the distance Dis between the unmanned aerial vehicle and the surrounding environment,
where n is ∈ {1,2,3}, k1,k2,k3Is not less than 0, and k1+k2+k3＝1；
And S33, comparing the distance value Dis with the safety distance, performing decision analysis, and generating a control strategy.
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