CN115793682A - Bridge intelligent inspection method and inspection system based on unmanned aerial vehicle - Google Patents
Bridge intelligent inspection method and inspection system based on unmanned aerial vehicle Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent bridge inspection methods and equipment, in particular to an intelligent bridge inspection method and an intelligent bridge inspection system based on an unmanned aerial vehicle. Compared with the prior art, the invention has the advantages that: load unmanned aerial vehicle and unmanned aerial vehicle hangar on the pick up car, the automatic battery of changing of in-process, need not the human intervention of measurement personnel, unmanned aerial vehicle accomplishes the automatic unmanned aerial vehicle hangar that falls after the task, measurement personnel drives pick up the pick up car and carries out the detection task to next bridge department, accomplish field work collection work back, data are automatic to be uploaded to cloud platform processing center, automatic modeling, disease automatic identification, the automatic generation detects the report, the maximize reduces field work and interior field work in-process measurement personnel's intervention, it is automatic to improve the detection, digitization and visual level.
Description
Technical Field
The invention relates to the technical field of intelligent bridge inspection methods and equipment, in particular to an intelligent bridge inspection method and an intelligent bridge inspection system based on an unmanned aerial vehicle.
Background
Compared with a traditional bridge detection mode recorded by an artificial paper pen, the bridge detection by adopting the unmanned aerial vehicle has the advantages of no traffic interruption, convenience for high-altitude operation, strong repeatability, high digitization degree and the like. At present, with the maturity of computer vision and image processing technology and the popularization of commercial unmanned aerial vehicles and the reduction of cost, bridge detection based on unmanned aerial vehicles is increasingly tried and applied in the field of engineering detection. At the present stage, bridge detection based on unmanned aerial vehicles mainly comprises that detection personnel or flying hands manually control an airplane to a specified position to shoot a bridge photo.
However, there are many problems in the actual detection process. On one hand, due to the limitation of the cruising ability of the unmanned aerial vehicle, the battery needs to be replaced manually and frequently, so that the on-site image acquisition efficiency of the unmanned aerial vehicle is greatly reduced; on the other hand, the bridge detection site environment is complex, so that the flight experience of the unmanned aerial vehicle for detection personnel or a flyer has higher requirements, and the application and popularization of the unmanned aerial vehicle in the field of bridge detection are greatly limited. A method and a system for automatically and intelligently inspecting a bridge are urgently needed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a bridge automation and intelligent inspection method based on vehicle-mounted unmanned aerial vehicle equipment.
In order to achieve the purpose, the bridge intelligent inspection method based on the unmanned aerial vehicle is designed, comprises a current flight attitude module, a hovering module, a multi-sensing fusion obstacle avoidance reminding module, an emergency obstacle avoidance module, a positioning and attitude recording module and an anti-falling module, and further comprises the following specific steps:
s1, when an unmanned aerial vehicle is about to encounter an obstacle, a multi-sensing fusion obstacle avoidance reminding module sends out alarm information, and an emergency obstacle avoidance module enables the unmanned aerial vehicle to stop suddenly and fly around the obstacle, so that the unmanned aerial vehicle is prevented from colliding with the obstacle;
s11, when the unmanned aerial vehicle meets the obstacle in the forward flight process and reminds, the flight attitude is adjusted to rotate to the right by 90 degrees, and the unmanned aerial vehicle continues to fly forward: if the obstacle avoidance early warning is removed, the flight attitude is adjusted again on the basis of the original attitude to turn left by 90 degrees to continue to recover the normal state to execute the flight task; if the obstacle avoidance early warning is kept and the distance of the obstacle is reduced, the flight attitude is adjusted to turn right by 90 degrees again, and the aircraft flies forwards until the obstacle avoidance early warning is relieved and the normal forward flight mode is recovered;
s12, when the unmanned aerial vehicle is reminded of meeting obstacles in the downward flight process, adjusting the flight attitude to fly forwards: if the obstacle avoidance early warning is removed, the flight attitude is adjusted to continue flying downwards; if the obstacle avoidance early warning is still kept, the flight attitude is adjusted to fly upwards until the warning is removed, after the warning is removed, the flight attitude is adjusted to fly forwards for 0.5m, and the flight attitude is continuously adjusted to be in a normal downward flight mode;
s13, when the unmanned aerial vehicle meets the obstacle in the upward flying process and is reminded, adjusting the flying posture to fly forwards: if the obstacle avoidance early warning is removed, the flight attitude is adjusted to continue flying upwards; if the obstacle avoidance early warning is still kept, the flight attitude is adjusted to be in downward flight until the warning is removed, and after the warning is removed and the flight attitude is adjusted to be in forward flight for 0.5m, the flight attitude is continuously adjusted to be in a normal upward flight mode;
s2, when the unmanned aerial vehicle collides, the motor of the unmanned aerial vehicle stops rotating and freely falls, the falling motion characteristic of the unmanned aerial vehicle is identified by the falling prevention module, and a parachute installed on the unmanned aerial vehicle is automatically started and opened to enable the unmanned aerial vehicle to land at a lower speed;
s3, when the electric quantity of the unmanned aerial vehicle is insufficient, the positioning and attitude recording module records the current position, the unmanned aerial vehicle starts a return flight mode and falls into a cabin of the unmanned aerial vehicle to replace a battery, and after the battery is replaced successfully, the unmanned aerial vehicle takes off to the recorded positioning position and continues to execute a flight task;
s4, after the unmanned aerial vehicle finishes the flight task, the unmanned aerial vehicle automatically descends to an unmanned aerial vehicle hangar, the vehicle-mounted unmanned aerial vehicle system runs to the next bridge to be detected to execute the flight task, after the unmanned aerial vehicle flight task is finished, data are uploaded to a database system, the cloud platform processing center processes the data, and bridge inspection digital delivery is carried out on processing results.
Preferably, the system further comprises a preset patrol route, wherein the patrol route comprises a snake route planning mode, a vertical sawtooth course mode, a step route mode and a surrounding route mode.
Preferably, the multi-sensing fusion obstacle avoidance reminding module in the step S1 is preset with a distance threshold, and when the distance between the unmanned aerial vehicle and the obstacle is smaller than the set threshold, an obstacle avoidance early warning is automatically sent out.
Preferably, the cloud platform processing center in the step S4 receives the unmanned aerial vehicle bridge detection data, automatically performs basic processing and photography modeling on the data after transmission is completed, performs bridge appearance disease recognition on the image data by adopting a deep learning algorithm, automatically highlights diseases and standardizes the diseases on a model, and performs correlation between the pictures and the model by a picture and model matching algorithm to obtain detailed characteristics and size information of the diseases.
The invention also comprises a routing inspection system adopting the method, which comprises a vehicle-mounted unmanned aerial vehicle system, a 4G/5G communication system, a flight control system, a route planning system and a database system, wherein the flight control system comprises a multi-sensing fusion obstacle avoidance reminding module, and the multi-sensing fusion obstacle avoidance reminding module comprises a laser radar sensor and a visual sensor and is used for synchronously positioning the surrounding environment of the unmanned aerial vehicle and reconstructing a map; the air route planning system is preset with a snakelike air route mode, a vertical sawtooth course mode, a step air route mode and a surrounding air route mode, and when the unmanned aerial vehicle is located at different bridge detection positions, the inspection air route mode is automatically selected.
Compared with the prior art, the invention has the advantages that: load unmanned aerial vehicle and unmanned aerial vehicle hangar on the pick up car, it drives the pick up car to waiting to detect bridge department to detect the technical staff to detect, set up and patrol and examine the task, unmanned aerial vehicle is automatic patrols and examines the acquisition data, the automatic battery of changing of in-process, need not the human intervention of measurement personnel, unmanned aerial vehicle accomplishes the automatic landing of unmanned aerial vehicle hangar after the task, measurement personnel drives the pick up car and carries out the detection task to next bridge department, accomplish field work collection work after, data is automatic to be uploaded to cloud platform processing center, automatic modeling, disease automatic identification, the automatic generation detects the report, the maximize reduces field work and interior work in-process measurement personnel's intervention, improve and detect automaticly, digitization and visual level.
Drawings
FIG. 1 is a structural diagram of an intelligent inspection system of a vehicle-mounted unmanned aerial vehicle according to the invention;
FIG. 2 is a block diagram of the unmanned aerial vehicle flight control system of the present invention;
FIG. 3 is a diagram of the unmanned aerial vehicle airline planning system of the present invention;
FIG. 4 is a control schematic diagram of a multi-sensor fusion obstacle avoidance reminding module according to the present invention;
FIG. 5 is a flow chart of the intelligent inspection work of the vehicle-mounted unmanned aerial vehicle of the present invention;
FIG. 6 is a schematic view of a serpentine pattern of the present invention;
FIG. 7 is a schematic view of a vertical sawtooth pattern of the present invention;
FIG. 8 is a schematic view of a wrap-around route of the present invention;
FIG. 9 is a schematic view of the stair flight path of the present invention.
Detailed Description
Example 1
Referring to fig. 4 and 5, an intelligent bridge inspection method based on an unmanned aerial vehicle includes the following steps:
s1, a vehicle-mounted unmanned aerial vehicle system runs to a proper position of a bridge to be detected, an inspection system is started, a flight task is formulated according to the condition that the unmanned aerial vehicle is located at different bridge detection positions, and an inspection route is planned;
s2, the unmanned aerial vehicle inspection system controls the unmanned aerial vehicle to take off from an unmanned aerial vehicle hangar to a monitoring point, and inspection is carried out according to a path planned by a route;
s3, in a normal state, a constant-speed cruise mode is adopted for polling, a current flight attitude module, a hovering module, a multi-sensing fusion obstacle avoidance reminding module, an emergency obstacle avoidance module, a positioning and attitude recording module and an anti-falling module are kept working simultaneously, and when an abnormal module occurs, a corresponding module is started to make a response measure;
s4, when the unmanned aerial vehicle is about to encounter an obstacle, the multi-sensing fusion obstacle avoidance reminding module sends out alarm information, the emergency obstacle avoidance module enables the unmanned aerial vehicle to stop suddenly and fly around the obstacle, and the unmanned aerial vehicle is prevented from colliding with the obstacle;
s41, when the unmanned aerial vehicle is reminded of meeting obstacles in the forward flight process, adjusting the flight attitude to turn right by 90 degrees, and continuing to fly forward: if the obstacle avoidance early warning is removed, the flight attitude is adjusted again on the basis of the original attitude to turn left by 90 degrees to continue to recover to the normal state to execute the flight task; if the obstacle avoidance early warning is kept and the distance between obstacles is reduced, the flight attitude is adjusted to rotate 90 degrees to the right again, and the aircraft flies forwards until the obstacle avoidance early warning is removed and the normal forward flight mode is recovered;
s42, when the unmanned aerial vehicle is reminded of meeting obstacles in the downward flight process, adjusting the flight attitude to fly forwards: if the obstacle avoidance early warning is removed, the flight attitude is adjusted to continue flying downwards; if the obstacle avoidance early warning is still kept, the flight attitude is adjusted to fly upwards until the warning is removed, after the warning is removed, the flight attitude is adjusted to fly forwards for 0.5m, and the flight attitude is continuously adjusted to be in a normal downward flight mode;
s43, when the unmanned aerial vehicle meets an obstacle in the upward flying process and is reminded, adjusting the flying posture to fly forwards: if the obstacle avoidance early warning is removed, the flight attitude is adjusted to continue flying upwards; if the obstacle avoidance early warning is still kept, the flight attitude is adjusted to fly downwards until the warning is removed, and after the warning is removed, the flight attitude is adjusted to fly forwards for 0.5m, and the flight attitude is continuously adjusted to be in a normal upward flight mode;
s5, when the unmanned aerial vehicle collides, the motor of the unmanned aerial vehicle stops rotating and freely falls, the falling motion characteristic of the unmanned aerial vehicle is identified by the falling prevention module, and a parachute installed on the unmanned aerial vehicle is automatically started and opened to enable the unmanned aerial vehicle to land at a lower speed;
s6, when the electric quantity of the unmanned aerial vehicle is insufficient, the positioning and attitude recording module records the current position, the unmanned aerial vehicle starts a return flight mode and falls into a cabin of the unmanned aerial vehicle to replace a battery, and after the battery is replaced successfully, the unmanned aerial vehicle takes off to the recorded positioning position and continues to execute a flight task;
s7, after the unmanned aerial vehicle completes the flight task, the unmanned aerial vehicle automatically descends to an unmanned aerial vehicle hangar, the vehicle-mounted unmanned aerial vehicle system runs to the next bridge to be detected to execute the flight task, after the unmanned aerial vehicle flight task is finished, data are uploaded to a database system, a cloud platform processing center processes the data, and bridge inspection digital delivery is carried out on a processing result.
Example 2
Referring to fig. 1, 2 and 3, an inspection system using the method described in embodiment 1 includes a vehicle-mounted hangar system, a 4G/5G communication system, an unmanned plane route planning system, a flight control system and a database system.
Vehicle-mounted hangar system of unmanned aerial vehicle: the unmanned aerial vehicle hangar is loaded on a pick-up truck, can be used at fixed points and can also be used in a movable mode, the unmanned aerial vehicle hangar can park an unmanned aerial vehicle for a long time, an intelligent battery center is arranged in the hangar, meanwhile, a plurality of unmanned aerial vehicle batteries are subjected to charging management, mechanical arms are arranged in the hangar, the unmanned aerial vehicle batteries can be assembled and disassembled, automatic replacement of the unmanned aerial vehicle batteries is achieved, a lifting platform is arranged in the hangar, and the lifting platform can automatically convey the unmanned aerial vehicle to the top of the hangar; at the top of the hangar, the lifting platform can be used as an apron.
The flight control system comprises an unmanned aerial vehicle takeoff state checking system, and before the unmanned aerial vehicle takes off, hardware and software such as wings, battery capacity, various sensors and a flight control system are checked to reduce the falling risk of the unmanned aerial vehicle caused by hardware and software faults in the flight process.
Referring to fig. 6, 7, 8 and 9, the airline planning system: and aiming at different bridge detection positions, different route planning modes are adopted. If aiming at the pavement of the bridge bottom and the bridge deck, a snakelike air route planning mode is adopted; aiming at the side face of the bridge, adopting a vertical sawtooth route planning mode; adopting a step route planning mode aiming at the inclined stay cable of the bridge; and aiming at the bridge tower, adopting a surrounding route planning mode.
The flight control system comprises an emergency obstacle avoidance module, a distance threshold value is preset in the multi-sensing fusion obstacle avoidance reminding module, when the distance of the reminding obstacle is smaller than the set distance threshold value, the emergency obstacle avoidance module is started, and after the module is started, the unmanned aerial vehicle is stopped suddenly to prevent the unmanned aerial vehicle from colliding with surrounding obstacles; recording the positioning and attitude of the interrupted flight caused by the abnormal states of insufficient electric quantity, obstacle and the like of the unmanned aerial vehicle, and continuously executing the flight task according to the position and attitude of the interruption point after the unmanned aerial vehicle recovers to be normal; when the unmanned aerial vehicle encounters a local signal loss in the flying process, the unmanned aerial vehicle enters an autonomous positioning flying mode, and according to the synchronous positioning and map reconstruction result of the multi-sensor fusion of the unmanned aerial vehicle, the flying parameters from the current position to the next waypoint are calculated based on the airline flying task, so that autonomous flying without GPS signals is realized.
Flight control system is including preventing falling the module, when unmanned aerial vehicle inevitable hits the barrier or loses the signal and lead to falling, prevents falling the module and discern it as the crash state according to its acceleration of motion, automatic activation immediately it prevents falling the function, then installs the parachute on unmanned aerial vehicle and will open automatically, and unmanned aerial vehicle falls with slower speed, reduces the loss that the crash led to the fact to surrounding environment, people, and unmanned aerial vehicle self.
The flight control system comprises a multi-sensing fusion obstacle avoidance reminding module, and the module integrates a laser radar sensor and a visual sensor to realize synchronous positioning and map reconstruction of the surrounding environment of the unmanned aerial vehicle; when the unmanned aerial vehicle obstacle avoidance system reminds that the distance between the obstacles is smaller than the set distance threshold value, the emergency obstacle avoidance module is started, and after the module is started, the unmanned aerial vehicle is stopped suddenly to prevent the unmanned aerial vehicle from colliding with surrounding obstacles; after the unmanned aerial vehicle suddenly stops, the unmanned aerial vehicle can fly around the obstacle, and the main flying directions of the unmanned aerial vehicle during the execution of the flying task are forward, upward and downward. When unmanned aerial vehicle flies the in-process forward and meets the barrier and remind, the adjustment flight gesture is to turning to the right 90 degrees, continues to fly forward: if the obstacle avoidance early warning is removed, the flight attitude is adjusted again on the basis of the original attitude to turn left by 90 degrees to continue to recover to the normal state to execute the flight task; and if the obstacle avoidance early warning is kept and the distance between the obstacles is reduced, the flight attitude is adjusted to be turned right by 90 degrees again, and the aircraft flies forwards until the obstacle avoidance early warning is removed and the normal forward flight mode is recovered. When unmanned aerial vehicle runs into the barrier when flying downwards and reminds, the adjustment flight gesture is for flying forward: if the obstacle avoidance early warning is removed, the flight attitude is adjusted to continue flying downwards; if the obstacle avoidance early warning is still kept, the flight attitude is adjusted to be in the upward flight mode until the warning is removed, after the warning is removed, the flight attitude is adjusted to be in the forward flight mode for 0.5m, and the flight attitude is continuously adjusted to be in the normal downward flight mode. When unmanned aerial vehicle upwards flies the in-process and meets the barrier and remind, the adjustment flight gesture is for flying forward: if the obstacle avoidance early warning is removed, the flight attitude is adjusted to continue flying upwards; if the obstacle avoidance early warning is still kept, the flight attitude is adjusted to fly downwards until the warning is removed, after the warning is removed, the flight attitude is adjusted to fly forwards for 0.5m, and the flight attitude is continuously adjusted to be in a normal upward flight mode.
The database system comprises a cloud platform processing center, the bridge detection data of the unmanned aerial vehicle is uploaded to a cloud end, and the processing result is in the cloud end, so that the original bridge detection data and the processing result can be quickly checked anytime and anywhere; the cloud platform processing center monitors the transmission precision of the detected original data, automatically processes the data after the data are transmitted and models oblique photography, and improves the modeling efficiency by adopting a lightweight processing technology; after the model is built, performing bridge apparent disease identification on the image data by adopting a deep learning algorithm, and automatically highlighting and marking the diseases on the model; the association and linkage between the picture and the model are realized through a picture and model matching algorithm, the spatial position information of the diseases is obtained through the model, and the detailed characteristics and size information of the diseases are obtained through the picture associated with the model, so that the technical condition of the bridge is systematically and comprehensively evaluated; and automatically generating an unmanned aerial vehicle bridge detection report based on the bridge apparent disease identification result.
Load unmanned aerial vehicle and unmanned aerial vehicle hangar on the pick up car, it drives the pick up car to waiting to detect bridge department to detect the technical staff to detect, set up and patrol and examine the task, unmanned aerial vehicle is automatic patrols and examines the acquisition data, the automatic battery of changing of in-process, need not the human intervention of measurement personnel, unmanned aerial vehicle accomplishes the automatic landing of unmanned aerial vehicle hangar after the task, measurement personnel drives the pick up car and carries out the detection task to next bridge department, accomplish field work collection work after, data is automatic to be uploaded to cloud platform processing center, automatic modeling, disease automatic identification, the automatic generation detects the report, the maximize reduces field work and interior work in-process measurement personnel's intervention, improve and detect automaticly, digitization and visual level.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be covered by the present invention within the technical scope of the present invention, and the technical solutions and novel concepts according to the present invention should be added with equivalent alternatives or modifications.
Claims (5)
1. An intelligent bridge inspection method based on an unmanned aerial vehicle comprises a current flight attitude module, a hovering module, a multi-sensor fusion obstacle avoidance reminding module, an emergency obstacle avoidance module, a positioning and attitude recording module and a falling prevention module, and is characterized by comprising the following specific steps:
s1, when an unmanned aerial vehicle is about to encounter an obstacle, a multi-sensing fusion obstacle avoidance reminding module sends out alarm information, an emergency obstacle avoidance module enables the unmanned aerial vehicle to stop suddenly and fly around the obstacle, and the unmanned aerial vehicle is prevented from colliding with the obstacle;
s11, when the unmanned aerial vehicle meets the obstacle in the forward flight process and reminds, the flight attitude is adjusted to rotate to the right by 90 degrees, and the unmanned aerial vehicle continues to fly forward: if the obstacle avoidance early warning is removed, the flight attitude is adjusted again on the basis of the original attitude to turn left by 90 degrees to continue to recover to the normal state to execute the flight task; if the obstacle avoidance early warning is kept and the distance of the obstacle is reduced, the flight attitude is adjusted to turn right by 90 degrees again, and the aircraft flies forwards until the obstacle avoidance early warning is relieved and the normal forward flight mode is recovered;
s12, when the unmanned aerial vehicle meets an obstacle in the downward flight process and is reminded, adjusting the flight attitude to fly forwards: if the obstacle avoidance early warning is removed, the flight attitude is adjusted to continue flying downwards; if the obstacle avoidance early warning is still kept, the flight attitude is adjusted to be in the upward flight mode until the warning is removed, and after the warning is removed, the flight attitude is adjusted to be in the forward flight mode for 0.5m, the flight attitude is continuously adjusted to be in the normal downward flight mode;
s13, when the unmanned aerial vehicle is reminded of encountering an obstacle in the upward flying process, adjusting the flying attitude to fly forwards: if the obstacle avoidance early warning is removed, the flight attitude is adjusted to continue flying upwards; if the obstacle avoidance early warning is still kept, the flight attitude is adjusted to fly downwards until the warning is removed, and after the warning is removed, the flight attitude is adjusted to fly forwards for 0.5m, and the flight attitude is continuously adjusted to be in a normal upward flight mode;
s2, when the unmanned aerial vehicle collides, the motor of the unmanned aerial vehicle stops rotating and freely falls, the falling motion characteristic of the unmanned aerial vehicle is identified by the falling prevention module, and a parachute installed on the unmanned aerial vehicle is automatically started and opened to enable the unmanned aerial vehicle to land at a lower speed;
s3, when the electric quantity of the unmanned aerial vehicle is insufficient, the positioning and attitude recording module records the current position, the unmanned aerial vehicle starts a return flight mode and falls into a cabin of the unmanned aerial vehicle to replace a battery, and after the battery is replaced successfully, the unmanned aerial vehicle takes off to the recorded positioning position and continues to execute a flight task;
and S4, after the unmanned aerial vehicle finishes the flight task, the unmanned aerial vehicle automatically descends to an unmanned aerial vehicle hangar, the vehicle-mounted unmanned aerial vehicle system runs to the next bridge to be detected to execute the flight task, after the unmanned aerial vehicle flight task is finished, data are uploaded to a database system, the cloud platform processing center processes the data, and bridge inspection digital delivery is carried out on the processing result.
2. The intelligent bridge inspection method according to claim 1, further comprising a preset inspection route, wherein the inspection route comprises a serpentine route planning mode, a vertical sawtooth heading mode, a step route mode and a round route mode.
3. The intelligent bridge inspection method based on the unmanned aerial vehicle as claimed in claim 1, wherein the multi-sensor fusion obstacle avoidance reminding module in the step S1 is preset with a distance threshold, and when the distance between the unmanned aerial vehicle and the obstacle is smaller than the set threshold, an obstacle avoidance early warning is automatically sent out.
4. The intelligent bridge inspection method based on the unmanned aerial vehicle according to claim 1, wherein the cloud platform processing center in the step S4 receives bridge detection data of the unmanned aerial vehicle, performs automatic basic processing and photography modeling on the data after transmission is completed, performs bridge apparent disease recognition on image data by adopting a deep learning algorithm, automatically highlights diseases and standardizes the diseases on a model, and performs correlation between a picture and the model by a picture and model matching algorithm to obtain detailed characteristics and size information of the diseases.
5. An inspection system adopting the unmanned aerial vehicle-based bridge intelligent inspection method according to any one of claims 1-4, comprising a vehicle-mounted unmanned aerial vehicle system, a 4G/5G communication system, a flight control system, a route planning system and a database system,
the flight control system comprises a multi-sensing fusion obstacle avoidance reminding module, wherein the multi-sensing fusion obstacle avoidance reminding module comprises a laser radar sensor and a visual sensor and is used for synchronously positioning the surrounding environment of the unmanned aerial vehicle and reconstructing a map;
the air route planning system is preset with a snakelike air route mode, a vertical sawtooth course mode, a step air route mode and a surrounding air route mode, and when the unmanned aerial vehicle is located at different bridge detection positions, the inspection air route mode is automatically selected.
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| CN117519278A (en) * | 2023-12-04 | 2024-02-06 | 上海市建筑科学研究院有限公司 | Unmanned aerial vehicle obstacle avoidance method for bridge inspection |
| CN117519278B (en) * | 2023-12-04 | 2024-04-30 | 上海市建筑科学研究院有限公司 | Unmanned aerial vehicle obstacle avoidance method for bridge inspection |
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