CN114253300A - Unmanned aerial vehicle inspection system and method for gridding machine nest - Google Patents

Abstract

The invention provides an unmanned aerial vehicle inspection system and method of a gridding nest, which comprises a plurality of nests which are arranged in a gridding mode, wherein each nest is used for accommodating at least one unmanned aerial vehicle; the nest comprises a nest controller communicated with the control terminal, the nest controller is communicated with the unmanned aerial vehicle remote controller, and the unmanned aerial vehicle remote controller is communicated with the unmanned aerial vehicle; the control terminal is used for generating an optimal routing inspection path of the unmanned aerial vehicle and sending the optimal routing inspection path to the nest controller by taking the shortest routing inspection time as an optimization target according to the current endurance mileage of the unmanned aerial vehicle and the distances between the routing inspection target and each nest; the invention realizes the efficient unmanned aerial vehicle cooperative inspection based on the unmanned aerial vehicle nest arranged in a gridding manner, reduces the labor cost and meets the requirements of the normalized or emergency inspection of a plurality of inspection targets across the field.

Description

UAV inspection system and method for gridded machine nest

technical field

The invention relates to the technical field of electric power inspection, in particular to a UAV inspection system and method for gridded machine nests.

Background technique

The statements in this section merely provide background related to the present disclosure and do not necessarily constitute prior art.

As the UAV's guarantee transfer station, its role is self-evident. The deployment position of the UAV nest is very important for the UAV, which is directly related to the UAV's flight inspection radius and Operational efficiency and results.

The inventor found that the existing power inspection has the following problems:

(1) The location of the drone nest is set arbitrarily, or only a few drone nests are arranged sporadically, which often cannot achieve full coverage of the target to be inspected; A single flight inspection of the target to be inspected does not involve the coverage of the surrounding short-range waypoints, resulting in a waste of voyage and power during the inspection.

(2) The existing drone nests use a large number of mechanical arms to control the centering and power exchange of the drone, which makes the power exchange of the drone more cumbersome, and the multi-degree-of-freedom manipulator or power exchange mechanism is not compatible with the unmanned aerial vehicle. The cooperation of the machine can also easily lead to the failure of the mechanical arm or the drone, which will cause equipment damage and reduce the stability of the overall system.

(3) The intelligent drone nest can realize the interconnection with the background monitoring center. After monitoring the flight environment on site, most of the operators in the background monitoring center subjectively judge the on-site flight conditions. The degree of intelligence is low, the subjectivity is strong, and there are There is a certain possibility of misjudgment; or the judgment of flight conditions is only a simple patchwork, lack of comprehensive judgment of multi-source data, causing certain safety hazards to the flight mission.

(4) When the UAV identifies the coordinates of the landing position, it needs high-precision real-time positioning. Not only the cost of positioning components is high, but also the coordinate data of the preset point of the UAV airport must be obtained in real time. Landing control is cumbersome; there are solutions in the prior art to realize landing by identifying specific images of the landing point, but most of them only identify a single data source, and the landing accuracy cannot be guaranteed.

(5) The prior art discloses the solution of using binocular vision to achieve hovering positioning and ranging, which is still to control the drones to arrive at the inspection target in sequence, brake and hover, take photos at a fixed point, and then accelerate to the next inspection. The target is that the braking, hovering and acceleration of the drone consume a lot of battery power, and it is impossible to achieve autonomous inspection without hovering.

SUMMARY OF THE INVENTION

In order to solve the deficiencies of the prior art, the present invention provides a grid-based drone inspection system and method, which realizes the efficient collaborative inspection of drones based on the grid-based drone nest , reducing labor costs and meeting the needs of normalized or emergency inspections for multiple inspection targets across fields.

In order to achieve the above object, the present invention adopts the following technical solutions:

A first aspect of the present invention provides an unmanned aerial vehicle inspection system with gridded machine nests, including a plurality of machine nests deployed in a grid, and each machine nest is used for accommodating at least one unmanned aerial vehicle;

The machine nest includes a machine nest controller that communicates with the control terminal, the machine nest controller communicates with the UAV remote control, and the UAV remote control communicates with the UAV;

The control terminal is used to obtain the inspection target corresponding to each nest according to the current cruising range of the UAV and the distance between the inspection target and each machine nest, and take the shortest inspection time as the optimization goal, and generate each inspection target according to the determined inspection target. The optimal inspection path of the UAV is sent to the nest controller.

Further, the machine nest includes:

a machine nest body, and a bearing mechanism, a vertical fixing mechanism and a lateral fixing mechanism arranged in the machine nest body; the bearing mechanism includes a retractable landing platform and a first motor, and the landing platform is driven by the first motor;

The vertical fixing mechanism includes a first centering rod, one end of the first centering rod is set on the side wall of the machine nest body through a rotating shaft, a gear is provided on the first centering rod, and a landing platform is provided with The rack meshing with the gear drives the first return rod to rotate around the rotation axis through the meshing of the gear and the rack;

The lateral fixing mechanism includes a rotating rod, a second centering rod and a second motor, the two ends of the rotating rod are arranged on the side wall of the main body of the machine nest, and the second centering rod is arranged on the rotating rod; The rod is driven by the second motor to rotate in the opposite direction of the moving direction of the landing platform relative to the main body of the machine nest, thereby driving the second centering rod to move in the vertical direction of the moving direction of the landing platform.

Further, the machine nest includes:

The main body of the machine nest, the inside of the main body of the machine nest includes the drone position, the charging module and the energy storage module;

The main body of the machine nest is provided with an installation module, and the installation module adopts a screw-type automatic locking structure to fix the main body of the machine nest. The controller communicates with the charging module and the installation module respectively.

Further, the UAV is loaded with a three-axis gimbal, an RTK positioning module and a front-end AI processing module;

A camera and a video camera are installed on the three-axis pan/tilt; the camera is a monocular zoom camera; the video camera is used to obtain video information of the tower; wherein, the camera and the video camera are integrated in one lens.

RTK positioning module, used to locate the three-dimensional coordinate information of UAV;

Front-end AI processing module, configured as:

Fit the UAV flight control data, RTK positioning module data and zoom camera to collect images, issue flight control commands to control the UAV flight, control the gimbal to adjust the camera angle and zoom, lock the inspection target and take pictures;

Use the wide-angle visual zoom camera to take photos during the flight close to the hovering point, calculate the coordinate value (GPS value) of the photo and the attitude of the gimbal, and identify the inspection target in the photo through the camera imaging principle;

According to the current UAV GPS position and 3D speed and the roll angle, pitch angle and yaw angle of the gimbal attitude, adjust the position of the UAV gimbal through the Kalman filter algorithm, and lock the zoom camera to the target viewing point of the tower through zooming ;

Take pictures to complete the information collection of the tower target inspection point, thereby improving the accuracy of the inspection target information collection and the quality of the collected images.

A second aspect of the present invention provides a UAV inspection method for gridded machine nests, including the following processes:

Get the distance between the inspection target and each machine nest;

Select the nest closest to the inspection target as the optimal nest;

The judgment of each inspection target is carried out in turn, and the corresponding inspection target of each machine nest is obtained;

Inspection mission planning for any nest, including:

According to the distance between the inspection target within the machine nest and the machine nest, the inspection target number is carried out, the farther the distance, the larger the number;

When the difference between the total endurance time of the UAV and the time of a single inspection of a certain inspection target is less than the minimum value of the time of a single inspection of other inspection targets, this inspection target is regarded as a single base tower task;

Otherwise, determine the time from the nest to the current inspection target, the inspection time of the current inspection target, the inspection time from the current inspection target to the nearest secondary inspection target whose number is smaller than the current inspection target, and the current inspection target Whether the sum of the time to the secondary inspection target and the time from the secondary inspection target to the machine's nest is greater than the total endurance time of the drone, if so, this inspection target will be regarded as a single-base tower task; otherwise, the two-base mission will be executed. For the route task of the tower, the current inspection target and the secondary inspection target are inspected in turn.

A third aspect of the present invention provides a method for judging an unmanned aerial vehicle task execution environment, using the above-mentioned unmanned aerial vehicle inspection system of a gridded machine nest, including:

Obtain the environment information inside the nest and the environment information outside the nest within the perception range;

According to the target nest selected by the position of the drone, the corresponding flight influencing factors are determined according to the flight instructions, and the corresponding flight environment data is retrieved from the environmental information outside the nest; the flight conditions are judged according to the flight environment data, if the flight environment When the data does not meet the flight conditions, control the drone to return;

Determine the corresponding landing influence factors according to the return-to-flight command, so as to retrieve the corresponding landing environment data and return environment data from the environment information outside the nest and the environment information inside the nest of the target nest; The landing method adjusts the environment in the nest according to the data of the returning environment until the drone returns to the target nest.

The selection of the target nest includes: according to the position of the drone, judging the perception range of the nest where the drone is located, and taking the nest that falls within the perception range as the target nest; if the perception ranges of the two nests overlap, then According to the distance between the drone and the nest, take the nearest nest as the target nest.

A fourth aspect of the present invention provides a precise landing control method for an unmanned aerial vehicle, which utilizes the above-mentioned drone inspection system for gridded machine nests, including:

Obtain the positioning data of the UAV;

According to the obtained positioning data, determine whether the UAV is within the preset landing range. When the UAV is within the preset landing range, execute the next step; otherwise, control the UAV to move until the position requirements are met;

When the drone is located at the first preset distance from the landing point, acquire the image data or video data below the drone, and control the drone when the precise descent range code is identified according to the acquired image data or video data. Descend the second preset distance, and execute the next step; otherwise, control the drone to descend the third preset distance, and perform the precision descent range code recognition again until the precise descent range code is identified;

Obtain the image data or video data below the drone again, and when the precise descent position code is identified according to the image data or video data obtained again, control the drone to descend to the fourth preset distance from the landing point, and control the unmanned aerial vehicle. The man-machine landed.

A fifth aspect of the present invention provides a UAV inspection method based on visual movement tracking, using the above-mentioned UAV inspection system for gridded machine nests, including:

S1: According to the inspection requirements, the image acquisition module on the gimbal is used to obtain the real-time wide-angle image of the inspection target before the UAV enters the inspection point at a constant speed;

S2: determine whether the inspection target is located in the real-time image obtained by shooting, and if so, go to step S3; otherwise, control the movement of the gimbal and change the attitude until the inspection target in the real-time image is found;

S3: The processing module uses the Kalman filter algorithm to fit the UAV shooting position and the gimbal attitude position according to the information of the inspection target position, the UAV shooting position, and the gimbal attitude in the real-time image, and determines the focal length of the image acquisition module model;

S4: Control the drone to fly at a constant speed to the calculated shooting position. During the flight, the processing module reversely adjusts the attitude of the gimbal in real time according to the three-dimensional direction of the drone’s constant speed flight, so as to achieve the real-time image setting of the image acquisition module The area locks the inspection target, and adjusts the focal length mode of the image acquisition module;

S5: The drone arrives at the shooting position, confirms that the inspection target position is in the set area of the real-time image of the image acquisition module, and locks the inspection point for image acquisition;

S6: The processing module processes the collected images, controls the UAV to perform the next detection point task, and re-executes S1 until all detection point image acquisition tasks are completed.

Compared with the prior art, the beneficial effects of the present invention are:

1. The present invention innovatively designs a drone inspection system for gridded nests, and proposes a drone inspection method for gridded nests. According to the current cruising range of the drone and the The distance between the inspection target and each machine nest is optimized with the shortest inspection time as the optimization goal, and various types of inspection targets corresponding to each machine nest are obtained. The collaborative inspection optimization problem of single-base tower tasks and multi-base tower tasks realizes the optimization of inspection paths with the goal of minimum inspection time, and realizes grid-based drone nests for inspection targets in various fields. The high-efficiency UAV collaborative inspection reduces labor costs and meets the needs of normalized or emergency inspections for multiple inspection targets in various fields.

2. The present invention innovatively proposes an unmanned aerial vehicle nest, designs a double restraint technology for the lateral and vertical restraint of the unmanned aerial vehicle, and proposes a method of matching the fixing mechanism of the return rod group with the rack and pinion mechanism Carrying out the drone return to the center solves the limitations of the single scene of the drone nest and the stability of the drone's parking; improves the stability of the drone in different inspection environments, and the drone is more stable. As a general-purpose machine nest, the Nest supports remote control operations, which significantly improves the efficiency of inspection operations, realizes the diversification of application scenarios, and realizes the coverage of UAVs in a wider range.

3. The present invention innovatively proposes a method for judging the mission execution environment of an unmanned aerial vehicle, which is combined with different mission environmental conditions for different flight missions or return-to-home missions to judge whether it is suitable to perform the mission, so as to meet the needs of the judging logic of the flight conditions of the aircraft nest, In complex flight situations, the self-judgment method of the aircraft nest realizes the redundancy of judgment conclusions, improves the judgment accuracy, and solves the limitations of single judgment conditions, subjective interference and low intelligence of the existing flight environment monitoring technology. With manual intervention, the autonomous pre-judgment of flight conditions under different tasks is realized, which significantly improves the inspection efficiency of UAVs and the safety of the nest system.

4. The present invention innovatively proposes a precise landing control method of the UAV, which realizes the preliminary calibration of the UAV and the position to be landed according to the positioning data of the UAV, and integrates the real-time differential positioning data and the precise landing range code. With the precise landing position code, through continuous image recognition and distance approach, the problem of difficult UAV precise landing control is solved, the precise echelon control of UAV landing is realized, and the accuracy of UAV landing control is improved.

5. The present invention innovatively proposes a UAV inspection method based on visual movement tracking. During the flight between the UAV entering the inspection target and leaving the inspection target, the UAV always follows the setting. Track flight, adjust the gimbal attitude and camera zoom in real time by fitting the current position and speed data through the Kalman filter algorithm to achieve the camera's movement tracking and locked shooting of the inspection target, and realize the inspection process of the drone without hovering. The automatic collection of the inspection target image greatly reduces the labor intensity of the inspection personnel, and the present invention adopts the reverse movement tracking method to achieve relative stillness with the inspection target by dynamically adjusting the posture of the drone and the pan-tilt camera; The power of the drone and the workload of a single flight are greatly saved; the acquisition of the inspection target of the present invention is completed based on the monocular camera, the structure is simple, and the cost is low.

Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will become apparent from the description which follows, or may be learned by practice of the invention.

Description of drawings

The accompanying drawings forming a part of the present invention are used to provide further understanding of the present invention, and the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute an improper limitation of the present invention.

FIG. 1 is a schematic diagram of a drone inspection system for a gridded machine nest provided in Embodiment 1 of the present invention.

FIG. 2 is a schematic diagram of a drone nest provided in Embodiment 1 of the present invention.

FIG. 3 is a schematic diagram of the main body of the drone nest provided in Embodiment 1 of the present invention.

FIG. 4 is a schematic diagram of the gear drive at the bottom of the first centering rod according to Embodiment 1 of the present invention.

FIG. 5 is a schematic diagram of the reset of the first centering rod and the second centering rod according to Embodiment 1 of the present invention.

Figures 6(a)-6(b) are schematic diagrams of the drone returning to the center provided by Embodiment 1 of the present invention.

7(a)-7(b) are schematic diagrams of landing of the UAV provided in Embodiment 1 of the present invention.

FIG. 8 is a schematic diagram of the installation of the drone nest provided in Embodiment 1 of the present invention.

FIG. 9( a ) is a schematic diagram of the internal structure of the mobile drone nest provided in Embodiment 2 of the present invention.

FIG. 9(b) is a schematic structural diagram of a hatch door of a mobile drone according to Embodiment 2 of the present invention.

FIG. 10 is a schematic structural diagram of a charging module provided in Embodiment 2 of the present invention.

FIG. 11 is a schematic structural diagram of a device for fixing a drone position according to Embodiment 2 of the present invention.

FIG. 12 is a schematic partial structural diagram of the drone camera position fixing device provided in Embodiment 2 of the present invention.

FIG. 13(a) and FIG. 13(b) are schematic structural diagrams of an installation module provided by Embodiment 2 of the present invention.

FIG. 14 is a schematic diagram of a partial structure of an installation module according to Embodiment 2 of the present invention.

FIG. 15 is a schematic diagram of a route planning process according to Embodiment 3 of the present invention.

FIG. 16 is a schematic diagram 1 of task planning according to Embodiment 3 of the present invention.

FIG. 17 is a second schematic diagram of task planning according to Embodiment 3 of the present invention.

FIG. 18 is a schematic diagram of division of task instructions and corresponding environmental factors according to Embodiment 4 of the present invention.

FIG. 19 is a schematic diagram of judging an execution environment of a UAV storage task according to Embodiment 4 of the present invention.

FIG. 20 is a schematic flowchart of a precise landing control method for an unmanned aerial vehicle according to Embodiment 5 of the present invention.

FIG. 21 is a schematic flowchart of a method for autonomous inspection of a UAV according to Embodiment 6 of the present invention.

Among them, 1, tower, 2, bottom support of machine nest, 3, machine nest, 4, landing platform, 5, top cover, 6, machine nest body, 7, rotating rod, 8, second motor, 9, first motor , 10, the second return pole, 11, the charging pole, 12, the charging port, 13, the first return pole, 14, the rack, 15, the fixed seat; 16, the charging module; 17, the drone position; 18. Energy storage module; 19. Display module; 20. Charging port; 21. BMS control board; 22. Cooling fan; 23. Communication interface; 24. Charging indicator light; 25. First clamping piece; 26. Elastic piece 27, the second clamping member; 27-1, the handle; 27-2, the first sleeve; 27-3, the first telescopic rod; 27-4, the fixed end; 27-5, the first spring; 28 , the second sleeve; 29, the double shaft motor; 30, the second telescopic rod; 31, the spring slider; 31-1, the first slider; 31-2, the second spring; 31-3, the second slider block; 32, lead screw; 33, drone; 34, machine nest.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and embodiments.

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

Embodiments of the invention and features of the embodiments may be combined with each other without conflict.

Example 1:

Embodiment 1 of the present invention provides a UAV inspection system with gridded nests. As shown in FIG. 1 , it includes a plurality of nests 34 deployed in grids, and each nest is used for accommodating at least one drone. UAV 33;

The machine nest includes a machine nest controller that communicates with the control terminal, the machine nest controller communicates with the UAV remote control, and the UAV remote control communicates with the UAV;

The control terminal is used to obtain the inspection target corresponding to each nest according to the current cruising range of the UAV and the distance between the inspection target and each machine nest, and take the shortest inspection time as the optimization goal, and generate each inspection target according to the determined inspection target. The optimal inspection path of the UAV is sent to the nest controller.

In this embodiment, the substation is selected as the main deployment point of the drone nest, and the drone can first inspect the equipment of the substation nearby; the surrounding area of the substation is the main intersection of power lines, and it is also an area that needs to be inspected. Inspection around the substation can inspect the power lines to the maximum extent; the drone nest is deployed in the substation and can also be used as a part of the substation maintenance, which is convenient for operation and maintenance.

It can be understood that in other embodiments, the drone nest can also be appropriately deployed in 5G base stations or mountain top photovoltaics, etc., and can also target targets in other fields as inspection targets, such as communication fields, fire protection fields, etc. , the drone nest can be deployed as long as there is electricity.

As shown in FIG. 2 and FIG. 3 , the drone nest described in this embodiment is a miniaturized drone nest, including: a nest body, and a bearing mechanism, a vertical a fixing mechanism and a lateral fixing mechanism; the bearing mechanism includes a retractable landing platform and a first motor, and the landing platform is driven by the first motor;

The vertical fixing mechanism includes a first centering rod, one end of the first centering rod is set on the side wall of the machine nest body through a rotating shaft, a gear is provided on the first centering rod, and a landing platform is provided with The rack meshing with the gear drives the first return rod to rotate around the rotation axis through the meshing of the gear and the rack;

The lateral fixing mechanism includes a rotating rod, a second centering rod and a second motor, the two ends of the rotating rod are arranged on the side wall of the main body of the machine nest, and the second centering rod is arranged on the rotating rod; The rod is driven by the second motor to rotate in the opposite direction of the moving direction of the landing platform relative to the main body of the machine nest, thereby driving the second centering rod to move in the vertical direction of the moving direction of the landing platform.

In this embodiment, the nest body 6 is a rectangular frame structure, the top of the nest body 6 is provided with a top cover 5, and the top cover 5 is provided with a solar photovoltaic panel, which absorbs light energy through the solar photovoltaic panel. , and convert the light energy into electrical energy storage as the power support of the machine nest.

Preferably, the top cover 5 is a sloped design to prevent water accumulation on the top of the nest.

In this embodiment, the nest body 6 is provided with a retractable landing platform 4. When the drone lands, the landing platform 4 is pushed out inside the nest body 6 to carry the drone, and the drone lands. After that, the landing platform 4 is recovered into the main body 6 of the nest; when the drone performs the inspection task, the landing platform 4 is pushed out from the main body 6 of the nest, the drone takes off, and then the landing platform 4 is recovered to the main body of the nest 6 within.

In this embodiment, the nest main body 6 is closed on three sides, and the forward surface and the landing platform 4 form a closed surface to ensure the overall protection performance of the nest.

In this embodiment, the first motor 9 is connected with the landing platform 4 through a rod, so as to drive the landing platform 4 to push out of the nest body 6 or be recovered into the nest body 6 .

Preferably, there are two first motors 9 .

In this embodiment, as shown in FIG. 3 , slide rails are provided at both ends of the nest body 6 , and both ends of the rotating rod 7 are set on the slide rails of the nest body 6 through rolling pulleys. The second motor 8 controls the rotation of the rotating rod 7 ; the rotating direction of the rotating rod 7 is opposite to the moving direction of the landing platform 4 .

The end of the second centering rod 10 is provided with a chute, and each end of the rotating rod 7 is provided with a second centering rod 10 , and the second centering rod 10 is arranged on the rotating rod 7 through the sliding groove , along with the rotation of the rotating rod 7, a one-way movement is performed along the direction of the rotating rod 7, that is, a lateral movement is performed in a direction perpendicular to the rotation direction of the rotating rod 7.

The rotating rod 7 is provided with a thread, and the second centering rod 10 moves unidirectionally along the thread with the rotation of the rotating rod 7 through the chute.

Preferably, the rotating rod 7 adopts a lead screw.

Described rotating rod 7 rotates on the slide rail under the drive of the second motor 8, and controls the movement direction of the second centering rod 10 according to the rotating direction of the rotating rod 7;

Preferably, when the rotating rod 7 rotates in the forward direction, the second centering rods 10 on both sides perform a centering motion, that is, move to the middle position; when the rotating rod 7 rotates in the reverse direction, the second centering rods 10 on both sides move toward the middle position. Move in the opposite direction, that is, open to both sides; the rotation of the rotating rod 7 on the slide rail cooperates with the movement of the second centering rod 10 through the chute to balance the force of the reciprocating motion of the second centering rod 10 with the rotating rod 7, It is ensured that the second return rod 10 is a displacement of one degree of freedom.

Preferably, when the landing platform 4 is pushed out, when the rotating rod 7 rotates in the reverse direction, the second centering rods 10 on both sides move in the opposite direction, that is, open to both sides; at this time, it is also used for the opening of the second centering rods 10 After that, the drone on the landing platform 4 can fly out;

When the landing platform is recovered and reset, when the rotating rod 7 is rotated in the forward direction, the second centering rods 10 on both sides will move back to the center, that is, move to the middle position; at this time, it is also used for the lateral movement of the drone on the landing platform. Reset, fix the drone with lateral restraints.

In this embodiment, the first centering rods 13 are provided on the two opposite side walls of the machine nest main body 6. Under the meshing of the gear and the rack, the first centering rods 13 on both sides rotate around the axis. In order to make the other end of the first centering rod 13 move to the middle position or open to both sides.

In this embodiment, as shown in FIG. 4 , the first centering rod 13 is provided with a gear, the landing platform 4 is connected to the rack 14 by screws, and the rack 14 is engaged with the gear; the landing platform is pushed out and retracted When the gear rotates, the first centering rod 13 is driven to rotate around the axis through the meshing of the rack and pinion; when the first centering rod 13 rotates around the axis, the moving power is converted into a rotational power torque through the rack and pinion transmission.

Preferably, the first centering rod 13 is used for the vertical reset of the UAV, the first centering rod 13 is rotated in a circle around the axis through the rotating shaft, and the other end of the rotation is rotated around the axis to the middle position, so as to Fixed drone.

In this embodiment, the resetting of the UAV is completed by jointly pushing the second centering rod 10 and the first centering rod 13. As shown in FIG. The pushing of the center-returning rod 10 results in a horizontal reset, and a part of the vertical reset is completed by the rotation of the first center-returning rod 13 .

Preferably, before the drone takes off, the first motor 9 pushes the landing platform 4 to open the front side of the main body 6 of the machine nest. During the opening process, the second centering rod 10 is opened, and the reverse direction of the rotating rod 7 is performed. Rotate, the second centering rods 10 on both sides move in the opposite direction, that is, open to both sides to release the lateral fixation of the drone; The meshing of the rack 14 in the landing platform 4 and the gear in the first return rod 13 drives the rotation of the first return rod 13, and the first return rods 13 on both sides are also opened to both sides by rotating around the axis, releasing the The vertical fixation of the UAV enables the UAV to take off autonomously according to the planned route and perform inspection operations.

After the UAV completes the inspection task, it will accurately land on the landing platform 4 through visual assistance, and then the first motor 9 will drive the landing platform 4 to close the cabin. , the second centering rods 10 on both sides perform a centering motion, that is, move to the middle position to complete the lateral reset of the drone; at the same time, through the meshing of the rack 14 and the gear, the first centering rod 13 is driven to rotate, The first return rods 13 on both sides are rotated around the axis and moved to the middle to fix the drone and complete the vertical reset of the drone. Figures 6(a)-6(b) and 7(a)-7(b) are schematic diagrams of the UAV returning to the center and landing.

In this embodiment, charging rods 11 are also provided at both ends of the main body 6 of the machine nest, and a plurality of charging ports 12 are provided on the charging rods. After the drone is reset, the charging contact pad at the bottom of the drone contacts the charging ports. 12. Charge through the nest control command.

When the drone performs the inspection task, it firstly detects the remaining power of the battery. When the power is insufficient, the power battery of the drone is charged through the charging port 12; The plane takes off.

In more embodiments, the above-mentioned drone nest, as a general-purpose drone nest, can be applied to the tower. The installation process is shown in FIG. 8 . On the tower 1, the machine nest 3 is connected with the bottom support of the machine nest through screws, and the machine nest bottom support 2 is fixedly installed on the tower 1 through bolts. In different terrains, it can be set on the basis of the tower, which can realize the diversification of the scene.

In more embodiments, the above-mentioned drone nest can be used with a vehicle-mounted drone, and the drone nest is installed on the roof of the vehicle through the bottom support of the nest.

Example 2:

Embodiment 2 of the present invention provides a UAV inspection system with gridded machine nests, including a plurality of machine nests deployed in a grid, and each machine nest is used for accommodating at least one UAV;

The machine nest includes a machine nest controller that communicates with the control terminal, the machine nest controller communicates with the UAV remote control, and the UAV remote control communicates with the UAV;

The control terminal is used to obtain the inspection target corresponding to each nest according to the current cruising range of the UAV and the distance between the inspection target and each machine nest, and take the shortest inspection time as the optimization goal, and generate each inspection target according to the determined inspection target. The optimal inspection path of the UAV is sent to the nest controller.

As shown in FIG. 9( a ) and FIG. 9( b ), the nest described in this embodiment is a mobile drone nest, including a main controller and a nest body, and the nest body includes a charging module 16 inside. , UAV position 17, energy storage module 18 and display module 19; wherein, the main body of the machine nest is provided with an installation module, and the installation module adopts a screw-type automatic locking structure to fix the main body of the machine nest; The drone position is provided with a drone fixing device that can absorb shock independently in the horizontal and vertical directions, and the main controller is respectively connected with the charging module and the installation module.

specific:

As shown in FIG. 10 , the charging module includes several charging ports 20 , a BMS (BATTERY MANAGEMENT SYSTEM) control board 21 , a charging cooling fan 22 , a communication interface 23 and a charging indicator light 24 ;

The drone position of the main body of the machine nest can be placed on the mainstream RTK (Real-time kinematic) drones in the market. Since the application scenario of the present invention corresponds to the mobile drone machine nest, it often faces random changes in different terrain environments. The vehicle moves, so a fixing device is provided at the drone position for fixing the drone. Wherein, as shown in FIG. 11 , the fixing device includes a first clamping member 25 and a second clamping member 27 , and the first clamping member and the second clamping member are connected by an elastic member 26 to form a clamping structure (similar to clip structure).

Wherein, the first clamping member 25 is fixed on the surface of the drone. As shown in FIG. 12 , the second clamping member includes a handle 27-1, a first sleeve 27-2, a first spring 27-5. Two telescopic rods 27-3 at both ends of the sleeve and the fixed end, the first spring is located in the middle of the sleeve, and the two ends of the first spring are respectively fixedly connected to one end of the two telescopic rods , the two telescopic rods are pulled toward the center of the sleeve through the first spring, and the horizontal direction of the drone is fixed through the fixed end. At the same time, the fixed end is based on the clamp formed by the fixing device. The holding structure can fix the UAV in the vertical direction. Based on the springs in the horizontal direction and the elastic members in the vertical direction of the fixing device, on the one hand, the fixing device can achieve better fixing; The force in the horizontal and vertical directions of the UAV realizes vibration reduction protection and further ensures the safety of the UAV.

The nest body is provided with an installation module for installing the drone nest, wherein the installation module penetrates the nest body and is fixedly connected to the nest body, as shown in Figure 13(a)- As shown in FIG. 14 , the installation module adopts a screw-type automatic locking structure to fix the main body of the machine nest; the screw-type automatic locking structure includes a second sleeve 28 and a double outlet shaft fixed at the center of the sleeve Motor 29, the two ends of the rotor of the dual-shaft motor are respectively fixedly connected with a section of the lead screw 32, and the other end of the lead screw 32 is connected with one end of the spring slider 31 through a threaded hole, and the spring slider 31 is connected with the screw The lever rotates and moves horizontally and linearly, and drives the expansion and contraction of the second telescopic rod 30 fixedly connected with the other end of the spring slider. The spring slider 31 includes a first slider 31-1 and a second slider 31-3, and the first slider 31-1 and the second slider 31-3 are connected by a second spring 31-2 . The first slider 31-1 of the spring slider 31 is provided with a threaded hole matching the lead screw 32, and the second slider 31-3 is in a position corresponding to the first slider 31-1 A circular hole is provided, and the diameter of the circular hole is larger than the outer diameter of the lead screw. At the same time, one end of the fixed rod fixedly connected with the second slider is also provided with a hole of a preset length, and the hole diameter of the hole is also larger than that of the lead screw. The outer diameter of the bar.

In order to facilitate the expansion and contraction of the spring slider 31 and the second telescopic rod 30 in the direction of water quality in the sleeve.

In order to ensure the automation of the installation process, a pressure sensor is provided on the first slider 31-1, and the pressure sensor is connected to the main controller; at the same time, the main controller is connected to the dual-shaft motor, And based on the comparison result of the acquired pressure value of the pressure sensor and the preset threshold value, the operation of the dual-shaft motor is controlled.

Specifically, the working mechanism of the installation module is as follows:

The dual-shaft motor acts as the power core to drive the lead screw to rotate, the lead screw rotates the power block to displace in the horizontal direction, and the power block transmits the thrust through the spring to make the fixed end gradually contact the cargo box (in this embodiment, the compartment of the pickup truck). , The power block is equipped with a pressure sensor. When the sensor receives the reaction force from the carriage and reaches a predetermined value, it forms a feedback, and the dual-shaft motor stops rotating and automatically locks. When the vehicle receives bumps, the spring acts as a damper to absorb the vibration and maintain the self-stable state of the mobile nest.

The drone operates according to the inspection task. The drone’s autonomous inspection software is equipped in the nest, and the refined inspection operation is carried out according to the track planning plan prepared in advance. The current state of the man-machine and the specific working mode, after the drone operation is completed, the staff manually replaces the drone battery, giving full play to the operator's subjective initiative.

The main controller is also connected with a display module, which is used for displaying the state of the battery in the charging port of the charging module, and issuing commands through the display module. Wherein, the issuing of the command includes an installation command (that is, installing the drone nest in the vehicle) and issuing an operation task to the drone.

The energy storage module 18 is used as an energy supply module for the mobile operation of the nest, and is equipped with a special charging gun to charge it. During the process of the mobile drone nest following the vehicle to the site for inspection operations, the energy storage module supports the machine. Various power supplies in the nest, including charging module, display module and main control module, etc.

As shown in FIG. 14 , after the mobile drone nest of the present invention is installed in the pickup truck, the personnel operate the installation module through the display module to automatically lock it with the pickup truck; when the drone is performing inspection operations, the vehicle Bring the mobile nest to the vicinity of the work site. After opening the nest, the personnel turn on the aircraft fixing device, take out the drone, select the battery recommended by the charging module for installation, and select a suitable inspection route through the autonomous flight software in the nest. The man-machine autonomously completes the inspection operation. After completing the task, the staff replaces the battery and puts the aircraft back into the nest.

Example 3:

As shown in FIG. 15 , Embodiment 3 of the present invention provides a UAV inspection method for gridded machine nests, which specifically includes:

It is assumed that the flying speed of the drone is V, and the position of the nest is three-dimensional coordinates (0, 0, 0). In this embodiment, the tower is used as the inspection target for example.

The coordinates of the positions of several towers are (X1, Y1, Z1), (X2, Y2, Z2),... , Z-axis extension, assuming the coordinates of the tower N (Xn, Yn, Zn), between the two points, the straight line is the shortest, so the straight-line distance of the drone flying from the nest to the tower N is

The inspection complexity of a single tower is determined according to the tower type (tensile tower, linear tower, corner tower, etc.), and its inspection complexity can be determined according to the three-dimensional point cloud model of the tower. Here, the time Tn is used to represent the complexity of tower N, which is The physical meaning is the time spent by the drone to inspect the power tower.

An example is as follows: For a single base power tower N, the time T=Sn/V+Tn+Sn/V required to complete an inspection, which includes three parts: the time-consuming Sn/V of the drone from the nest to the tower N , the time-consuming Tn for inspecting the target object of the tower N and the time-consuming Sn/V for the UAV returning to the nest after the inspection is completed.

What needs to be considered here is that if the drone's battery life is still sufficient after the inspection of the tower N, you can consider inspecting the two-base or even multi-base towers in a single flight mission. The purpose of this is to reduce the number of times the drone returns to the nest. , which also reduces the number of drone inspections within the coverage area of the machine nest, and achieves the goal of the optimal path and the shortest time.

In this embodiment, the complexity T of the towers 1, 2, 3, ···, n and its three-dimensional coordinates (Xn, Yn, Zn) are known, and the towers from the near to the farthest distance from the origin of the machine nest are numbered as 1, 2, 3, ···, n, provide the basis for the subsequent planning method, the cruising ability of the UAV is T, the inspection speed of the UAV is V, and the coordinates of the nest position are (0, 0, 0).

最大的开始，依次减少进行判断。The methods and steps of autonomous route planning are as follows: The basic principle of route planning is to first plan the tower that is far away from the origin of the nest, namely

Start with the largest and decrease in turn for judgment.

First of all, it is planned to confirm that only one base tower can be inspected. The condition of this task is to screen out the towers within the coverage area of the machine nest, far from the origin of the machine nest, and with high complexity of their own towers, and perform as a single base tower task. , expressed by the formula as:

这里杆塔N的复杂度记作Tn，单位为秒，杆塔N的三维坐标记作(Xn，Yn，Zn)，单位为米，还有无人机的巡航能力记作T，单位为秒，无人机巡检速度V，单位为米每秒，还有机巢位置(0，0，0)，单位为米。in,

Here, the complexity of tower N is denoted as Tn, in seconds, the three-dimensional coordinates of tower N are denoted as (Xn, Yn, Zn), in meters, and the cruising ability of the UAV is denoted as T, in seconds, no The human-machine inspection speed V, the unit is meters per second, and the nest position (0, 0, 0), the unit is meters.

The judgment principle of the above formula is: after the drone has inspected a certain base tower alone, the remaining endurance is less than the complexity T of all other towers within the coverage of the machine nest, that is, the base tower can only be completed by a single inspection task. These towers are considered to be the most far-reaching airline missions with nest coverage.

但是其周边的杆塔T_n-1又不足以利用巡检杆塔Tn后的剩余续航能力去完成单次巡检，用公式表达为：Second, some towers are tasked with

However, the surrounding towers Tn _-1 are not enough to use the remaining endurance after the inspection of the towers Tn to complete a single inspection. The formula is expressed as:

且：

and:

Among them, T _n-1 is the surrounding tower that is closer to Tn in a straight line. Because it is numbered n-1, it is closer to the origin of the nest; so far, the routes for all single-base tower missions have been planned.

The next step is a route mission involving two base towers. The determination principle is that after the drone has inspected a base tower alone, the remaining endurance is only enough for a base tower near the tower to continue the inspection, and the drone will fly straight to the vicinity. The tower continues to patrol, and then returns to the nest in a straight line, with its flight path forming a triangle.

It should be pointed out that the order of route planning is from far to near, that is, the number is from large to small. After a certain tower is inspected, it still has endurance. When searching for nearby towers, you can only search for towers with a smaller number than the current tower. No. to ensure the clarity of the method.

The formula is expressed as follows:

Next is the route mission that includes the three base towers. The route task forms a quadrilateral. At this time, it is necessary to search for the unidirectionality of the nearby towers. For example, the tower 3-2-1 in the left side of Figure 16 is a clockwise search method, which can avoid the overlapping of the routes and optimize the route path, because Tower 3 is farther from the origin of the nest than tower 1. The route planning is calculated from tower 3. In fact, after the route task is determined, the inspection can be performed in reverse order. For example, the inspection order of tower 3-2-1 or 1-2-3 is both Feasible, because the inspection path distance is the same length, the planned route here is 3-2-1, which follows the planning principle of towers from far to near, which is calculated by the reverse planning method of near-to-far The path out is shorter.

By analogy, flight missions including four bases or five bases can be gradually planned. For example, the four-base route planned is an irregular pentagon, and the sum of the five sides is the total length of the route. With more and more route planning, it means that the towers farther from the origin of the nest have been planned to the route, and the towers closer to the origin of the nest will search for nearby towers more and more. This is because the distance The remaining battery life of the tower closer to the origin of the nest will be greater and greater.

As shown in Figure 16, the difference between the two figures is the route planning of towers 6, 1, 2, and 3. In the left figure, tower 6 is planned first, and the nearby towers are searched according to the endurance. If the search range does not include tower 1, as shown on the left As shown in the figure, tower 6 can only be planned as a single-base route task, and as shown in the figure on the right, if tower 1 is included in the planning range of tower 6, then the triangular route formed by tower 6, tower 1 and the origin of the machine nest is calculated. To meet the endurance capability of the UAV, the tower 6 and the tower 1 are planned to be dual-base routes.

In this embodiment, gridization is embodied in deploying multiple machine nests based on substations in an area all over the power poles and towers, and staggered deployment in the area like a grid. Power towers need to be inspected, and the optimal path generation method is:

First, determine which UAV nest is closer to the power tower, because the inspection path with the short distance is shorter, and a tower only needs to be included once in the inspection task of one UAV nest;

Secondly, according to the above-mentioned route planning method of a single machine nest, the power towers within the coverage area of the machine nest are numbered from the nearest to the farthest from small to large, and the starting tower of the route planning is from the largest number, that is, the distance from the UAV machine nest. Start with the farthest tower, and judge whether the tower is a single-base route mission or a multi-base route mission by far and near.

If a power tower is contained and covered by two drone nests or multiple drone nests, it is enough to determine the route task of which drone nest the tower belongs to.

It is understandable that the nests deployed in the substation are not limited to one. For example, in order to improve the inspection efficiency, a substation can deploy multiple nests to complete line inspections with different requirements such as different directions or different voltage levels. Such grid deployment in substations further increases the significance and feasibility of nest grid.

When all UAV nests and their corresponding towers are bound, and the route mission planning is completed, each tower only exists in a certain route mission of a certain UAV nest, ensuring no duplicate inspection paths. According to the SN code of the drone nest, the background control terminal will issue only the routes owned by the nest, ensuring the one-to-one correspondence between each route and the drone nest.

Example 4:

Embodiment 4 of the present invention provides a method for judging a UAV task execution environment, using the UAV inspection system of the gridded machine nest described in Embodiment 1 or Embodiment 2, and the method includes:

Obtain the environment information inside the nest and the environment information outside the nest within the perception range;

According to the target nest selected by the position of the drone, the corresponding flight influencing factors are determined according to the flight instructions, and the corresponding flight environment data is retrieved from the environmental information outside the nest; the flight conditions are judged according to the flight environment data, if the flight environment When the data does not meet the flight conditions, control the drone to return;

Determine the corresponding landing influence factors according to the return-to-flight command, so as to retrieve the corresponding landing environment data and return environment data from the environment information outside the nest and the environment information inside the nest of the target nest; The landing method adjusts the environment in the nest according to the data of the returning environment until the drone returns to the target nest.

In this embodiment, the environment information in the nest includes: temperature in the nest, humidity in the nest, and smoke concentration in the nest;

The environmental information in the machine nest is collected by a temperature sensor, a humidity sensor and a smoke sensor;

Among them, the temperature sensor is used to collect the ambient temperature in the nest. When the temperature is lower than the lower limit of the set temperature range, the indoor temperature of the nest can reach the normal working range by controlling the heating function of the air conditioner. When the temperature is higher than the upper limit of the set temperature range, Turn on the cooling function of the air conditioner to make the temperature inside the nest reach the normal working range; the humidity sensor is used to detect the ambient humidity in the nest, and when the humidity in the nest is higher than the set threshold, the dehumidification function of the air conditioner is turned on; the smoke sensor is used to detect Smoke concentration in the nest.

In this embodiment, the environmental information outside the nest includes: wind speed, wind direction, temperature outside the nest, humidity outside the nest, rainfall, air pressure, light intensity and visibility;

The environmental information outside the nest is collected by wind speed sensor, wind direction sensor, temperature sensor, humidity sensor, rain gauge, barometer, photosensitive sensor and visibility sensor;

Among them, the wind speed sensor is used to measure the wind speed at the location of the machine nest; the wind direction sensor is used to measure the wind direction; the temperature sensor is used to measure the ambient temperature; the humidity sensor is used to measure the ambient humidity; Light rain, moderate rain, heavy rain, etc.; the barometer is used to measure the local air pressure; the photosensitive sensor is used to measure the current light intensity; the visibility sensor can continuously output atmospheric visibility.

In this embodiment, the above-mentioned several sensors transmit the collected data through wireless communication.

As an optional implementation manner, the wireless communication may adopt the UWB wireless communication technology, which has the characteristics of low power consumption, high data transmission rate, strong anti-interference ability, and strong penetration ability.

It can be understood that the use of UWB wireless communication is only an achievable implementation manner provided in this embodiment, but is not limited to this wireless communication manner. In more embodiments, other wireless communication manners may also be adopted according to the actual situation on site. , such as 4G, 5G, etc.

It can be understood that this embodiment only enumerates data types and sensor types commonly used in the centralized field. In more embodiments, sensor types can be added or deleted according to actual conditions.

In this embodiment, various types of sensors are used to collect the internal and external environment data of the machine nest, and the collected sensory data is preprocessed. The preprocessing includes: preprocessing the sensor data through a sliding average low-pass filter. Processing, filtering out jumping or abnormal environmental information, and obtaining relatively stable environmental information after preprocessing;

As an optional implementation, the moving average low-pass filter model is to take the output of the N-point moving average filter: y(n)=[x(n-N+1)+x(n-N+2) ...+x(n)]/N.

In this embodiment, the influencing factors are divided according to the mission instructions, so as to judge the flight conditions in combination with the required influencing factors for different mission instructions;

As shown in Figure 18, specifically, the task instructions include UAV storage, UAV charging, UAV inspection, NEST self-inspection, NEST switch action, NEST ON state, UAV flight task, no Human-machine precision landing, UAV alternate landing, etc.;

Specifically, the main influencing factors of drone storage, drone charging, and self-checking of the drone nest are the environmental information in the drone nest, including the temperature in the drone nest, the humidity in the drone nest, and the smoke concentration in the drone nest;

The main influencing factors of drone inspection include: wind speed, wind direction, temperature outside the nest, rainfall, barometer, light intensity, and visibility;

The main influencing factors of the switch action of the nest are the rainfall and the smoke concentration in the nest;

The main influencing factors in the UAV flight mission are wind speed, wind direction, barometer, and visibility;

The main factors affecting the precision landing of UAV are wind speed, wind direction, light intensity and visibility;

The main influencing factors of UAV alternate landing are wind speed and wind direction.

As an optional implementation, taking the UAV storage task as an example, combined with the environmental influence factors under the task conditions, the task suitability condition judgment is carried out through the threshold judgment method, as shown in Figure 19:

Obtain the temperature in the nest, the humidity in the nest and the smoke concentration in the nest;

Preset temperature threshold, humidity threshold and smoke threshold;

Determine whether the temperature in the nest meets the temperature threshold condition, if not, the temperature in the nest is abnormal;

If it is satisfied, it is judged whether the humidity in the nest meets the humidity threshold condition, if not, the humidity in the nest is abnormal;

If it is satisfied, the smoke threshold is used to judge whether there is smoke in the nest. If so, the smoke in the nest is abnormal. If not, the internal environment of the nest is normal, and the drone can be returned to the warehouse normally.

As an optional implementation, the current judgment result and abnormal factors are packaged into message information for push, and the output of the corresponding task adopts U8 type data to represent the current judgment result and abnormal factors, wherein 01 is the task number, and the following 8-bit data Used to indicate the judgment result, the judgment conclusion is the comprehensive environmental judgment result 0 is abnormal, 1 is suitable; followed by the sensor judgment conclusion, 0 is the current environmental item abnormal, otherwise the environment is suitable, when the environmental judgment result is 1, the sensor judges The result is all 1. Otherwise, it can be judged which environment does not meet the current task requirements through the register data of the sensor location, and the message information under different tasks can be formed by analogy. The judgment result can be called directly according to the current task status, and whether to execute task decision.

As an optional implementation manner, the packet information is sent externally at a rate not lower than the set rate.

As an optional implementation, the above method can be applied to a single-machine nest and UAVs that perform flight tasks within the sensing range of a single-machine nest, including:

Obtain the internal environment information of the single-machine nest and the external environment information of the single-machine nest within the perception range of the single-machine nest;

Determine the corresponding flight influencing factors according to the flight instructions, so as to retrieve the corresponding flight environment data from the external environment information of the single-unit nest; determine the flight conditions according to the flight environment data, and control the drone to return if the flight environment data does not meet the flight conditions. ;

Determine the corresponding landing influencing factors according to the return-to-flight command, so as to retrieve the corresponding landing environment data and return environment data from the external environment information and internal environment information of the single-unit nest; control the landing method of the UAV according to the landing environment data, according to the return The warehouse environment data adjusts the environment in the nest until the drone returns to the nest.

In this embodiment, the method of one nest and one aircraft is adopted, and the flight tasks of the drone are all within the perception range of the nest, so the nest can collect the environmental information of the drone during the flight mission and the return flight in real time, Thereby making a conditional judgment.

As an optional implementation, if the drone flies out of the sensing range of the nest, the method of using multiple nests deployed in a grid and the drone performing the flight task within the sensing range of the nest, the specific method includes: :

Obtain the environment information inside the nest and the environment information outside the nest within the perception range;

According to the target nest selected by the position of the drone, the corresponding flight influencing factors are determined according to the flight instructions, and the corresponding flight environment data is retrieved from the environmental information outside the nest; the flight conditions are judged according to the flight environment data, if the flight environment When the data does not meet the flight conditions, control the drone to return;

Determine the corresponding landing influence factors according to the return-to-flight command, so as to retrieve the corresponding landing environment data and return environment data from the environment information outside the nest and the environment information inside the nest of the target nest; The landing method adjusts the environment in the nest according to the data of the returning environment until the drone returns to the target nest.

In this embodiment, the distance between the nests does not exceed the sensing distance, that is, if the drone flies out of the sensing range of one of the nests, it will fall within the sensing range of the other nest. The position of the drone is used to determine whether the drone falls within the perception range of the nest, and the nest that falls within the perception range is used as the target nest, and the target nest collects the environmental information of the drone during the flight mission and returning home;

If the sensing ranges of the two nests overlap, according to the distance between the drone and the nest, the nest with the closest distance will be the target nest.

In this embodiment, the UAV flight condition judgment process of the above method specifically includes:

Receive sensor information and task instructions;

According to the task instructions, determine the task category and decompose the task;

If it is an inspection command, determine whether the current environment outside the nest is suitable for the inspection task; if not, terminate the task and upload the reason for the termination of the task;

If it is suitable to perform the task, carry out the self-check of the nest;

After passing the self-check of the nest, the drone takes off and performs the inspection task;

During the execution of the inspection task, determine whether there are adverse inspection conditions in the external environment;

If the external environment is not suitable for flying or the drone receives a return instruction, the drone will return and determine whether the current environment meets the precise descent conditions;

If the precision descent is satisfied, execute the drone precision descent, and at the same time judge whether the nest is suitable for the storage and charging of the drone. If the environment in the nest is abnormal, adjust the environment in the nest until the drone can be charged and stored;

If the precision landing is not satisfied, the drone will be alternately landed;

If the UAV alternate landing is not met, the UAV will be forced to land, and the forced landing status and adverse factors will be uploaded.

In this embodiment, the process of adjusting the environment in the nest includes:

If the temperature in the nest is lower than the lower limit of the set temperature range, the temperature in the nest can reach the normal working range by controlling the heating function of the air conditioner;

When the temperature inside the nest is higher than the upper limit of the set temperature range, turn on the cooling function of the air conditioner to make the temperature inside the nest reach the normal working range;

When the humidity in the nest is higher than the set threshold, turn on the dehumidification function of the air conditioner;

If the smoke in the nest is abnormal, the drone will be deployed.

Example 5:

As shown in FIG. 20 , Embodiment 5 of the present invention provides a precise landing control method for an unmanned aerial vehicle, using the UAV inspection system of the gridded machine nest described in Embodiment 1 or Embodiment 2, including the following processes :

Obtain the positioning data of the UAV;

According to the obtained positioning data, determine whether the UAV is within the preset landing range. When the UAV is within the preset landing range, execute the next step; otherwise, control the UAV to move until the position requirements are met;

When the drone is located at the first preset distance from the landing point, acquire the image data or video data below the drone, and control the drone when the precise descent range code is identified according to the acquired image data or video data. Descend the second preset distance, and execute the next step; otherwise, control the drone to descend the third preset distance, and perform the precision descent range code recognition again until the precise descent range code is identified;

Obtain the image data or video data below the drone again, and when the precise descent position code is identified according to the image data or video data obtained again, control the drone to descend to the fourth preset distance from the landing point, and control the unmanned aerial vehicle. The man-machine landed.

In this embodiment, the precision drop range code and the precision drop position code are next to each other, one large and one small, the larger one is called the fine drop range code, which is used at high altitudes and is mainly used to determine the approximate location of the drone landing. Land, adjust posture. The small one is called the precision drop position code. When it reaches low altitude, the drone begins to recognize it, and it continuously adjusts the pose and finally landed at the small drone fine drop position code.

The method described in this embodiment firstly uses the UAV RTK technology to make the UAV return to the landing point quickly and accurately after completing the task. RTK technology can make the flight error of the UAV reach centimeter level, so that the unmanned The drone can quickly return to the landing point without needing to update the coordinates multiple times; when reaching the landing range, turn on the camera to search for the landing range code, receive the image and complete the recognition within 0.7s. Return to the drone to adjust the posture, and land to a height of 20 cm. Blind landing, to achieve complete automation of UAV inspection tasks.

Example 6:

Embodiment 6 of the present invention provides a UAV inspection method based on visual movement tracking, using the UAV inspection system of the gridded machine nest described in Embodiment 1 or Embodiment 2, wherein:

The drone is loaded with a three-axis gimbal, an RTK positioning module and a front-end AI processing module, and a camera and a video camera are installed on the three-axis gimbal; the camera is a monocular zoom camera; the camera is used to obtain the video information of the tower; wherein , the camera and camcorder are integrated in one lens.

RTK positioning module, used to locate the three-dimensional coordinate information of UAV;

The front-end AI processing module is used to fit the UAV flight control data, RTK positioning module data and the zoom camera to collect images, issue flight control commands to control the UAV flight, control the PTZ to adjust the camera angle and zoom, and lock the inspection target And take pictures; use the visual zoom wide-angle camera to take pictures during the flight close to the hovering point, calculate the coordinate value (GPS value) of the photographed photo and the attitude of the gimbal, and identify the inspection target in the photo through the camera imaging principle; The current GPS position and 3D speed of the UAV and the roll angle, pitch angle and yaw angle of the gimbal's attitude are adjusted by the Kalman filter algorithm to adjust the position of the UAV's gimbal, and the zoom camera is locked to the target viewing point of the tower through zooming; Finally, take pictures to complete the information collection of the tower target inspection point, thereby improving the accuracy of the inspection target information collection and the quality of the collected images.

During the flight between the UAV entering the inspection target and leaving the inspection target, the UAV always flies according to the set track, and uses the Kalman filter algorithm to fit the current position and speed data to adjust the gimbal attitude and camera in real time. The zoom enables the camera to track the movement of the inspection target and lock the shooting.

When using the visual movement tracking method to control the rotation of the gimbal, the gimbal has m degrees of freedom, the angular velocity of the gimbal rotation is w=[w ₁ ,...,w _m ], and the linear velocity of the end is v=[ v ₁ ,..., _vm ], the two have the following relationship:

v=J _v ×w

in,

Calculate the rotation matrix R _cw from the geodetic coordinate system to the camera coordinate system:

分别为相机云台姿态的翻滚角、俯仰角和偏航角，根据相机的初始朝向，还需左乘一个初始旋转R_cw0，此时：Among them, the subscript cw represents the abbreviation of the transformation from the geodetic coordinate system to the camera coordinate system, and R _cwx (φ), R _cwy (θ), and R _cwz (ψ) represent that from the camera coordinate system to the geodetic coordinate system, it is necessary to revolve around x, y, the matrix for the z-axis rotation,

are the roll angle, pitch angle and yaw angle of the camera's gimbal attitude, respectively. According to the initial orientation of the camera, an initial rotation R _cw0 needs to be multiplied to the left. At this time:

R _cw =R _cw0 ×(R _cwx (φ)×R _cwy (θ)×R _cwz (ψ))

In the formula,

As shown in Figure 21, the specific steps include:

S1: According to the inspection requirements, before the drone enters the hovering point at a constant speed, the monocular zoom (telephoto mode) camera on the gimbal is used to obtain the real-time wide-angle image of the inspection target, and the next step is entered;

S2: Determine whether the inspection target is located in the real-time image obtained by shooting, and if so, go to S4; otherwise, the "O" shape controls the gimbal attitude to search for the inspection target in the real-time image, and goes to the next step after searching;

S3: The front-end AI processing module uses the Kalman filter algorithm to fit the position of the UAV, the attitude position of the gimbal and the camera to be adjusted according to the real-time image of the inspection target position, the shooting position of the UAV, the attitude of the three-axis gimbal and other information. In focal length mode, go to S2;

S4: Adjust the monocular zoom (close focus mode) camera on the gimbal before the drone reaches the hovering point at a constant speed. The front-end AI processing module adjusts the three-axis gimbal in real time according to the three-dimensional direction of the drone flying at a constant speed. posture, to lock the inspection target at the central position of the real-time image of the monocular zoom camera, and go to the next step;

S5: The drone reaches the hovering point position, that is, the frontal direction in front of the inspection target. Confirm the central position of the real-time image of the monocular zoom camera to lock the viewing point and take a photo, and go to the next step;

S6: The camera shooting is completed, the front-end AI processing module processes the photos, controls the drone to perform the next hovering point task, and re-executes S1 until all hovering point shooting is completed and it returns to home safely;

Further, the specific process of the S2 is:

The Faster-RCNN algorithm is used to input the picture into CNN for feature extraction; then it is judged whether there is an inspection target in the picture.

Further, the specific process of the S3 is:

In this step, it is assumed that the inspection target object has been identified in the image through S2;

S3.1: Determine the rotation direction of the gimbal according to the position of the target object at the viewing point in the image. The rotation direction of the gimbal is the direction in which the tower is offset to the center of the image; first rotate the gimbal by the smallest unit to obtain the tower at the current position image and extract its features;

S3.2: Match the features of the two images before and after, and calculate the offset of the matching point in the pixel point;

S3.3: According to the linear mapping relationship between the feature offset and the rotation of the gimbal, the rotation of the gimbal is obtained.

S3.4: Adjust the gimbal attitude according to the rotation amount, and execute S2 again.

Further, the specific process of S4 is:

Assuming that the initial state is as described in step 3, the inspection target has been positioned at the center of the camera image;

S4.1: Calculate the current UAV position and the three-dimensional vector direction P of the upcoming motion through RTK and accelerometer on the UAV;

S4.2: Adjust the motion vector of the gimbal camera to be equal to the motion vector of the UAV, and in the opposite direction;

S4.3: According to the method described in S4.2, calculate the offset of the camera's central target on the pixel at the current moment. If there is no offset, it is considered that the pan-tilt camera moves and tracks the inspection target object is relatively static, otherwise it enters Next step;

S4.4: According to the linear mapping relationship between the pixel feature offset at the center of the image and the rotation of the gimbal, the rotation of the gimbal is obtained, and then the gimbal camera is fine-tuned, and the center of the gimbal camera is re-locked to the target object of the viewing point.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (23)

1. The utility model provides an unmanned aerial vehicle system of patrolling and examining of meshing machine nest which characterized in that:

a plurality of nests comprising a grid deployment, each nest for housing at least one drone;

the nest comprises a nest controller communicated with the control terminal, the nest controller is communicated with the unmanned aerial vehicle remote controller, and the unmanned aerial vehicle remote controller is communicated with the unmanned aerial vehicle;

the control terminal is used for obtaining the routing inspection targets corresponding to the nests by taking the shortest routing inspection time as an optimization target according to the current endurance mileage of the unmanned aerial vehicle and the distance between the routing inspection target and each nest, generating the optimal routing inspection path of each unmanned aerial vehicle according to the determined routing inspection target and sending the optimal routing inspection path to the nest controller.

2. The unmanned aerial vehicle inspection system of meshed nests of claim 1, wherein:

the nest comprises:

the device comprises a machine nest main body, and a bearing mechanism, a vertical fixing mechanism and a transverse fixing mechanism which are arranged in the machine nest main body, wherein the bearing mechanism comprises a telescopic landing platform and a first motor, and the landing platform is driven by the first motor;

the vertical fixing mechanism comprises a first centering rod, one end of the first centering rod is arranged on the side wall of the machine nest main body through a rotating shaft, a gear is arranged on the first centering rod, a rack meshed with the gear is arranged on the landing platform, and the first centering rod is driven to rotate around the rotating shaft through the meshing of the gear and the rack;

the transverse fixing mechanism comprises a rotating rod, a second centering rod and a second motor, wherein two ends of the rotating rod are arranged on the side wall of the machine nest main body, and the second centering rod is arranged on the rotating rod; the rotating rod is driven by the second motor to rotate along the opposite direction of the moving direction of the descending platform relative to the machine nest main body, so that the second centering rod is driven to move along the vertical direction of the moving direction of the descending platform.

3. The unmanned aerial vehicle inspection system of meshed nests of claim 2, wherein:

under the meshing of the gear and the rack, the first centering rods on two sides rotate around the shaft, so that the other ends of the first centering rods move towards the middle position or open towards the directions of two sides.

4. The unmanned aerial vehicle inspection system of meshed nests of claim 3, wherein:

two ends of the rotating rod are respectively provided with a second centering rod, when the landing platform is driven to reset, the rotating rod rotates in the forward direction, and the second centering rods at the two ends move to the middle position along the rotating rod so as to transversely restrain the solid unmanned aerial vehicle;

through the meshing of rack and gear, drive the first time pole of both sides and remove to the intermediate position to vertical restraint unmanned aerial vehicle.

5. The unmanned aerial vehicle inspection system of meshed nests of claim 3, wherein:

the two ends of the rotating rod are respectively provided with a second centering rod, when the landing platform is pushed out of the nest main body, the rotating rod rotates reversely, and the second centering rods at the two ends move towards the two sides along the rotating rod so as to release the transverse constraint on the unmanned aerial vehicle;

through the meshing of rack and gear, drive the first time pole of both sides and open to both sides to remove the vertical restraint to unmanned aerial vehicle.

6. The unmanned aerial vehicle inspection system of meshed nests of claim 1, wherein:

the nest comprises:

the device comprises a machine nest main body, a charging module and an energy storage module, wherein the machine nest main body internally comprises an unmanned aerial vehicle position, the charging module and the energy storage module;

the aircraft nest main part is provided with the installing module, and the installing module adopts lead screw formula automatic locking structure to fix the aircraft nest main part, and the unmanned aerial vehicle position is provided with at level and vertical direction autonomous shock absorbing unmanned aerial vehicle fixing device, and the aircraft nest controller communicates with charging module and installing module respectively.

7. The unmanned aerial vehicle inspection system of meshed nests of claim 6, wherein:

the screw rod type automatic locking structure comprises a sleeve and a double-output-shaft motor fixed at the center of the second sleeve, wherein two ends of a rotor of the double-output-shaft motor are respectively connected with a screw rod, the other end of the screw rod is connected with one end of a spring slide block through a threaded hole, the spring slide block moves linearly along with the rotation of the screw rod and drives a telescopic rod fixedly connected with the other end of the spring slide block to stretch.

8. The unmanned aerial vehicle inspection system of meshed nests of claim 1, wherein:

the unmanned aerial vehicle is provided with a three-axis holder, an RTK positioning module and a front-end AI processing module;

a camera and a video camera are arranged on the triaxial holder; the camera is a monocular zoom camera; the camera is used for acquiring video information of the tower; wherein, the camera and the video camera are integrated in one lens.

The RTK positioning module is used for positioning the three-dimensional coordinate information of the unmanned aerial vehicle;

the front-end AI processing module is used for fitting flight control data of the unmanned aerial vehicle, data of the RTK positioning module and images acquired by the zooming camera, issuing flight control commands to control the unmanned aerial vehicle to fly, controlling the tripod head to adjust the angle and zooming of the camera, locking the inspection target and taking pictures; when the inspection target is not located at the central position of the camera image, the rotation of the holder is controlled by adopting a visual movement tracking mode, and the rotation direction of the holder is determined according to the position of the inspection target in the image.

9. An unmanned aerial vehicle inspection method of a gridded nest is characterized by comprising the following steps:

the method comprises the following steps:

acquiring the distance between a polling target and each nest;

selecting a nest closest to the inspection target as an optimal nest;

sequentially judging each routing inspection target to obtain the corresponding routing inspection target of each nest;

routing inspection task planning of any nest, comprising:

carrying out routing inspection target numbering according to the distance between a routing inspection target and the machine nest within the range of the machine nest, wherein the longer the distance is, the larger the number is;

when the difference value between the total cruising time of the unmanned aerial vehicle and the time when one polling target is independently polled is smaller than the minimum value of the time when other polling targets are independently polled, taking the polling target as a single-base-tower task;

if not, judging whether the sum of the time from the nest to the current routing inspection target, the routing inspection time of the current routing inspection target, the routing inspection time from the current routing inspection target to the nearest secondary routing inspection target with the number smaller than that of the current routing inspection target, the time from the current routing inspection target to the secondary routing inspection target and the time from the secondary routing inspection target to the nest is larger than the total cruising time of the unmanned aerial vehicle or not, and if so, taking the routing inspection target as a single-base-tower task; otherwise, executing the route task of the two base towers, and sequentially polling the current polling target and the secondary polling target.

10. The unmanned aerial vehicle inspection method of a meshed nest of claim 9, wherein:

and judging the comprehensive inspection time of the next-level node, when the comprehensive inspection time is greater than the total duration of the unmanned aerial vehicle, only executing the current inspection task, and otherwise, continuing to judge the comprehensive inspection time of the next-level node.

11. An unmanned aerial vehicle task execution environment judgment method is characterized by comprising the following steps:

the unmanned aerial vehicle inspection system utilizing the meshed nest of any of claims 1-8, comprising:

acquiring environment information in the nest and environment information outside the nest in a sensing range;

determining corresponding flight influence factors according to a target nest selected by the position of the unmanned aerial vehicle and a flight instruction, calling corresponding flight environment data from environment information outside the nest, judging flight conditions according to the flight environment data, and controlling the unmanned aerial vehicle to return if the flight environment data does not meet the flight conditions;

determining corresponding landing influence factors according to the return flight instruction so as to call corresponding landing environment data and return bin environment data from the environment information outside the nest and the environment information inside the nest of the target nest; and controlling the landing mode of the unmanned aerial vehicle according to the landing environment data, and adjusting the environment in the nest according to the warehouse returning environment data until the unmanned aerial vehicle returns to the target nest.

The selection of the target cell includes: judging the sensing range of the nest where the unmanned aerial vehicle is located according to the position of the unmanned aerial vehicle, taking the nest falling into the sensing range as a target nest, and if the sensing ranges of the two nests are overlapped, taking the nest closest to the unmanned aerial vehicle as the target nest according to the distance between the unmanned aerial vehicle and the nest.

12. The method for determining the task execution environment of the unmanned aerial vehicle as claimed in claim 11, wherein:

the intra-cell environment information includes: temperature in the nest, humidity in the nest and smoke concentration in the nest;

the environment information outside the cell includes: wind speed, wind direction, temperature outside the nest, humidity outside the nest, rainfall, air pressure, illumination intensity and visibility.

13. The method for determining the task execution environment of the unmanned aerial vehicle as claimed in claim 12, wherein:

in the process of determining corresponding flight influence factors according to flight instructions, the flight instructions comprise unmanned aerial vehicle routing inspection, nest switching actions and unmanned aerial vehicle flight tasks;

unmanned aerial vehicle patrols and examines flight influence factor that corresponds and includes: wind speed, wind direction, temperature outside the nest, rainfall, air pressure, illumination intensity and visibility;

flight influencing factors corresponding to the actions of the nest switch comprise: rainfall and concentration of smoke in the nest;

the flight influencing factors corresponding to the flight mission of the unmanned aerial vehicle comprise: wind speed, wind direction, air pressure and visibility.

14. The method for determining the task execution environment of the unmanned aerial vehicle as claimed in claim 12, wherein:

in the process of determining corresponding landing influence factors according to the return instructions, the instructions influenced by the return instructions comprise: unmanned aerial vehicle storage, unmanned aerial vehicle charging, nest self-checking, unmanned aerial vehicle fine landing and unmanned aerial vehicle standby landing;

the landing influence factors corresponding to unmanned aerial vehicle storage, unmanned aerial vehicle charging and nest self-checking include: temperature in the nest, humidity in the nest and smoke concentration in the nest;

the landing influence factor that unmanned aerial vehicle precision landing corresponds includes: wind speed, wind direction, illumination intensity and visibility;

the landing influence factor that unmanned aerial vehicle is equipped with to land and corresponds includes: wind speed and wind direction.

15. The method for determining the task execution environment of the unmanned aerial vehicle as claimed in claim 11, wherein:

if the current flight instruction is an unmanned aerial vehicle inspection instruction and the current flight environment data meet flight conditions, performing nest self-inspection, and taking off the unmanned aerial vehicle to execute an inspection task after the nest self-inspection is passed; in the unmanned aerial vehicle inspection task execution process, whether flight environment data meet flight conditions or not is continuously detected.

16. The method according to claim 15, wherein:

judging whether the current landing environment data meet a fine landing condition, if so, executing fine landing of the unmanned aerial vehicle, and simultaneously judging whether the nest meets the storage and charging of the unmanned aerial vehicle, and if the environment in the nest is abnormal, adjusting the environment in the nest; if the fine landing is not satisfied, executing the standby landing of the unmanned aerial vehicle; if the unmanned aerial vehicle is not satisfied with the standby landing, the unmanned aerial vehicle is forced to land.

17. The utility model provides an accurate landing control method of unmanned aerial vehicle which characterized in that:

the unmanned aerial vehicle inspection system utilizing the meshed nest of any of claims 1-8, comprising:

acquiring positioning data of the unmanned aerial vehicle;

judging whether the unmanned aerial vehicle is located within a preset landing range or not according to the acquired positioning data, and executing the next step when the unmanned aerial vehicle is located within the preset landing range; otherwise, controlling the unmanned aerial vehicle to move until the position requirement is met;

when the unmanned aerial vehicle is located at a position which is a first preset distance away from a landing point, acquiring image data or video data below the unmanned aerial vehicle, and when a fine landing range code is identified according to the acquired image data or video data, controlling the unmanned aerial vehicle to descend for a second preset distance, and executing the next step; otherwise, controlling the unmanned aerial vehicle to descend for a third preset distance, and identifying the fine descent range code again until the fine descent range code is identified;

and acquiring image data or video data below the unmanned aerial vehicle again, and controlling the unmanned aerial vehicle to descend to a position which is a fourth preset distance away from the descent point when the fine descent position code is identified according to the acquired image data or video data again, so as to control the unmanned aerial vehicle to descend.

18. An unmanned aerial vehicle accurate landing control method according to claim 17, wherein:

and calculating the actual distance and angle from the unmanned aerial vehicle to the landing point according to the camera internal reference and the camera external reference obtained by camera calibration.

19. An unmanned aerial vehicle accurate landing control method according to claim 17, wherein:

camera calibration, comprising:

acquiring shot images of the checkerboard by a camera at different angles;

detecting characteristic points in the image such as the calibration board angular points to obtain pixel coordinate values of the calibration board angular points, and calculating to obtain physical coordinate values of the calibration board angular points according to the size of the checkerboard and the origin of a world coordinate system;

solving an internal reference matrix and an external reference matrix according to the obtained physical coordinate values;

solving distortion parameters according to the obtained internal reference matrix and external reference matrix;

and optimizing the distortion parameters by using an L-M algorithm.

20. An unmanned aerial vehicle inspection method based on visual mobile tracking is characterized in that:

the unmanned aerial vehicle inspection system utilizing the meshed nest of claim 8, comprising:

s1: according to the inspection requirement, before the unmanned aerial vehicle enters a detection point at a constant speed, an image acquisition module on a holder is adopted to acquire a real-time wide-angle image of an inspection target;

s2: judging whether the inspection target is positioned in the shot real-time image, if so, entering the step S3; otherwise, controlling the cradle head to move, and changing the posture until the routing inspection target in the real-time image is searched;

s3: the processing module adopts a Kalman filtering algorithm to fit the unmanned aerial vehicle shooting position and the holder attitude position according to the information of the patrol target position, the unmanned aerial vehicle shooting position and the holder attitude in the real-time image, and determines the focal mode of the image acquisition module;

s4: controlling the unmanned aerial vehicle to fly to the shooting position obtained through calculation at a constant speed, and in the flying process, reversely adjusting the posture of the holder in real time by the processing module according to the three-dimensional direction of the unmanned aerial vehicle flying at the constant speed so as to lock the routing inspection target in the set area of the real-time image of the image acquisition module and adjust the focal mode of the image acquisition module;

s5: when the unmanned aerial vehicle reaches the shooting position, confirming that the inspection target position is in the set area of the real-time image of the image acquisition module, and locking the inspection point for image acquisition;

s6: the processing module processes the acquired pictures, controls the unmanned aerial vehicle to execute the next detection point task and re-executes S1 until all detection point image acquisition tasks are completed.

21. The unmanned aerial vehicle inspection method based on visual mobile tracking of claim 20, wherein:

in S2:

the specific process of judging whether the polling target is positioned in the shot and acquired real-time image comprises the following steps: inputting the picture into CNN by adopting a Faster-RCNN algorithm for feature extraction; and then judging whether the picture has the routing inspection target or not.

22. The unmanned aerial vehicle inspection method based on visual mobile tracking of claim 20, wherein:

in S3:

s3.1: determining the rotation direction of the holder according to the position of the viewpoint target object in the image, wherein the rotation direction of the holder is the direction which enables the tower to deviate towards the center of the image; firstly, rotating the cradle head by a minimum unit to obtain a tower image at the current position and extracting the characteristics of the tower image;

s3.2: matching the characteristics of the front and the rear images, and calculating the offset of the matching point at the pixel point;

s3.3: obtaining the rotation quantity of the holder according to the linear mapping relation between the characteristic offset and the rotation quantity of the holder;

s3.4: and adjusting the posture of the holder according to the rotation amount.

23. The unmanned aerial vehicle inspection method based on visual mobile tracking of claim 20, wherein:

in S4:

s4.1: calculating the current position of the unmanned aerial vehicle and the direction P of the three-dimensional vector to be moved according to the position information and the acceleration information of the unmanned aerial vehicle;

s4.2: adjusting the motion vector of the pan-tilt image acquisition module to be just equal to the motion vector of the unmanned aerial vehicle in magnitude and opposite in direction;

s4.3: calculating the offset of a central target object of the image acquisition module at the current moment on a pixel, if no offset exists, considering that the tripod head image acquisition module moves to track and patrol the target object in a relatively static state, and if no offset exists, entering the next step;

s4.4: and obtaining the rotation quantity of the holder according to the linear mapping relation between the characteristic offset of the central pixel of the image and the rotation quantity of the holder, then finely adjusting the position of the holder image acquisition module, and locking the central part of the holder image acquisition module to the target object of the inspection point again.

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