Automatic cabin entering method, device and equipment for large unmanned aerial vehicle transport vehicle and medium

Abstract

The invention discloses an automatic cabin entering method, device and equipment for a large unmanned aerial vehicle transport vehicle and a medium, and the method comprises the steps: obtaining the current attitude information of a target transport vehicle based on a high-speed camera, and enabling the current attitude information to comprise coordinates and a current orientation angle; according to the current attitude information, to-be-adjusted parameters of the target transport vehicle and the longitudinal displacement distance of a guide rail of a cabin are determined, and the to-be-adjusted parameters comprise an accelerator and a rotation angle; and after the target transport vehicle is controlled to enter a cabin according to the parameters to be adjusted and the longitudinal displacement distance of the guide rail, a brake starting point and a brake terminal point are determined according to a vehicle body identifier of the target transport vehicle, and the cabin entering process of the target transport vehicle is completed at the brake terminal point. The invention belongs to the field of transport vehicle cabin entering. According to the invention, the unmanned vehicle can accurately enter the cabin.

Description

Technical Field

[0001] This invention relates to the field of transport vehicle loading, and more particularly to a method, apparatus, equipment, and medium for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle. Background Technology

[0002] Large drones, due to their massive size and complex structure, typically need to be disassembled into components such as the fuselage and wings during relocation and transportation. These components are then loaded onto specialized transport vehicles and pushed into standard containers for transport by road, rail, sea, or air. However, because the entire system is heavy and has high inertia, the traditional method of manually or using simple winches to push the drones into containers is not only labor-intensive and inefficient, but also poses safety hazards such as inaccurate positioning and damage from impacts. This method cannot meet the urgent needs of modern drones for rapid relocation, efficient containerization, and safe and reliable operation in scenarios such as flight testing, emergency response, or operational deployment. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, and medium for automatic loading of large unmanned aerial vehicle (UAV) transport vehicles, which solves the technical problem of low loading accuracy of transport vehicles in the prior art and achieves improved technical effect of automatic loading.

[0004] In a first aspect, the present invention provides an automatic loading method for a large unmanned aerial vehicle (UAV) transport vehicle, comprising: Based on a high-speed camera, the current attitude information of the target transport vehicle is acquired, including coordinates and current orientation angle. Based on the current attitude information, determine the parameters to be adjusted for the target transport vehicle and the longitudinal displacement distance of the guide rail of the compartment. The parameters to be adjusted include throttle and steering angle. After the target transport vehicle enters the cabin using the parameters to be adjusted and the longitudinal displacement distance of the guide rail, the braking start point and braking end point are determined according to the vehicle body identification of the target transport vehicle, and the target transport vehicle entry process is completed at the braking end point.

[0005] Furthermore, based on the current attitude information, the parameters to be adjusted for the target transport vehicle are determined, including: Constructing the kinematic model of the target transport vehicle includes: A method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle, characterized in that, based on the current attitude information, the parameters to be adjusted for the target transport vehicle are determined, including: Constructing the kinematic model of the target transport vehicle includes: , in, For the target transport vehicle in the global coordinate system Instantaneous velocity in direction, For the target transport vehicle in the global coordinate system Instantaneous velocity in direction, The current facing angle. For throttle, For the corner; Define the error, including: , in, This represents the longitudinal error of the vehicle coordinate system. This represents the lateral error of the vehicle coordinate system. It is a two-dimensional rotation matrix. For the global coordinate system Directional error, Global coordinate system Directional error, For transpose; Control laws include: , , in, For speed proportional gain, Let be the Euclidean norm of the positional error. This is the proportional gain for the heading angle error. The difference between the target heading angle and the current heading angle. This is the gain proportional to the lateral error.

[0006] Further, the longitudinal displacement distance of the guide rail of the compartment is determined, including: , in, This represents the longitudinal displacement distance of the guide rail. This represents the current longitudinal distance of the guide rail. The wheelbase of the target transport vehicle.

[0007] Further, determine whether the parameter to be adjusted is available, including: If the following conditions are met: , in, For the target transport vehicle in the global coordinate system Axis position, For the goal Axis position, For the target transport vehicle in the global coordinate system Axis position, For the goal Axis position, as well as All are preset thresholds; The parameters to be adjusted are then available; Otherwise, recalculate the parameters to be adjusted.

[0008] Furthermore, based on the vehicle identification markings of the target transport vehicle, the braking start point and braking end point are determined, including: The first camera in the control compartment identifies the vehicle identification mark, and when the vehicle identification mark is identified, the position is used as the braking start point; The second camera in the control compartment identifies the vehicle body markings. When identifying the vehicle body markings, the location is taken as the braking endpoint, wherein the first camera is located closer to the compartment door than the second camera.

[0009] Furthermore, upon reaching the braking initiation point, the target transport vehicle is controlled to decelerate, including: , in, The current speed of the target transport vehicle. This represents the distance already traveled. This refers to the length of the compartment.

[0010] Furthermore, it also includes: After the target transport vehicle enters the cabin, the control compartment door is closed.

[0011] Secondly, the present invention provides an automatic cabin entry device for a large unmanned aerial vehicle transport vehicle, comprising: The attitude information acquisition module is used to acquire the current attitude information of the target transport vehicle based on a high-speed camera. The current attitude information includes coordinates and the current orientation angle. The adjustment module is used to determine the parameters to be adjusted for the target transport vehicle and the longitudinal displacement distance of the guide rail of the compartment based on the current attitude information. The parameters to be adjusted include throttle and rotation angle. The packing module is used to control the target transport vehicle to enter the cabin based on the parameters to be adjusted and the longitudinal displacement distance of the guide rail. It then determines the braking start point and braking end point according to the vehicle body identification of the target transport vehicle, and completes the target transport vehicle entering the cabin process at the braking end point.

[0012] Thirdly, the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to execute a method for automated loading of a large unmanned aerial vehicle (UAV) transport vehicle, as provided in the first aspect.

[0013] Fourthly, the present invention provides a non-transitory computer-readable storage medium, wherein when the instructions in the non-transitory computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform an automatic cabin entry method for a large unmanned aerial vehicle transport vehicle as provided in the first aspect.

[0014] One or more technical solutions provided in this invention have at least the following technical effects or advantages: This invention provides a high-precision, high-reliability method for automated loading of large unmanned aerial vehicle (UAV) transport vehicles. It acquires vehicle posture in real-time using a high-speed camera, dynamically adjusts throttle and steering using a kinematic model and feedback control law, and precisely determines the braking start and end points using vehicle identification markers and a visual triggering mechanism from dual cameras inside the vehicle. A nonlinear deceleration strategy is then employed to achieve smooth docking. Furthermore, adaptive adjustment of the longitudinal displacement of the guide rails is introduced, ensuring compatibility with different vehicle models. This method eliminates the need for complex sensors, is low-cost, robust, and offers good positioning repeatability, effectively solving the problems of low efficiency and large errors associated with manual docking. It is suitable for unmanned rapid loading operations in scenarios such as airports, ports, and ships. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating an automatic loading method for a large unmanned aerial vehicle (UAV) transport vehicle provided by the present invention. Figure 2 This is a schematic diagram of the automatic cabin entry of the unmanned aerial vehicle provided by the present invention; Figure 3 This is a schematic diagram of the QR code installation inside the container cabin provided by the present invention; Figure 4 This is a schematic diagram of the drone's cabin entry status provided by the present invention. Detailed Implementation

[0017] This invention provides an automatic loading method for large unmanned aerial vehicle (UAV) transport vehicles, which solves the technical problem of low loading accuracy of transport vehicles in the prior art.

[0018] The technical solution of this invention is to solve the above-mentioned technical problems, and the overall idea is as follows: A method for automatically entering a large unmanned aerial vehicle (UAV) transport vehicle includes: acquiring the current attitude information of the target transport vehicle based on a high-speed camera, wherein the current attitude information includes coordinates and current orientation angle; determining the parameters to be adjusted for the target transport vehicle and the longitudinal displacement distance of the guide rail of the container based on the current attitude information, wherein the parameters to be adjusted include throttle and turning angle; controlling the target transport vehicle to enter the container using the parameters to be adjusted and the longitudinal displacement distance of the guide rail; determining the braking start point and braking end point based on the vehicle's body identifier; and completing the target transport vehicle entry process at the braking end point.

[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0020] First, it should be clarified that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] This invention provides, for example Figure 1 The method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle, as shown, includes steps S11-S13: Step S11: Based on the high-speed camera, acquire the current attitude information of the target transport vehicle, wherein the current attitude information includes coordinates and current orientation angle.

[0022] Step S11 involves using a high-speed camera system deployed inside the cabin or in the entrance area to perform real-time visual perception of the target transport vehicle performing the cabin entry task, so as to accurately obtain its current spatial pose state.

[0023] Specifically, a high-speed camera refers to an industrial-grade camera with high frame rate image acquisition capabilities. It can effectively overcome motion blur and capture clear and stable image frames while the transport vehicle is moving at a certain speed, thereby ensuring the accuracy and robustness of subsequent visual processing.

[0024] In practical applications, high-contrast, distortion-resistant visual markers with unique codes are usually pre-set on the bottom of the transport vehicle or at specific structural locations. The physical size and relative position of the markers in the global coordinate system have been pre-determined through calibration.

[0025] When the transport vehicle enters the camera's field of view, the high-speed camera continuously captures images of the markings on the ground below or on the vehicle body, and transmits the image stream to the vehicle control unit (VCU) or shore-based processing unit. Computer vision algorithms process images in real time, using perspective projection models or analytical methods based directly on marked geometric features to calculate the two-dimensional position coordinates of the transport vehicle in the global coordinate system (usually a two-dimensional plane coordinate system established with the container guide rail entrance or a fixed point inside the compartment as the origin) and the current orientation angle, i.e., the angle between the vehicle's longitudinal axis and the global X-axis, in radians or degrees.

[0026] The coordinates reflect the absolute position of the transport vehicle's center point (or reference point) on the plane, while the orientation angle represents the yaw state of its driving direction.

[0027] Step S12: Based on the current attitude information, determine the parameters to be adjusted for the target transport vehicle and the longitudinal displacement distance of the guide rail of the compartment, wherein the parameters to be adjusted include throttle and rotation angle; Specifically, it includes: Constructing the kinematic model of the target transport vehicle includes: , in, For the target transport vehicle in the global coordinate system Instantaneous velocity in direction, For the target transport vehicle in the global coordinate system Instantaneous velocity in direction, The current facing angle. For throttle, For the corner; Define the error, including: , in, This represents the longitudinal error of the vehicle coordinate system. This represents the lateral error of the vehicle coordinate system. It is a two-dimensional rotation matrix. For the global coordinate system Directional error, Global coordinate system Directional error, For transpose; Control laws include: , , in, For speed proportional gain, Let be the Euclidean norm of the positional error. This is the proportional gain for the heading angle error. The difference between the target heading angle and the current heading angle. This is the gain proportional to the lateral error.

[0028] This invention constructs a simplified kinematic model of a transport vehicle, abstracting the complex vehicle motion into planar rigid body motion driven by linear velocity and angular velocity, thereby establishing a clear mathematical relationship between control input and pose change.

[0029] Based on this, the position error in the global coordinate system is transformed to the vehicle coordinate system through a rotation matrix to obtain the longitudinal and lateral errors. This allows the controller to understand the target's position in front / back, left / right from the vehicle's own perspective, which is more in line with actual driving logic.

[0030] Subsequently, the proportional feedback control law achieves a smooth approach behavior where the speed increases as the distance increases and decreases as the distance increases. At the same time, it works in conjunction with heading error and lateral deviation to correct the steering, ensuring that the vehicle completes position alignment and direction correction simultaneously as it approaches the target.

[0031] This invention decouples the control into two channels: speed and steering. By using the error components in the vehicle coordinate system for lateral correction, the system can effectively suppress lateral drift and improve the alignment accuracy of the guide rail.

[0032] Determine the longitudinal displacement distance of the guide rails of the compartment, including: , in, This represents the longitudinal displacement distance of the guide rail. This represents the current longitudinal distance of the guide rail. The wheelbase of the target transport vehicle.

[0033] By calculating the difference between the current position of the guide rail and the wheelbase of the transport vehicle, the amount of guide rail movement required to ensure that the wheels of the transport vehicle accurately fall into the guide rail limit groove or align with the parking reference can be obtained. This guides the guide rail drive mechanism in the cabin to make pre-adjustments, ensuring that the transport vehicle can be smoothly embedded into the guide rail system during the automatic cabin entry process, and achieving mechanical positioning and structural locking.

[0034] Determining whether the parameter to be adjusted is available includes: If the following conditions are met: , in, For the target transport vehicle in the global coordinate system Axis position, For the goal Axis position, For the target transport vehicle in the global coordinate system Axis position, For the goal Axis position, as well as All are preset thresholds; The parameters to be adjusted are then available; Otherwise, recalculate the parameters to be adjusted.

[0035] The judgment mechanism is used to verify whether the currently calculated control parameters are within a valid and safe execution range. Its logic is: only when the transport vehicle is close enough to the target position and the orientation deviation is small enough, is the currently generated control command considered reliable and usable to drive the vehicle to complete the final alignment.

[0036] Specifically, the system calculates whether the Euclidean distance between the current position of the transport vehicle and the target position is less than the preset position tolerance threshold A, and at the same time checks whether the absolute value of the heading angle error is less than the angle tolerance threshold B. If both conditions are met, it means that the vehicle has entered the "convergence region" near the target. At this time, the parameters output by the control law can guide the vehicle to stop smoothly and accurately, so it is judged as "usable". Conversely, if either error exceeds the limit, it means that the current state deviates far from the expected trajectory. Continuing to use the existing parameters may lead to oscillation, overshoot, or misalignment. It is necessary to recalculate new control commands based on the latest pose information, thereby forming a closed-loop feedback correction mechanism to ensure the robustness and success rate of the automatic cabin entry process.

[0037] Step S13: After the target transport vehicle enters the cabin by controlling the parameters to be adjusted and the longitudinal displacement distance of the guide rail, determine the braking start point and braking end point according to the vehicle body identification of the target transport vehicle, and complete the target transport vehicle entering the cabin process at the braking end point.

[0038] Specifically, it includes: The first camera in the control compartment identifies the vehicle identification mark, and when the vehicle identification mark is identified, the position is used as the braking start point; The second camera in the control compartment identifies the vehicle identification mark. When identifying the vehicle identification mark, the position is taken as the braking endpoint. The first camera is located closer to the compartment door than the second camera.

[0039] Furthermore, based on the vehicle identification markings of the target transport vehicle, the braking start point and braking end point are determined, including: The first camera in the control compartment identifies the vehicle identification mark, and when the vehicle identification mark is identified, the position is used as the braking start point; The second camera in the control compartment identifies the vehicle body markings. When identifying the vehicle body markings, the location is taken as the braking endpoint, wherein the first camera is located closer to the compartment door than the second camera.

[0040] When the vehicle reaches the braking initiation point, control the target transport vehicle to decelerate, including: , in, The current speed of the target transport vehicle. This represents the distance already traveled. This refers to the length of the compartment.

[0041] After the transport vehicle enters the compartment, two cameras installed at fixed positions inside the compartment—the first camera near the door and the second camera further inside—are used to sequentially identify the unique identifier on the transport vehicle's body: When the first camera detects the identifier for the first time, it determines that the transport vehicle has reached the preset braking starting point, and the deceleration process is initiated at this time. The vehicle then continues forward until the second camera recognizes the same identifier, confirming that it has reached the braking endpoint. At this point, the vehicle should stop precisely at the aligned position on the guide rail, completing the cabin entry process.

[0042] To achieve smooth deceleration, nonlinear speed programming is employed: As the vehicle moves deeper into the cabin, the remaining driving distance decreases, resulting in a relatively gentle initial deceleration to avoid impact. However, the deceleration gradient increases significantly as the vehicle approaches the finish line, effectively suppressing inertial forward momentum and achieving a more stable braking effect the closer to the finish line.

[0043] This invention combines a dual-vision trigger point positioning and distance-adaptive nonlinear deceleration strategy, which not only eliminates the reliance on high-precision absolute positioning systems, but also ensures the consistency and repeatability of parking positions through physical markers and geometric relationships. It is suitable for automated cabin entry scenarios of drone transport vehicles in structured cabin environments where docking error requirements are stringent.

[0044] It also includes: after the target transport vehicle enters the cabin, the control compartment door is closed.

[0045] In addition, the inventors also provided corresponding explanations: like Figure 1 As shown, the drone transport vehicle achieves one-click entry and exit from the container. The transport vehicle mainly uses a camera to scan the guide QR code and sends its coordinates, angle information (and model) to the in-vehicle controller VCU. After receiving the signal, the VCU calculates the throttle and turning angle, controls the drive wheel set to precisely align with the guide rail inside the container, and completes the automatic entry and exit from the container.

[0046] The visual guidance PGV module mainly consists of PGV QR code strips, PGV navigation and positioning visual sensors, and PGV readers, and is used to determine the starting and stopping positions of the drone transport vehicle.

[0047] Before entering the container, the drone transport vehicle identifies the starting position code and adjusts its angle and position using the in-vehicle positioner to complete the preparation for entry. The automatic entry command is initiated with a single button press via remote control or programmable command. By reading the QR code information laid out inside the container, the transport vehicle adjusts its position for entry. Once inside the container, the vision module detects the QR code and enters a deceleration phase. The drone transport vehicle automatically stops moving when it detects the stop marker QR code, ensuring safe entry and reducing the difficulty of manual operation.

[0048] like Figure 2 As shown, a QR code can be affixed to the bottom of the container to guide transport vehicles in and out of the container. This includes a two-dimensional track, a position code strip, and a control code strip. The position code strip guides the transport vehicle to precise positioning, and upon detecting the control code strip, it outputs control code information, enabling functions such as pre-entry preparation and stopping upon entry.

[0049] In summary, this invention provides a high-precision, high-reliability method for automatic loading of large unmanned aerial vehicle (UAV) transport vehicles. It acquires vehicle posture in real-time using a high-speed camera, dynamically adjusts throttle and steering using a kinematic model and feedback control law, and precisely determines the braking start and end points using vehicle identification markers and a visual triggering mechanism from dual cameras inside the cabin. A nonlinear deceleration strategy further ensures smooth docking. Simultaneously, it introduces adaptive adjustment of the longitudinal displacement of the guide rails, making it compatible with different vehicle models. This method requires no complex sensors, is low-cost, robust, and has good positioning repeatability, effectively solving the problems of low efficiency and large errors associated with manual docking. It is suitable for unmanned rapid loading operations in scenarios such as airports, ports, and ships.

[0050] Based on the same inventive concept, this invention provides an automatic cabin entry device for a large unmanned aerial vehicle (UAV) transport vehicle, comprising: The attitude information acquisition module is used to acquire the current attitude information of the target transport vehicle based on a high-speed camera. The current attitude information includes coordinates and the current orientation angle. The adjustment module is used to determine the parameters to be adjusted for the target transport vehicle and the longitudinal displacement distance of the guide rail of the compartment based on the current attitude information. The parameters to be adjusted include throttle and rotation angle. The packing module is used to control the target transport vehicle to enter the cabin based on the parameters to be adjusted and the longitudinal displacement distance of the guide rail. It then determines the braking start point and braking end point according to the vehicle body identification of the target transport vehicle, and completes the target transport vehicle entering the cabin process at the braking end point.

[0051] Based on the same inventive concept, the present invention also provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to execute an automated loading method for a large unmanned aerial vehicle (UAV) transport vehicle, as described above.

[0052] Based on the same inventive concept, the present invention also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform an automatic cabin entry method for a large unmanned aerial vehicle transport vehicle as described above.

[0053] Since the electronic device described in this embodiment is an electronic device used to implement the information processing method in the embodiments of the present invention, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the information processing method described in the embodiments of the present invention. Therefore, how the electronic device implements the method in the embodiments of the present invention will not be described in detail here. Any electronic device used by those skilled in the art to implement the information processing method in the embodiments of the present invention falls within the scope of protection of the present invention.

[0054] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0058] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0059] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle, characterized in that, include: Based on a high-speed camera, the current attitude information of the target transport vehicle is acquired, wherein the current attitude information includes coordinates and current orientation angle; Based on the current attitude information, the parameters to be adjusted for the target transport vehicle and the longitudinal displacement distance of the guide rail of the compartment are determined, wherein the parameters to be adjusted include throttle and steering angle; After the target transport vehicle is controlled to enter the cabin using the parameters to be adjusted and the longitudinal displacement distance of the guide rail, the braking start point and braking end point are determined according to the vehicle body identification of the target transport vehicle, and the target transport vehicle entry process is completed at the braking end point.

2. The method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle as described in claim 1, characterized in that, Based on the current attitude information, determine the parameters to be adjusted for the target transport vehicle, including: Constructing the kinematic model of the target transport vehicle includes: ， in, For the target transport vehicle in the global coordinate system Instantaneous velocity in direction, For the target transport vehicle in the global coordinate system Instantaneous velocity in direction, The current facing angle. For throttle, For the corner; Define the error, including: ， in, This represents the longitudinal error of the vehicle coordinate system. This represents the lateral error of the vehicle coordinate system. It is a two-dimensional rotation matrix. For the global coordinate system Directional error, Global coordinate system Directional error, For transpose; Control laws include: ， ， in, For speed proportional gain, Let be the Euclidean norm of the positional error. This is the proportional gain for the heading angle error. The difference between the target heading angle and the current heading angle. This is the gain proportional to the lateral error.

3. The method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle as described in claim 1, characterized in that, Determine the longitudinal displacement distance of the guide rails of the compartment, including: ， in, This represents the longitudinal displacement distance of the guide rail. This represents the current longitudinal distance of the guide rail. The wheelbase of the target transport vehicle.

4. The method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle as described in claim 2, characterized in that, Determining whether the parameter to be adjusted is available includes: If the following conditions are met: ， in, For the target transport vehicle in the global coordinate system Axis position, For the goal Axis position, For the target transport vehicle in the global coordinate system Axis position, For the goal Axis position, as well as All are preset thresholds; The parameters to be adjusted are then available; Otherwise, recalculate the parameters to be adjusted.

5. The method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle as described in claim 1, characterized in that, Based on the vehicle identification markings of the target transport vehicle, determine the braking start point and braking end point, including: The first camera in the control compartment identifies the vehicle identification mark, and when the vehicle identification mark is identified, the position is used as the braking start point; The second camera in the control compartment identifies the vehicle body markings. When identifying the vehicle body markings, the location is taken as the braking endpoint, wherein the first camera is located closer to the compartment door than the second camera.

6. The method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle as described in claim 2, characterized in that, When the vehicle reaches the braking initiation point, control the target transport vehicle to decelerate, including: ， in, The current speed of the target transport vehicle. This represents the distance already traveled. This refers to the length of the compartment.

7. The method for automatic loading of a large unmanned aerial vehicle (UAV) transport vehicle as described in claim 1, characterized in that, Also includes: After the target transport vehicle enters the cabin, the control compartment door is closed.

8. An automatic cabin entry device for a large unmanned aerial vehicle transport vehicle, characterized in that, include: The attitude information acquisition module is used to acquire the current attitude information of the target transport vehicle based on a high-speed camera, wherein the current attitude information includes coordinates and current orientation angle; The adjustment module is used to determine the parameters to be adjusted of the target transport vehicle and the longitudinal displacement distance of the guide rail of the compartment based on the current attitude information, wherein the parameters to be adjusted include throttle and rotation angle; The packing module is used to control the target transport vehicle to enter the cabin according to the parameters to be adjusted and the longitudinal displacement distance of the guide rail, determine the braking start point and braking end point according to the vehicle body identifier of the target transport vehicle, and complete the target transport vehicle entering the cabin process at the braking end point.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute an automated loading method for a large unmanned aerial vehicle (UAV) transport vehicle as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the non-transitory computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform an automatic cabin entry method for a large unmanned aerial vehicle transport vehicle as described in any one of claims 1 to 7.

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