Warehousing system and method

Abstract

The application provides a warehousing system and method, the system comprising: a UAV, an AGV, a parking apron, a field end camera, a field end server; the field end camera is configured to obtain image information of the parking apron; the field end server is configured to plan a path of the AGV and / or control the AGV to travel based on the image information; the AGV is configured to clean foreign matter debris on the parking apron and / or guide the UAV to enter the warehouse. Embodiments of the application perceive scene information by reforming unmanned airport infrastructure and take on the computing overhead by the field end server. In the face of this single special scene of the UAV take-off and landing platform, the scheme reduces the overall complexity of the system, maximizes resource utilization, avoids the problem of insufficient computing power of the on-board chip, and also provides an unmanned cleaning solution, effectively reducing the labor cost overhead required to maintain the safety of the ground environment of the parking apron.

Description

Technical Field

[0001] This application relates to the field of drone storage technology, and more particularly to a storage system and method. Background Technology

[0002] Unstable airflow and other complex environmental factors can easily affect the autonomous landing process of drones, resulting in insufficient landing accuracy and thus affecting subsequent operations such as retrieval and storage. The existing storage function is achieved through the support and movement of a ground-based grid-like support structure, but this method suffers from low efficiency, limited practical value, and complex mechanical structure maintenance.

[0003] Foreign Object Debris (FOD), including metal parts, waterproof plastic sheeting, gravel, broken branches and leaves, poses a significant threat to the safety of takeoff and landing operations on the tarmac. FOD on the tarmac can easily be engulfed by propellers, causing engine failure; debris can also affect the movement of towing vehicles, greatly hindering the scheduling and storage of drones. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a database entry system and method.

[0005] To achieve the above objectives, this application provides an inbound system, characterized in that it includes: a drone, an automated guided vehicle, a helipad, a field camera, and a field server;

[0006] The field camera is configured to acquire image information of the helipad;

[0007] The field server is configured to plan the path of the automated guided vehicle and / or control the movement of the automated guided vehicle based on the image information.

[0008] The automated guided vehicle is configured to clear foreign debris from the helipad and / or guide the drone into the storage area.

[0009] In one possible implementation, the automated guided vehicle is equipped with a cleaning unit;

[0010] The cleaning unit is located in the first direction of the automated guided vehicle and is configured to push foreign debris on the helipad to a foreign debris dumping area set up on the helipad.

[0011] In one possible implementation, the automated guided vehicle is equipped with a first guidance unit;

[0012] The first guidance unit is located in the second direction of the automated guided vehicle and is configured to guide the drone into the warehouse.

[0013] In one possible implementation, the drone is equipped with a second guidance unit;

[0014] The second guiding unit is configured to match the first guiding unit so that it can be guided into the warehouse by the automated guided vehicle.

[0015] Based on the same inventive concept, embodiments of this application also provide a method for storing data using the storage system described in any of the preceding claims, including:

[0016] The image information of the helipad acquired by the field camera is processed to identify the category and location information of at least one of the drones, automated guided vehicles and foreign object debris in the helipad;

[0017] Based on the category, the task type of the automated guided vehicle is determined;

[0018] Based on the location information, the ideal path of the automated guided vehicle is planned;

[0019] Based on the task type and the ideal path, the automated guided vehicle is driven to complete the task corresponding to the task type.

[0020] In one possible implementation, processing the image information of the helipad acquired by the field camera to identify the category and location information of at least one of the drones, automated guided vehicles, and foreign debris in the helipad includes:

[0021] By combining coordinate perspective transformation algorithm and target recognition algorithm to process the image information, the category and location information of at least one of the drones, automated guided vehicles and foreign object debris in the helipad are identified.

[0022] In one possible implementation, the task type includes a foreign debris removal task;

[0023] The process of driving the automated guided vehicle to complete the task corresponding to the task type based on the task type and the ideal path includes:

[0024] Based on the foreign object debris removal task and the ideal path, the automated guided vehicle cleans up foreign object debris on the tarmac.

[0025] In one possible implementation, the task type includes guiding a drone into a warehouse;

[0026] The process of driving the automated guided vehicle to complete the task corresponding to the task type based on the task type and the ideal path includes:

[0027] Based on the task of guiding the drone into the warehouse and the ideal path, the automated guided vehicle guides the drone into the warehouse.

[0028] In one possible implementation, the method further includes:

[0029] The driving path of the automated guided vehicle is corrected by using the location information of the automated guided vehicle fed back in real time by the field camera.

[0030] In one possible implementation, correcting the driving path of the automated guided vehicle (AGV) based on its real-time location information fed back by the field camera includes:

[0031] The position to be reached by the automated guided vehicle along the ideal path within the next preset time period is predicted to obtain the first position;

[0032] Predict the positions that the automated guided vehicle will reach in different directions during the next preset time period to obtain at least one second position;

[0033] Compare the differences between the first position and the second position, and select the position from the second position that is closest to the first position to obtain the third position;

[0034] The driving path of the automated guided vehicle is corrected based on the third position.

[0035] As can be seen from the above, the storage system and method provided in this application include: a drone, an automated guided vehicle (AGV), a helipad, an on-site camera, and an on-site server. The on-site camera is configured to acquire image information of the helipad. The on-site server is configured to plan the path of the AAV based on the image information and / or control the AAV's movement. The AAV is configured to clean up foreign debris on the helipad and / or guide the drone into the storage area. This application embodiment uses modifications to the drone airport infrastructure to perceive scene information, with the on-site server handling the computational overhead. Facing the single, specialized scenario of a drone take-off and landing platform, this solution reduces the overall system complexity, maximizes resource utilization, avoids the problem of insufficient computing power in onboard chips, and provides an unmanned cleaning solution, effectively reducing the manpower costs required to maintain the safety of the helipad ground environment. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1This is a schematic diagram of the warehousing system according to an embodiment of this application;

[0038] Figure 2 This is a schematic diagram of the planar structure of the drone landing pad according to an embodiment of this application;

[0039] Figure 3 This is a schematic diagram of the appearance of the automated guided vehicle according to an embodiment of this application;

[0040] Figure 4 This is a schematic diagram of the appearance of the drone according to an embodiment of this application;

[0041] Figure 5 This is a schematic diagram of the data entry method according to an embodiment of this application;

[0042] Figure 6 This is a schematic diagram of the execution and storage method according to an embodiment of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0044] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0045] As described in the background section, complex environmental factors such as unstable airflow can easily affect the autonomous landing process of UAVs, resulting in insufficient landing accuracy and thus affecting subsequent operations such as retrieval and storage. Current retrieval functions are achieved through the support and movement of a ground-based grid-like support structure, but this method suffers from low efficiency, limited practical value, and complex mechanical structure maintenance.

[0046] Foreign Object Debris (FOD) poses a significant threat to the safety of takeoff and landing operations on the tarmac. FOD on the tarmac can easily be engulfed by propellers, causing engine failure; debris can also affect the movement of towing vehicles, greatly hindering the scheduling and storage of drones.

[0047] Furthermore, existing technologies cannot automatically clean up FOD. The only way to clean it up is to install radar or other detection devices to help drones avoid FOD when landing, which significantly increases the cost of devices that guide drones into the hangar. Alternatively, manual cleaning can be used to keep the tarmac clean, but this cannot guarantee real-time cleaning and greatly increases labor costs.

[0048] Based on the above considerations, this application proposes a storage system and method. The system includes: a drone, an automated guided vehicle (AGV), a helipad, an on-site camera, and an on-site server. The on-site camera is configured to acquire image information of the helipad. The on-site server is configured to plan the path of the AAV based on the image information and / or control the AAV's movement. The AAV is configured to clean up foreign object debris on the helipad and / or guide the drone into the storage area. This application proposes to modify the drone airport infrastructure to perceive scene information, with the on-site server handling the computational overhead. For the single, specialized scenario of a drone take-off and landing platform, this solution reduces the overall system complexity, lowers the hardware cost of the AAV, maximizes resource utilization, and avoids the problem of insufficient computing power in onboard chips. Regarding the FOD safety issue of helipads, the on-site intelligent system provides an unmanned cleaning solution to reduce the manpower costs required to maintain the safety of the helipad ground environment.

[0049] The technical solutions of the embodiments of this application will be described in detail below through specific examples.

[0050] refer to Figure 1 The diagram below is a schematic of the warehousing system according to an embodiment of this application, including: a helipad 2, a field camera 1, a field server 6, an automated guided vehicle (AGV) 5, and a drone 4.

[0051] The field camera 1 is configured to acquire image information of the helipad;

[0052] The field server 6 is configured to plan the path of the automated guided vehicle 5 and / or control the movement of the automated guided vehicle based on the image information.

[0053] The automated guided vehicle 5 is configured to clear foreign debris 3 from the helipad 2 and / or guide the drone 4 into the storage area.

[0054] The field-mounted camera 1 is wired to the field-mounted server 6 via a USB 3.0 interface. The helipad 2 contains broken tree branches and foreign objects (debris fragments 3), and the landing area includes a drone 4, an automated guided vehicle 5 performing a mission, a FOD dumping area 203, and a hangar door 204. The field-mounted camera 1 is mounted above the hangar door 204 at a height of 3.4m, with 2 megapixels, a horizontal field of view of 86°, and a vertical field of view of 80°. It should be noted that those skilled in the art can adjust the aforementioned parameters according to actual conditions, and are not limited to the specific values ​​shown above.

[0055] Further reference Figure 2 This is a schematic diagram of the planar structure of the drone landing pad according to an embodiment of this application.

[0056] Specifically, such as Figure 2 As shown, the helipad 2 includes a drone take-off and landing area 201, an AGV operation area 202, a FOD dumping area 203, and a hangar door 204.

[0057] The drone take-off and landing area 201 is a circle with a radius of 2m, used for drone take-off and landing and accommodating landing errors; the AGV operation area 202 is a 3m wide annular area adjacent to the drone take-off and landing area 201, used to provide space for AGV navigation; the FOD dumping area 203 is a fan-shaped area located at the edge of the apron, with a central angle of 40°, an inner diameter of 5m, and an outer diameter of 6.3m, used to centrally handle foreign objects pushed and swept to this area by the AGV; the hangar door 204 is a narrow strip area, where the arrival of the drone 4 signifies the completion of the hangar entry process. It should be noted that the above specific values ​​are only illustrative examples and can be adjusted according to user needs and the specific dimensions of the drone.

[0058] For the automated guided vehicle 5, a cleaning unit is provided on the automated guided vehicle 5;

[0059] The cleaning unit is located in the first direction of the automated guided vehicle 5 and is configured to push the foreign object fragments 3 on the helipad to the foreign object dumping area 203 set on the helipad 2.

[0060] The automated guided vehicle 5 is equipped with a first guiding unit;

[0061] The first guidance unit is located in the second direction of the automated guided vehicle 5 and is configured to guide the drone 4 into the warehouse.

[0062] For details, please refer to Figure 3 This is a schematic diagram of the appearance of the automated guided vehicle according to an embodiment of this application.

[0063] like Figure 3As shown, the automated guided vehicle 5 includes: a vertical three-section push plate 501, a hard-core brush 502, and a robotic arm 503.

[0064] The automated guided vehicle 5 is 50cm long, 35cm wide, and 30cm high. It adopts a dual front-wheel drive chassis structure, including two pairs of motors, two pairs of steering servos, an electronic speed controller, and a support structure. This chassis is used to switch the drive wheel sets and driving direction according to different operation types, always operating in front-wheel drive. The automated guided vehicle 5 has a minimum turning radius of 60cm and an average driving speed of 0.7m / s. The robotic arm 503 is a pair of single-degree-of-freedom synchronous robotic arms assembled at one end of the vehicle body, which is the first guiding unit mentioned above. The robotic arm 503 is 30cm long, with a distance of 35cm between the two arms. Based on the horizontal plane, it rotates from -10° to +90° and is used to grasp the drone 4. A vertical three-section push plate 501 is fixed to opposite ends of the robotic arm 503, with the central push plate forming a 130° angle with the side push plates to gather foreign object fragments 3. A hard-core brush 502 is attached to the bottom of the push plate to clean up small foreign objects and reduce wear between the push plate structure and the ground. The vertical three-section push plate 501 and the hard-core brush 502 together constitute the cleaning unit. It should be noted that the hard-core brush 502 may not be installed on the cleaning unit and can be selectively installed according to actual needs.

[0065] It should be noted that the aforementioned automated guided vehicle 5 can be configured for bidirectional drive in a straight line or for omnidirectional drive. The above embodiment is merely illustrative. The purpose of setting it to bidirectional drive is to effectively reduce the manufacturing cost of the automated guided vehicle 5. Furthermore, the parameters such as the turning radius and travel speed of the automated guided vehicle 5 are not fixed values ​​and can be adjusted according to actual conditions. In addition, the first guiding unit is not limited to the single-degree-of-freedom synchronous robotic arm 503 shown above. It can be any structure that can match the second guiding unit set on the drone 4, as long as it can successfully guide the drone 4. The above example structure is simple, effectively reducing the matching difficulty between the automated guided vehicle 5 and the drone 4, and further effectively saving the manufacturing cost of the automated guided vehicle 5.

[0066] Furthermore, the cleaning unit and the first guiding unit are positioned opposite each other and are located on the same straight line. In some other embodiments, they can also be positioned at an angle between 0° and 180°, or at any angle. In this embodiment, because bidirectional drive is used, the cleaning unit and the first guiding unit are positioned in two different driving directions, which makes it easier to control the trolley to clean up foreign object fragments 3 and guide the drone 4 into the warehouse.

[0067] Regarding the drone 4, a second guidance unit is provided on the drone 4;

[0068] The second guide unit is configured to match the first guide unit so as to be guided into the warehouse by the automated guided vehicle 5.

[0069] For details, please refer to Figure 4 This is a schematic diagram of the appearance of the drone according to an embodiment of this application.

[0070] like Figure 4 As shown, UAV 4 is a medium-sized fixed-wing VTOL UAV with a fuselage length of 4.2m and a wingspan of 2.1m. The docking device 401 is welded to the lower part of the UAV's nose, representing one specific embodiment of the aforementioned second guiding unit. It is a horizontal tie rod with a width of 40cm, slightly larger than the spacing of the AGV robotic arms 503 to reduce docking accuracy requirements, used for docking with the AGV robotic arms 503. Similarly, the aforementioned second guiding unit can be set to any structure that matches the first guiding unit, as long as it can assist in completing the guiding task of UAV 4.

[0071] As can be seen from the above embodiments, the warehousing system described in this application includes: a drone 4, an automated guided vehicle 5, a helipad 2, a field-end camera 1, and a field-end server 6. The field-end camera 1 is configured to acquire image information of the helipad 2. The field-end server 6 is configured to plan the path of the automated guided vehicle 5 based on the image information, and / or control the movement of the automated guided vehicle. The automated guided vehicle 5 is configured to clean up foreign object debris 3 on the helipad 2, and / or guide the drone 4 into the warehouse. By modifying the drone airport infrastructure to perceive scene information, the field-end server 6 is responsible for the computational overhead. Facing the single specialized scenario of drone take-off and landing platforms, this solution reduces the overall system complexity, reduces the hardware cost of the automated guided vehicle 5, maximizes resource utilization, and avoids the problem of insufficient computing power of onboard chips. Regarding the FOD safety issue of helipads, the field-end intelligence provides an unmanned cleaning solution to reduce the manpower costs required to maintain the safety of the helipad ground environment.

[0072] Based on the same inventive concept, this application also provides an inbound method using the inbound system described in any of the above embodiments, referencing... Figure 5 The above is a schematic diagram of the data entry method in the application embodiment, which specifically includes:

[0073] Step S501: Process the image information of the helipad 2 acquired by the field camera 6 to identify the category and location information of at least one of the drone 4, automated guided vehicle 5 and foreign object debris 3 in the helipad 2.

[0074] Step S502: Based on the category, determine the task type of the automated guided vehicle 5;

[0075] Step S503: Based on the orientation information, plan the ideal path for the automated guided vehicle 5;

[0076] Step S504: Based on the task type and the ideal path, drive the automated guided vehicle 5 to complete the task corresponding to the task type.

[0077] The image information of the helipad 2 acquired by the field camera 1 is processed to identify the category and location information of at least one of the drones 4, automated guided vehicles 5, and foreign debris 3 in the helipad 2, including:

[0078] By combining coordinate perspective transformation algorithm and target recognition algorithm to process the image information, the category and location information of at least one of the drones, automated guided vehicles and foreign object debris in the helipad are identified.

[0079] The task type includes foreign object fragment removal task;

[0080] The process of driving the automated guided vehicle 5 to complete the task corresponding to the task type based on the task type and the ideal path includes:

[0081] Based on the task of removing foreign debris 3 and the ideal path, the automated guided vehicle 5 cleans up the foreign debris 3 on the helipad 2.

[0082] The task types include guiding drones into storage;

[0083] The process of driving the automated guided vehicle 5 to complete the task corresponding to the task type based on the task type and the ideal path includes:

[0084] Based on the task of guiding the drone into the warehouse and the ideal path, the automated guided vehicle 5 guides the drone 4 into the warehouse.

[0085] The method further includes:

[0086] The driving path of the automated guided vehicle 5 is corrected by using the location information of the automated guided vehicle 5 fed back in real time by the field camera 1.

[0087] The method of correcting the driving path of the automated guided vehicle 5 by using the real-time position information fed back by the field camera 1 includes:

[0088] The position to be reached by the automated guided vehicle 5 along the ideal path in the next preset time period is predicted to obtain the first position;

[0089] Predict the positions that the automated guided vehicle 5 will reach in different directions during the next preset time period to obtain at least one second position;

[0090] Compare the differences between the first position and the second position, and select the position from the second position that is closest to the first position to obtain the third position;

[0091] The driving path of the automated guided vehicle 5 is corrected based on the third position.

[0092] For details, please refer to Figure 6 This is a schematic diagram of the execution and storage method of an embodiment of this application.

[0093] First, the field camera 1 acquires image information of the helipad 2. Then, it performs coordinate system perspective transformation and target recognition on the image information. Next, it obtains the coordinates of each unit within the helipad (Automated Guided Vehicle 5, UAV 4, and foreign object debris 3), as well as the orientation (direction information) of the automated guided vehicle 5 and UAV 4. Then, it determines whether foreign object debris 3 exists on the helipad 2. If it does, the target is set as foreign object debris 3, and a foreign object removal task is performed. At this time, the automated guided vehicle 5 automatically cruises to the location of foreign object debris 3 in the direction of the vertical three-section push plate 501, and then pushes the foreign object debris 3 to the foreign object dumping area. If there is no foreign object debris on the helipad at this time, the target is set as UAV 4, and a UAV loading task is performed. At this time, the automated guided vehicle 5 automatically cruises to the nose of the UAV in the direction of its robotic arm, and the robotic arm of the automated guided vehicle 5 docks with the nose pull rod 401 of the UAV 4. Then, the automated guided vehicle 5 automatically cruises in the opposite direction to the hangar door 204, completing the loading task.

[0094] Specifically, the field camera 1 acquires real-time image information of the helipad 2 and transmits the information to the field server 6. The real-time image information at this time may include the drone 4, the automated guided vehicle 5, the helipad 2, and foreign object debris 3 that may be present on the helipad 2.

[0095] Furthermore, the field server 6 first performs a coordinate perspective transformation on the image information input from the field camera 1, and then uses a target recognition algorithm to obtain the category and orientation information of each unit within the helipad 2. Specifically, the coordinate perspective transformation is achieved by rotating and translating the planar image in three-dimensional space and then projecting it back onto the original plane to transform the view plane.

[0096] The target recognition algorithm is a target recognition, classification, and localization algorithm based on convolutional neural networks. The classifier output is the output of each helipad unit, including the automated guided vehicle 5, the drone 4, and the foreign debris 3. The localization module output is the center coordinates of the automated guided vehicle 5, the drone 4, and the foreign debris 3, as well as the orientation of the automated guided vehicle 5 and the drone 4.

[0097] In some embodiments, after the drone 4 lands, the field server 6 takes the image information of the helipad 2 captured by the field camera 1 as input and first executes a coordinate perspective transformation algorithm. The two-dimensional image acquired by the field camera 1 contains the distance and depth relationships of objects in the real world, and the algorithm extracts the state information of objects in the two-dimensional plane of the real-world helipad from it. The field server 6 then executes a target recognition algorithm to calculate the coordinates of the automated guided vehicle 5, the drone 4, and the foreign object debris 3 within the helipad, as well as the direction vectors of the automated guided vehicle 5 and the drone 4.

[0098] Based on the above data, the field camera 1 determines whether there are foreign object debris (FOD) fragments (3) in the helipad 2 to determine the task type of the automated guided vehicle (AGV) 5. If FOD fragments (3) are present, a FOD cleaning task is performed; if no FOD fragments (3) are present, a drone storage task is performed. In this embodiment, the AAV 5 first completes FOD cleaning and then tows the drone 4 into the storage area.

[0099] In some embodiments, when performing the FOD cleaning task, the automated guided vehicle 5 travels along the planned ideal path from its starting position to the target foreign object debris 3 in the direction of the vertical three-section pusher 501. During the process, the field-end camera 1 provides real-time feedback to complete local path correction. After arriving at the foreign object debris 3, the path endpoint is reset to the FOD dumping area 203, and the automatic cruise process is repeated to push the foreign object to the FOD dumping area 203. After the task is completed, it returns to the hangar door 204 to wait for further instructions.

[0100] In some embodiments, when performing the drone loading task, the automated guided vehicle 5 travels along the planned ideal path from the starting position of the automated guided vehicle 5 to the nose of the drone 4, along the direction of the robotic arm 503. During this process, the field-end camera 1 provides real-time feedback to complete local path corrections. After the field-end server 6 detects that the automated guided vehicle 5 has arrived at the drone 4, it controls the automated guided vehicle 5 to lower the robotic arm 503 to dock with the drone's handle 401. After docking is completed, the path endpoint is updated to the hangar door 204, and the automated guided vehicle 5 repeats the automatic cruise process in the opposite direction of the robotic arm 503 to complete the loading.

[0101] In some embodiments, the principle of the above path planning is as follows: a two-dimensional topology is formed based on the distribution of the starting point, ending point, and non-task objectives of the automated guided vehicle 5 within the apron 2; since the ideal planned path consists of two tangent circular arcs, the path planning process is a single-objective dual-decision-variable optimization problem with a determined radius of the two circular arcs, and the constraints are the shortest path and avoiding non-task objectives.

[0102] In some embodiments, the principle of the aforementioned path local correction is as follows: predict the position reached by the automated guided vehicle 5 along the ideal path after the next preset time Δt, and the position reached by turning left or right or going straight, and compare the difference between the two to determine the action of the automated guided vehicle 5 in the next stage, thereby making the automated guided vehicle 5 approach the desired path. For example, if the next position reached by the automated guided vehicle 5 after turning left from the current position and traveling Δt is the shortest distance from the next position reached along the ideal path, then a left-turn correction command is sent to the automated guided vehicle 5.

[0103] The principle of the above target recognition is as follows: First, a region generation network generates a series of candidate target objects and determines the orientation information of each object. Then, a convolutional neural network identifies the specific classification of each candidate target object.

[0104] The methods described above have the same beneficial effects as the corresponding data entry system embodiments, which will not be repeated here.

[0105] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0106] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0108] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0109] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0110] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A warehousing system, characterized in that, include: The system includes a drone, an automated guided vehicle (AGV), a helipad, a field camera, and a field server; the AAV is equipped with a cleaning unit and a first guiding unit; the drone is equipped with a second guiding unit. The field camera is configured to acquire image information of the helipad and transmit the image information to the field server; The field server is configured to plan the path of the automated guided vehicle and control the movement of the automated guided vehicle based on the image information. The field server performs coordinate perspective transformation on the image information input from the field camera, and then uses a target recognition algorithm to obtain the category and orientation information of each unit within the helipad; The automated guided vehicle is configured to clear foreign debris from the helipad and guide the drone into the warehouse; the field camera determines whether there is foreign debris in the helipad to determine the task type of the automated guided vehicle. If foreign object debris is present, a foreign object debris removal task will be performed; if no foreign object debris is present, a drone storage task will be performed.

2. The system according to claim 1, characterized in that, The cleaning unit is located in the first direction of the automated guided vehicle and is configured to push foreign debris on the helipad to a foreign debris dumping area set up on the helipad.

3. The system according to claim 1, characterized in that, The first guidance unit is located in the second direction of the automated guided vehicle and is configured to guide the drone into the warehouse.

4. The system according to claim 3, characterized in that, The second guiding unit is configured to match the first guiding unit so that it can be guided into the warehouse by the automated guided vehicle.

5. A method for warehousing using the warehousing system according to any one of claims 1 to 4, characterized in that, include: The image information of the helipad acquired by the field camera is processed to identify the category and location information of at least one of the drones, automated guided vehicles and foreign debris in the helipad; Based on the category, the task type of the automated guided vehicle is determined; Based on the location information, the ideal path of the automated guided vehicle is planned; Based on the task type and the ideal path, the automated guided vehicle is driven to complete the task corresponding to the task type.

6. The method according to claim 5, characterized in that, The process of processing the image information of the helipad acquired by the field camera to identify the category and location information of at least one of the following on the helipad: (The process includes...) By combining coordinate perspective transformation algorithm and target recognition algorithm to process the image information, the category and location information of at least one of the drones, automated guided vehicles and foreign object debris in the helipad are identified.

7. The method according to claim 5, characterized in that, The task types include foreign object / fragment removal tasks; The process of driving the automated guided vehicle to complete the task corresponding to the task type based on the task type and the ideal path includes: Based on the foreign object debris removal task and the ideal path, the automated guided vehicle cleans up foreign object debris on the tarmac.

8. The method according to claim 5, characterized in that, The task types include guiding drones into storage; The process of driving the automated guided vehicle to complete the task corresponding to the task type based on the task type and the ideal path includes: Based on the task of guiding the drone into the warehouse and the ideal path, the automated guided vehicle guides the drone into the warehouse.

9. The method according to claim 5, characterized in that, The method further includes: The driving path of the automated guided vehicle is corrected by using the location information of the automated guided vehicle fed back in real time by the field camera.

10. The method according to claim 9, characterized in that, The method of correcting the driving path of the automated guided vehicle (AGV) based on the real-time position information fed back by the field camera includes: The position to be reached by the automated guided vehicle along the ideal path within the next preset time period is predicted to obtain the first position; Predict the positions that the automated guided vehicle will reach in different directions during the next preset time period to obtain at least one second position; Compare the differences between the first position and the second position, and select the position from the second position that is closest to the first position to obtain the third position; The driving path of the automated guided vehicle is corrected based on the third position.

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