Full-process unmanned transfer system and method based on multi-robot cooperation

Abstract

The invention provides a full-process unmanned transfer system and method based on multi-robot collaboration.The full-process unmanned transfer system comprises a multi-robot collaboration system and a robot control system.The multi-robot collaboration system at least comprises an unmanned tractor, a jacking automatic guiding vehicle, a trailer and a material frame; the robot control system sends task instructions to the unmanned tractor and the jacking automatic guide vehicle and controls the unmanned tractor and the jacking automatic guide vehicle to complete automatic loading and unloading and unmanned transfer work; the unmanned tractor is used for receiving the scheduling task sent by the robot control system, dragging the trailer and the material rack by adopting fusion positioning, and realizing autonomous navigation and operation of the vehicle dragging the material rack and the trailer on a planned route of an indoor and outdoor scene through radar sensing obstacle avoidance; the jacking automatic guide vehicle is used for loading and unloading the materials on the back, adopting radar for navigation and sensing obstacle avoidance, and autonomously running and working on a planned route; the trailer is used as a transport carrier to load the material frame.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent logistics, and particularly relates to a full-process unmanned transfer system and method based on multi-robot collaboration. Background Art

[0002] In the prior art, traditional logistics transfer relies on manual operation of equipment such as forklifts and tractors, which has problems such as low efficiency, high labor costs, and poor coordination. Existing AGV systems are mostly limited to single-scene handling, lacking indoor-outdoor cross-scene collaboration capabilities, and the communication scheduling between devices lacks global optimization.

[0003] Through research on automobile OEMs, in-depth analysis of the current situation and market demands in the automotive industry, it is understood that the automotive industry is currently undergoing a transformation, and the importance of intelligent and digital logistics solutions is becoming increasingly apparent. The application of robots has become a necessary choice in scenarios such as loading and unloading, storage, picking, distribution to the production line, and in-line feeding. Traditional manufacturing plants will gradually transform into digital factories. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems existing in the prior art, and propose a full-process unmanned transfer system and method based on multi-robot collaboration, which is committed to the intelligent overall solution for the logistics industry, and helps customers achieve unmanned logistics to reach the goal of cost reduction and efficiency improvement.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions.

[0006] The full-process unmanned transfer system based on multi-robot collaboration includes a multi-robot collaboration system and a robot control system. Among them,

[0007] The multi-robot collaboration system at least includes an unmanned tractor, a lifting automatic guided vehicle, a trailer, and a rack. The robot control system sends task instructions to the unmanned tractor and the lifting automatic guided vehicle, and controls the unmanned tractor and the lifting automatic guided vehicle to complete automatic loading and unloading and unmanned transfer work. The unmanned tractor receives the scheduling task sent by the robot control system, uses integrated positioning to tow the trailer and the rack, and uses radar to sense obstacles to achieve autonomous navigation and operation of towing the rack and the trailer on the planned route in indoor and outdoor scenarios. The lifting automatic guided vehicle is used to carry materials for loading and unloading, uses radar for navigation and obstacle sensing, and autonomously runs and operates on the planned route. The trailer is used as a transportation carrier and loads at least one rack.

[0008] Further, the unmanned tractor at least includes a main navigation lidar, a blind spot filling lidar, a single-line lidar, an ultrasonic radar, a control panel, and a real-time kinematic positioning module. Among them,

[0009] The main navigation lidar is used to generate a high-precision 3D point cloud map around the vehicle in real time through multi-beam laser scanning, identify medium-distance dynamic and static obstacles, combine with a high-precision map to provide navigation information for the main path of the vehicle for global path planning, and provide global environmental data for the control system;

[0010] The blind spot compensation lidar is used for near-field blind spot coverage, covering the blind spots of the main navigation lidar, tracking dynamic objects, detecting sudden obstacles at close range, and using high-frequency scanning of solid-state lidar to achieve fast response;

[0011] The single-line lidar is used to detect ground markings, tracks or QR code tags through single-line scanning, perform ground feature recognition, and combine with the SLAM algorithm to correct the pose of the vehicle in a structured environment;

[0012] The ultrasonic radar is used to detect obstacles at extremely close range using sound waves;

[0013] The real-time kinematic (RTK) positioning module is used to achieve centimeter-level absolute positioning accuracy through the differential signal between the base station and the vehicle-mounted terminal, providing a stable position reference.

[0014] Furthermore, the unmanned tractor also includes a control screen, which is used as a human-machine interaction interface for status monitoring, real-time display of the status information of the unmanned tractor, support for manual takeover and task parameter adjustment, recording data, and storing operation logs for analysis and algorithm optimization.

[0015] Furthermore, the robot control system at least includes a user control terminal and a background management system. Among them,

[0016] The user control terminal at least includes a workstation terminal, a person-following terminal, and a vehicle-following terminal. The workstation terminal is used to support calling the robot and issuing single-point tasks; the person-following terminal is used to monitor the daily transportation tasks of all robots, and can switch different robots to issue single-point tasks and background multi-point tasks; the vehicle-following terminal is used to bind a single robot and can issue single-point tasks and background multi-point tasks;

[0017] The background management system is used to manage all equipment components in the current factory area, can customize tasks, monitor the running status and task status of all robots, view the operation information of all users, and supervise the logistics situation in the factory area.

[0018] Furthermore, the robot control system also includes a scheduling algorithm module, which includes a multi-objective optimization task allocation model, uses a reinforcement learning algorithm for dynamic task allocation, and executes a dynamic priority adjustment strategy.

[0019] Furthermore, the lifting automatic guided vehicle is equipped with a hydraulic lifting mechanism, which lifts the rack to a specified height, loads / unloads the rack from the trailer, and transports it to a specified work station or storage area.

[0020] Furthermore, the trailer loads the rack and is towed by an unmanned tractor for movement; the rack is a standardized storage unit for carrying goods.

[0021] To achieve the above object, the present invention also provides a full-process unmanned transfer method based on multi-robot collaboration, which applies the full-process unmanned transfer system based on multi-robot collaboration as described above, including:

[0022] The robot control system receives order data and generates a transportation task instruction set;

[0023] According to the transportation task instruction set, control the lifting automatic guided vehicle to grasp the rack through visual positioning and load it on the trailer;

[0024] According to the transportation task instruction set, control the unmanned tractor to autonomously plan the optimal path and tow the fully loaded trailer to the target station;

[0025] At the target station, the lifting automatic guided vehicle triggers the unloading action to complete the goods handover;

[0026] The robot control system updates the equipment status library in real time and dynamically adjusts the task queue.

[0027] To achieve the above object, the present invention also provides an electronic device, including a memory and a processor. A program is stored on the memory and runs on the processor. When the processor runs the program, it executes the steps of the full-process unmanned transfer system based on multi-robot collaboration as described above.

[0028] To achieve the above object, the present invention also provides a computer-readable storage medium, on which computer instructions are stored. When the computer instructions run, they execute the steps of the full-process unmanned transfer system based on multi-robot collaboration as described above.

[0029] The present invention proposes a full-process unmanned transfer system and method based on multi-robot collaboration, which has the following beneficial effects:

[0030] The collaborative work among driverless tractors, trailers, racks, and lifting AGVs is usually based on an intelligent scheduling system and Internet of Things technology, achieving efficient cooperation through task allocation, path planning, and real-time communication. The four types of equipment can form an efficient logistics network of "trunk transportation + last-mile delivery", which is suitable for large-scale and multi-node intelligent manufacturing and warehousing scenarios. The RTK of the driverless tractor and the SLAM positioning of lidar form an "absolute + relative" fusion positioning, avoiding cumulative errors after long-term operation, which is particularly crucial in a featureless environment (such as an open space).

[0031] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0033] Figure 1 It is a schematic diagram of the architecture of the full-process unmanned transfer system based on multi-robot collaboration of the present invention;

[0034] Figure 2 It is a schematic diagram of the overall view of the full-process unmanned transfer system based on multi-robot collaboration of the present invention;

[0035] Figure 3 It is a schematic diagram of the architecture of the driverless tractor in the full-process unmanned transfer system based on multi-robot collaboration of the present invention.

[0036] Wherein:

[0037] 1 - Driverless tractor, 2 - Trailer, 3 - Lifting AGV,

[0038] 4 - Rack, 5 - Robot control system DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not used to limit the present invention.

[0040] Embodiment 1

[0041] Figure 1 It is a schematic diagram of the architecture of the full-process unmanned transfer system based on multi-robot collaboration according to the present invention, as Figure 1As shown in the figure, the full-process unmanned transfer system based on multi-robot cooperation of the present invention includes an unmanned tractor, a lifting AGV (Automated Guided Vehicle), a trailer, a rack, and a robot control system. Among them,

[0042] The unmanned tractor is used for traction by means of 3D-slam (Simultaneous Localization and Mapping) lidar + RTK + IMU fusion positioning, and senses obstacles through lidar and ultrasonic radar, so as to realize the autonomous navigation and operation of the vehicle towing a trailer full of racks on the established route in indoor and outdoor scenarios.

[0043] In this embodiment, the steps of fusing IMU + odometer + RTK include collecting the data of the three sensors respectively to form a calibration rule, and using an algorithm to fuse the data according to the calibration rule. In different situations, different sensors are used as the standard, and the data of the three sensors can be calibrated with each other.

[0044] In this embodiment, through multi-sensor redundant design, hierarchical perception, and tight coupling algorithm, the unmanned tractor realizes highly reliable autonomous operation in a dynamic and complex environment.

[0045] In this embodiment, the unmanned tractor is connected to the background server through WIFI or 4G / 5G communication, receives the scheduling tasks sent by the robot control system, and automatically towes the trailer full of materials from one point to another point.

[0046] Optionally, the unmanned tractor at least includes a main navigation lidar, a blind spot supplement lidar, a single-line lidar, an ultrasonic radar, a control screen, and RTK (Real-time kinematic), among which,

[0047] The main navigation lidar is used for core environment modeling, including generating a high-precision 3D point cloud map around the vehicle in real time through multi-beam (such as 16-line, 32-line, or 64-line) laser scanning. Obstacle detection, including identifying dynamic obstacles (such as pedestrians, vehicles) and static obstacles (such as buildings, road piles) at medium and long distances (50 - 200 meters). Global path planning, including providing navigation information for the main path of the vehicle in combination with a high-precision map.

[0048] In this embodiment, the main navigation lidar, as the "main perception unit", provides global environment data for the control system to ensure the vehicle maintains a safe path in complex scenarios (such as intersections, curves).

[0049] Blind Spot Compensating LiDAR, for covering near-field blind spots: Installed on the side or rear of the vehicle body to cover the blind spots of the main navigation radar (such as under the vehicle body, short-distance dead corners). Dynamic object tracking: Detect sudden obstacles at short distances (5 - 20 meters) (such as suddenly appearing forklifts, low roadblocks). High-frequency scanning: Usually uses solid-state LiDAR (such as 905nm wavelength) to achieve fast response (scanning frequency > 10Hz).

[0050] In this embodiment, the blind spot compensating LiDAR and the main navigation radar form "far and near complementarity", providing redundant perception during vehicle turning, reversing or in dense scenarios to prevent collisions.

[0051] Single-line LiDAR, for identifying low obstacles, covering the blind spots in the stable recognition areas of the blind spot compensating LiDAR and the main navigation LiDAR.

[0052] In this embodiment, ground feature recognition: By single-line scanning (such as horizontally or inclined installation), detect ground markings, tracks or QR code tags. Positioning assistance: Combined with the SLAM (Simultaneous Localization and Mapping) algorithm to correct the vehicle pose in a structured environment (such as a warehouse). Low-cost deployment: Suitable for fixed routes or scenarios with low demand for height information.

[0053] In this embodiment, the single-line LiDAR provides emergency positioning when the main navigation radar fails, or improves positioning accuracy in standardized scenarios (such as between warehouse shelves).

[0054] Ultrasonic radar, for extremely short-distance detection: Use sound waves (frequency 40 - 70kHz) to detect obstacles within 0.1 - 5 meters (such as steps, short piles). All-weather adaptability: Can still work stably in harsh environments such as rain, fog, and dust (but vulnerable to sound wave interference).

[0055] In this embodiment, the ultrasonic radar serves as the "last line of defense" to prevent collisions with extremely small obstacles (such as pets, children's toys) when the vehicle starts, stops, or moves at low speeds.

[0056] RTK (Real-Time Kinematic), for centimeter-level positioning: Through the differential signals (GPS / Beidou) between the base station and the vehicle-mounted terminal, achieve an absolute positioning accuracy of 2 - 5 centimeters. Anti-multipath interference: Provide a stable position reference in open scenarios (such as ports, airports).

[0057] In this embodiment, RTK and the SLAM positioning of LiDAR form an "absolute + relative" fusion positioning to avoid cumulative errors after long-term operation, which is crucial especially in featureless environments (such as open spaces).

[0058] Control panel, for status monitoring: It displays information such as vehicle position, battery level, task progress, and fault alarm in real time. Manual intervention: It supports operators to take over manually and adjust task parameters (such as speed, waypoint setting). Data recording: It stores operation logs for post-event analysis and algorithm optimization.

[0059] In this embodiment, the control panel serves as a human-machine interaction interface, ensuring that operators can intervene quickly in case of emergencies and providing visual support for system debugging.

[0060] In this embodiment, as Figure 3 shown, the collaborative working process of each component of the driverless tractor can be specifically executed as follows:

[0061] (1) Initialization stage:

[0062] RTK obtains the initial vehicle position and loads the pre-stored high-precision map.

[0063] The lidar starts SLAM and fuses with the RTK positioning data to complete self-positioning.

[0064] (2) Dynamic perception stage:

[0065] The main navigation radar constructs a global 3D map and plans the main path.

[0066] The blind spot compensation radar and ultrasonic radar monitor near-field obstacles in real time and trigger local path replanning (such as detouring, decelerating).

[0067] The single-line radar identifies ground features and assists in correcting positioning offsets.

[0068] (3) Control execution stage:

[0069] The control system synthesizes all sensor data and generates motion commands (speed, steering angle).

[0070] The drive motor and steering mechanism execute the traction action and feedback the real-time status through the control panel at the same time.

[0071] (4) Abnormal handling stage:

[0072] If a certain sensor fails (such as the radar being blocked), the system automatically degrades to a backup plan (such as running at a low speed relying on RTK + single-line radar).

[0073] When the ultrasonic radar detects an emergency obstacle, it immediately triggers an emergency stop.

[0074] In this embodiment, through multi-sensor redundant design, hierarchical perception, and tightly coupled algorithms, the driverless tractor realizes highly reliable autonomous operation in a dynamic and complex environment.

[0075] The lifting AGV is used to carry materials for loading and unloading. It uses a 2D lidar for navigation and obstacle perception to achieve autonomous operation and tasks on a predefined route in an indoor scenario.

[0076] In this embodiment, the unmanned tractor is connected to the background server through WIFI or 4G / 5G communication, receives tasks sent by the robot control system, and carries the rack to complete the automatic loading and unloading actions from the trailer (or tow truck).

[0077] Optionally, the lifting AGV includes 2D lidar navigation and visual assisted positioning, has a lifting mechanism (lifting height ≥ 300mm), is equipped with an RFID rack identification system, and supports precise positioning of ±10mm. The lifting mechanism includes an electric lifting mechanism and a hydraulic lifting mechanism.

[0078] The trailer is used to load materials or racks and is towed by a tractor.

[0079] Optionally, the trailer includes a modular detachable structure, is equipped with an electronic locking mechanism, and has an internal cargo weight distribution sensor.

[0080] The rack is a standardized storage unit for carrying goods.

[0081] The robot control system is a multi-robot intelligent scheduling central management system. It adopts a management mode of central management and independent control and scheduling of multiple vehicles to manage the cooperation of multiple robots, realizing functions such as task allocation, path planning, scheduling coordination, traffic control, and robot management of multiple robots. It supports multiple types of navigation modes under complex conditions and reserves a call interface with other auxiliary systems of MES to build an information closed-loop and create an automated and flexible intelligent logistics platform system. The main functions include account login, remote call, real-time monitoring, device management, point deployment, task management, historical data query, system settings, etc.

[0082] The robot control system includes at least a PAD terminal and a background management system. Among them,

[0083] The PAD terminal is for ordinary users. It includes three PAD terminal versions: the workstation terminal, which supports calling the robot and issuing single-point tasks; the person-following terminal, which supports monitoring the daily transportation tasks of all robots on one map, and can switch different robots to issue single-point tasks and background multi-point tasks; the vehicle-mounted terminal, which is for the vehicle-mounted tablet, is only bound to one robot, and can issue single-point tasks and background multi-point tasks.

[0084] The background management system is for administrators and ordinary users. It can manage all equipment components in the current factory area, customize tasks, monitor the running status and task status of all robots, view the operation logs of all ordinary users, etc., and comprehensively supervise the logistics situation in the factory area.

[0085] In this embodiment, the robot control system sends task instructions to the unmanned tractor and the jack-up AGV through the cloud server, thereby completing the entire automatic loading and unloading and unmanned transportation work.

[0086] In this embodiment, the robot control system initiates the task, the lifting AGV automatically loads the goods onto the trailer (or trailer), the unmanned tractor automatically transports the trailer loaded with goods to the designated location, and the lifting AGV automatically unloads the goods from the trailer (or trailer). The whole process is managed and controlled by the robot control system in the background and the machine is dispatched, realizing the instant docking of the unmanned tractor and the lifting AGV, and the whole process does not require human participation.

[0087] In this embodiment, Figure 2 As shown in the figure, the operation process of the full-process unmanned transfer system with multi-robot collaboration can be specifically implemented as follows:

[0088] (1) WMS (Warehouse Management System) or MES (Manufacturing Execution System) generates transportation tasks (such as replenishing production lines and warehousing finished products). The scheduling system allocates resources based on task type, priority, and equipment status.

[0089] (2) The top-lift AGV is responsible for picking up materials from the warehouse shelves, confirming the rack information through RFID / QR code scanning, synchronizing data with the dispatching system, and moving the rack to the handover area. After receiving the command, the unmanned tractor tows an empty trailer to the handover area to load the rack. The tractor tows the trailer to the handover area and stops accurately through visual or laser positioning. The top-lift AGV places the rack on the trailer (or loads with the assistance of a robotic arm). The dispatching system verifies that the loading is complete and updates the trailer status to "full load". The trailer is used as a transport carrier to load multiple racks (standardized adaptation is required).

[0090] (3) The unmanned tractor tows the trailer to the target area (such as production line, storage area) along the planned path (magnetic stripe, QR code or SLAM navigation). When multiple vehicles work together, the dispatching system dynamically optimizes the path to avoid congestion.

[0091] (4) After the tractor arrives at the unloading point, the jack-up AGV or another AGV lifts the material rack and moves it out of the trailer and transports it to the designated workstation or storage area.

[0092] (5) The unmanned tractor tows the empty trailer back to the charging station or waiting area to prepare for the next task. After the jack-up AGV completes unloading, it returns to its initial position and waits for orders. The entire process data is uploaded in real time and visualized through the digital twin system.

[0093] The full-process unmanned transfer system and method based on multi-robot collaboration proposed by the present invention, the scheduling system monitors the device positions in real time, avoids conflicts (such as deadlocks and congestion), and performs dynamic path planning. It uses UWB, laser SLAM or vision positioning technology to ensure high-precision docking between the tractor and the trailer, and between the AGV and the rack. It realizes the sharing of device status (such as rack ID, task progress) through 5G / Wi-Fi. When a device fails, the system automatically reassigns tasks or triggers a manual intervention process. The sizes and connection methods (such as hooks, latches) of the trailers and racks are unified. Combining the real-time order requirements and the device power, the task priorities are dynamically adjusted. In complex scenarios (such as narrow channels), the AGV collaborates with manual forklifts. Through the above process, the four types of devices can form an efficient logistics network of "trunk transportation + last-mile delivery", which is applicable to large-scale, multi-node intelligent manufacturing and warehousing scenarios.

[0094] The present invention also provides an electronic device, including a memory and a processor. A program is stored on the memory and runs on the processor. When the processor runs the program, it executes the steps of the above-mentioned full-process unmanned transfer method based on multi-robot collaboration.

[0095] The present invention also provides a computer-readable storage medium, on which computer instructions are stored. When the computer instructions run, they execute the above-mentioned full-process unmanned transfer method based on multi-robot collaboration. For the full-process unmanned transfer method based on multi-robot collaboration, refer to the introduction in the foregoing part and will not be elaborated here.

[0096] Those of ordinary skill in the art can understand that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A fully unmanned transfer system based on multi-robot collaboration, characterized in that, It includes a multi-robot cooperation system and a robot control system. Among them, the multi-robot cooperation system at least includes an unmanned tractor, a lifting automated guided vehicle, a trailer and a rack. The robot control system sends task instructions to the unmanned tractor and the lifting automated guided vehicle, and controls the unmanned tractor and the lifting automated guided vehicle to complete automatic loading and unloading and unmanned transfer work; the unmanned tractor receives the scheduling tasks sent by the robot control system, uses integrated positioning to tow the trailer and the rack, senses obstacles through radar, and realizes the autonomous navigation and operation of towing the rack and the trailer on the planned route in indoor and outdoor scenarios; the lifting automated guided vehicle is used to carry materials for loading and unloading, uses radar for navigation and obstacle sensing, and operates and works autonomously on the planned route; the trailer is used as a transport carrier to load the rack.

2. The full-process unmanned transfer system based on multi-robot collaboration according to claim 1, wherein The unmanned tractor at least includes a main navigation lidar, a blind spot filling lidar, a single-line lidar, an ultrasonic radar, a control screen and a real-time kinematic positioning module. Among them, the main navigation lidar is used to generate a high-precision 3D point cloud map around the vehicle in real time through multi-beam laser scanning, identify medium-distance dynamic obstacles and static obstacles, and combine with the high-precision map to provide navigation information for the main path of the vehicle for global path planning and provide global environment data for the control system; the blind spot filling lidar is used for near-field blind spot coverage, covering the blind spots of the main navigation radar, tracking dynamic objects, detecting sudden obstacles at close range, and realizing fast response by using high-frequency scanning of solid-state lidar; the single-line lidar is used to identify low obstacles, covering the blind spots of the identification areas of the blind spot filling lidar and the main navigation lidar; the ultrasonic radar is used to detect obstacles at extremely close range by using sound waves; the real-time kinematic positioning module is used to integrate the inertial measurement unit, the odometer and the real-time kinematic differential positioning system to provide position information for the vehicle.

3. The fully automated transfer system based on multi-robot collaboration according to claim 2, wherein The unmanned tractor also includes a control screen, which is used as a human-machine interaction interface for status monitoring, real-time display of the status information of the unmanned tractor, support for manual takeover and task parameter adjustment, data recording, storage of operation logs, and used for analysis and algorithm optimization.

4. The full-process unmanned transfer system based on multi-robot cooperation according to claim 1, characterized in that, The robot control system at least includes a user control terminal and a background management system. Among them, the user control terminal at least includes a workstation terminal, a person-following terminal and a vehicle-following terminal. The workstation terminal is used to support calling robots and issuing single-point tasks; the person-following terminal is used to monitor the daily transportation tasks of all robots, and can switch different robots to issue single-point tasks and background multi-point tasks; the vehicle-following terminal is used to bind a single robot and can issue single-point tasks and background multi-point tasks; the background management system is used to manage all equipment components in the current factory area, can customize tasks, monitor the running status and task status of all robots, view the operation information of all users, and supervise the logistics situation in the factory area.

5. The full-process unmanned transfer system based on multi-robot collaboration according to claim 1, wherein, The robot control system also includes a scheduling algorithm module, which is used to include a multi-objective optimization task allocation model, uses a reinforcement learning algorithm for dynamic task allocation, and executes a dynamic priority adjustment strategy.

6. The fully unmanned transfer system based on multi-robot collaboration according to claim 1, characterized in that, The lifting automatic guided vehicle is provided with a lifting mechanism, which lifts the rack to a specified height, loads / unloads the rack from the trailer, and transports it to a specified working station or storage area.

7. The full-process unmanned transfer system based on multi-robot cooperation according to claim 1, wherein, The trailer loads the rack and is towed by an unmanned tractor; the rack is a standardized storage unit for carrying goods.

8. A full-process unmanned transfer method based on multi-robot collaboration, which applies the full-process unmanned transfer system based on multi-robot collaboration as described in claims 1-7, characterized in that, It includes: The robot control system receives order data and generates a transportation task instruction set; According to the transportation task instruction set, it controls the lifting automatic guided vehicle to grab the rack through laser SLAM positioning and load it onto the trailer; According to the transportation task instruction set, it controls the unmanned tractor to autonomously plan the optimal path and tow the fully loaded trailer to the target station; At the target station, the lifting automatic guided vehicle triggers the unloading action to complete the goods handover; The robot control system updates the equipment status library in real time and dynamically adjusts the task queue.

9. An electronic device, characterized in that, It includes a memory and a processor. The memory stores a program that runs on the processor. When the processor runs the program, it executes a full-process unmanned transfer method based on multi-robot collaboration as claimed in claim 8.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that, When the computer instructions run, they execute a full-process unmanned transfer method based on multi-robot collaboration as claimed in claim 8.

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