AGV warehousing trolley and warehousing path planning method thereof

Abstract

The invention discloses an AGV warehousing trolley and a warehousing path planning method thereof, and belongs to the technical field of warehouse logistics, and the AGV warehousing trolley comprises a scheduling planning module, a sensing reconstruction module, a control navigation module, a communication coordination module, a resource distribution module, a positioning adjustment module, a leveling execution module, a recovery energy storage module and an energy management module. According to the method, the efficiency and robustness of the path are improved, conflict detection and priority scheduling among the AGV warehousing trolleys are realized, congestion and collision are reduced, and stable and reliable warehousing action is ensured; and meanwhile, the feasibility and execution stability of a fine tuning path are ensured, dynamic, reasonable and efficient allocation and recovery of resources are realized, the overall scheduling efficiency is improved, and then the task continuity is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of warehouse logistics, and particularly relates to an AGV warehousing trolley and a warehousing path planning method thereof. BACKGROUND

[0002] With the development of intelligent logistics and intelligent warehousing technology, in the modern warehousing logistics system, the automatic guided vehicle has become one of the important equipment to realize the automation and intelligentization of material handling, especially in the intelligent warehouse, the AGV undertakes the key tasks of cargo handling, warehousing, and unloading, etc. The traditional AGV path planning method is mostly based on a static map, and the path does not have dynamic perception ability, which is easy to cause path congestion, resource conflict, poor docking accuracy, etc. in a complex environment, and at the same time, the allocation and management of resources are relatively extensive, and the comprehensive consideration of the task urgency, vehicle state and resource historical use is lacking, in addition, in the process of shelf docking, due to environmental changes and image errors, the AGV is often difficult to accurately complete the warehousing action, which affects the operation efficiency and accuracy.

[0003] The existing AGV warehousing trolley and warehousing path planning method have low path planning efficiency, lack coordination of AGV warehousing trolleys, and have insufficient path fine-tuning precision and single resource management mode, and therefore, an AGV warehousing trolley and a warehousing path planning method are proposed. SUMMARY

[0004] The present application relates to the technical field of warehouse logistics, and particularly relates to an AGV warehousing trolley and a warehousing path planning method thereof.

[0005] In order to achieve the above-mentioned purpose, the technical scheme adopted by the present application is as follows: An AGV warehousing trolley comprises a scheduling planning module, a perception reconstruction module, a control navigation module, a communication coordination module, a resource allocation module, a positioning adjustment module, a leveling execution module, a recycling energy storage module and an energy management module. The scheduling planning module is used to construct a dynamic weight matrix and plan a basic path for the movement of each AGV warehousing trolley. The perception reconstruction module is used to obtain the pose information and surrounding environment information of each AGV warehousing trolley in real time, so as to dynamically generate a driving path. The control navigation module is used to calculate the wheel speed and steering instruction of each AGV warehousing trolley, and control each AGV warehousing trolley to avoid obstacles in real time. The communication coordination module is used to construct a communication basis for task-energy-traffic flow coupling scheduling, and generate a resolution strategy. The resource allocation module is used to dynamically allocate the use authority and time quota of resources according to the reservation request sent by each AGV warehousing trolley. The positioning adjustment module is configured to acquire visual features of the shelves, and calculate pose deviations between each AGV warehouse entry trolley and a target storage location; The leveling execution module is configured to control each AGV warehouse entry trolley to perform dynamic leveling, and execute tray storage and retrieval operations; The waste heat recovery and energy storage module is configured to convert waste heat into electrical energy, and store the generated electrical energy; The energy management module is configured to preheat or assist in heat dissipation of the battery pack of each AGV warehouse entry trolley.

[0006] As a further scheme of the present application, the specific steps of the perception reconstruction module dynamically generating a driving path are as follows: S1.1: Acquire a warehouse static map, and take lane intersection points, shelf gaps, and charging station locations as nodes, and connected lanes between the nodes as edges, then acquire a starting point and a target storage location region in a task instruction, and calculate actual costs from the starting point to corresponding nodes and Manhattan distances from each node to the target storage location, to generate a basic path, wherein a specific calculation formula of the actual cost is as follows: In the formula, denotes the actual cost, denotes a weight coefficient of an edge in the th segment of the path, denotes an actual physical length of an edge in the th segment of the basic path; A specific calculation formula of the Manhattan distance is as follows: In the formula, denotes the Manhattan distance, denotes coordinates of the node in a warehouse plane coordinate system, denotes reference point coordinates of a target storage location region; S1.2: Smoothly process the generated basic path, and simultaneously acquire parameters of a dynamic weight matrix while real-time collecting a current position of each AGV warehouse entry trolley, congestion of surrounding lanes, dynamic obstacle coordinates, an electrical quantity of each AGV warehouse entry trolley, and a task urgency degree; S1.3: Split the basic path into multiple micro-path segments according to a preset interval, calculate a comprehensive score of each micro-path segment, mark each micro-path segment with a score lower than a preset threshold as a to-be-optimized segment, search for each connected path within a preset range around each to-be-optimized segment, to generate multiple alternative micro-path segments; S1.4: calculate the resilience value of each alternative micro-path segment, arrange from high to low, select the first ranked alternative micro-path segment as a replacement path, embed the replacement path into the basic path, and correct the connection points of the replacement path and the basic path through a smoothing algorithm to generate a complete path, wherein the specific calculation formula of the resilience value is: In the formula, denotes the resilience value, denotes the width adaptation coefficient, denotes the historical stability coefficient, denotes the time compatibility coefficient.

[0007] As a further scheme of the present application, the specific steps of the communication coordination module generating a resolution strategy are as follows: S2.1: Real-time extraction of each node in the complete path, time prediction for each path node, generation of the corresponding predicted arrival time window, acquisition of reservation information of each AGV storage car, and synchronization to the external central dispatching system and adjacent AGV storage cars in the path overlap area through a wireless network; S2.2: The receiver writes information into the local path reservation cache table, compares the path node overlap situation, if multiple AGV storage cars appear simultaneously within the time window of the same path node, conflict prediction is performed, and according to the preset division rule, the conflict level of the current AGV storage car is judged according to high, medium and low levels; S2.3: According to the task urgency, energy criticality, path replaceability, path resilience and historical waiting factor of each conflict AGV storage car, calculate the corresponding resource priority score, arrange from high to low, and according to the ranking order, keep the original path of the AGV storage car with a score higher than the preset threshold, and adjust the path of other AGV storage cars.

[0008] As a further scheme of the present application, the specific steps of the positioning adjustment module calculating the pose deviation between each AGV storage car and the target storage location are as follows: S3.1: Collect the front-end structure image data of the goods shelf, obtain coordinate information according to each front-end structure image data of the goods shelf, and convert the image coordinates to the spatial pose in the coordinate system of each AGV storage car through calibration parameters to output the corresponding three-dimensional coarse positioning information; S3.2: Obtain the historical docking pose of each AGV relative to the storage location, and compare it with the self-pose of each AGV storage car and the attitude of the storage location in the three-dimensional coarse positioning information, and calculate the pose deviation of each AGV storage car and the target storage location, wherein the specific calculation formula of the pose deviation is: The specific calculation formula of the position deviation is: wherein, represents the position deviation amount, represents the actual position of the AGV warehouse truck, coordinates, represents the actual position of each AGV warehouse truck, coordinates, represents the expected position of each AGV warehouse truck, coordinates, represents the expected position of each AGV warehouse truck, coordinates; The specific calculation formula of the attitude angle deviation amount is: wherein, represents the attitude angle deviation amount, represents the actual attitude angle of each AGV warehouse truck, represents the expected attitude angle of each AGV warehouse truck; S3.3: Compare the calculated pose deviation amount with the target pose of the goods location, calculate the required compensation adjustment amount for each AGV warehouse truck, and perform a micro correction on the pose of each AGV warehouse truck according to the calculated compensation adjustment amount, wherein the specific calculation formula of the compensation adjustment amount is: wherein, represents the pose compensation adjustment amount, which contains three components corresponding to X-axis position compensation, Y-axis position compensation, and attitude angle compensation in turn, represents the X and Y axis position compensation proportion coefficient, represents the attitude angle compensation proportion coefficient, represents the X and Y coordinates of the target pose of the goods location, the current actual X and Y coordinates of the AGV warehouse truck, the target attitude angle of the goods location, the current actual attitude angle of each AGV warehouse truck.

[0009] An AGV warehouse truck warehousing path planning method, the specific steps of which are as follows: I: Collect and analyze the warehousing task information, and generate the basic path of each AGV warehouse truck according to the warehouse static map; II: Collect the position and surrounding environment information of each AGV warehouse truck in real time, evaluate and adjust the generated basic path; III: Collect the path reservation information sent by each AGV warehouse truck, and perform conflict prediction and coordination according to each path reservation information; IV: Collecting real-time occupation of resources and resource use reservation application of each AGV warehouse car, and performing dynamic quota allocation; V: Calculating pose deviation between each AGV warehouse car and target storage location to generate corresponding fine-tuning path.

[0010] As a further scheme of the present application, the specific steps of step IV for performing dynamic quota allocation are as follows: S4.1: Collect and allocate corresponding identifiers for each type of management resource, real-time detect whether there is an AGV warehouse car occupying resources in the management resource area, simultaneously call the occupation records of each type of resource within 1 hour from the resource local database, and count the average occupation time and peak use period; S4.2: Collect resource reservation requests of each AGV warehouse car, arrange each resource reservation request in order from old to new, then mark each resource reservation request as a to-be-processed state, and then evaluate the priority of each resource reservation request and arrange it in order from high to low; S4.3: Based on the occupation of each type of resource, sort the priority of each resource reservation request, allocate usage quota for each type of resource to generate corresponding quota instructions, and then issue the quota instructions to the corresponding AGV warehouse car, if the AGV warehouse car with allocated quota fails to stop or the task is cancelled, the original allocated quota is recovered and allocated to the AGV warehouse car with the highest priority of resource reservation request.

[0011] As a further scheme of the present application, the specific steps of step V for generating corresponding fine-tuning path are as follows: S5.1: Collect the reference pose data of the target storage location and the image feature points of the shelf, extract the pixel coordinates of the image feature points of the shelf, correct the pixel coordinates of the image feature points of the shelf through the distortion coefficient, and then convert the corrected pixel coordinates into coordinates in a three-dimensional coordinate system through the perspective projection formula; S5.2: Convert the coordinates in the three-dimensional coordinate system into coordinates in the AGV warehouse car coordinate system to obtain the current planar position of each AGV warehouse car, calculate the pose deviation between each AGV warehouse car and the target storage location, and set the constraint parameters of the fine-tuning path according to the physical performance of each AGV warehouse car and the target storage location environment; S5.3: Based on the calculated pose deviation and the set constraint parameters of the fine-tuning path, generate corresponding fine-tuning path, and real-time verify each fine-tuning path, if the verification is passed, the fine-tuning path enters the execution phase, otherwise, return to regenerate, then convert the verified fine-tuning path into drive wheel control instructions and send it to the corresponding each AGV warehouse car, wherein the specific formula for fine-tuning path verification is: In the formula, represent the real-time distance between each AGV warehouse entry trolley and the first peripheral obstacle during fine adjustment, represent the real-time coordinates of the AGV warehouse entry trolley during fine adjustment, represent the coordinates of the first obstacle.

[0012] Compared with the prior art, the beneficial effects of the present application are: 1、The warehouse static map is obtained, the path graph is constructed through the channel intersection, the shelf gap and the charging station, the basic path is generated from the task starting point and the target storage location and is subjected to smoothing processing, the position of each AGV warehouse entry trolley, the channel congestion, the dynamic obstacle, the power and the task urgency are collected in real time, the dynamic weight is formed, the basic path is then split into micro-path segments and scored, the low-scored segment is optimized to generate a complete path, the path nodes are synchronized to the external central scheduling system, the arrival time of each node is predicted, the conflict detection and level judgment are performed, the driving path of each AGV warehouse entry trolley is adjusted based on the priority score, the shelf image is collected for coordinate conversion, the pose deviation is calculated in combination with the historical docking posture, the pose fine adjustment of each AGV warehouse entry trolley is completed, the efficiency and robustness of the path are improved, the conflict detection and priority scheduling among the AGV warehouse entry trolleys are realized, the congestion and collision are reduced, and the warehouse entry action is stable and reliable.

[0013] 2、The use state of various management resources is identified and monitored, the occupation time length and peak period of various resources are counted, the resource quota instruction is generated by evaluating the priority and combining the resource reservation request of each AGV warehouse entry trolley, the efficient allocation and dynamic recovery of resources are realized, the shelf feature points are converted into three-dimensional coordinates through shelf image collection and pixel correction, and then converted into the position under the coordinate system of each AGV warehouse entry trolley, the pose deviation is calculated in combination with the target storage location reference pose, the fine adjustment path is generated and real-time verification is performed according to the performance of each AGV warehouse entry trolley and the surrounding environment, the qualified path is converted into a driving instruction and issued to each AGV warehouse entry trolley, so as to accurately dock and stably execute, ensure the feasibility and execution stability of the fine adjustment path, realize the dynamic, reasonable and efficient allocation and recovery of resources, improve the overall scheduling efficiency, and further ensure the task continuity. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application.

[0015] Figure 1 A system block diagram of an AGV warehouse entry trolley is provided for the present application; Figure 2A flow chart of an AGV warehouse entry trolley warehouse entry path planning method is provided. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0017] Embodiment 1 Reference Figure 1 An AGV warehouse entry trolley includes a scheduling planning module, a perception reconstruction module, a control navigation module, a communication coordination module, a resource allocation module, a positioning adjustment module, a leveling execution module, a recycling energy storage module, and an energy management module. The scheduling planning module is configured to construct a dynamic weight matrix and plan a basic path for movement of each AGV warehouse entry trolley. The perception reconstruction module is configured to obtain real-time pose information and surrounding environment information of each AGV warehouse entry trolley to dynamically generate a driving path.

[0018] Specifically, a warehouse static map is obtained, and a channel intersection point, a shelf gap, and a charging station position are taken as nodes, and a connected channel between the nodes is taken as an edge. A starting point and a target shelf area in a task instruction are obtained, and an actual cost from the starting point to a corresponding node and a Manhattan distance from each node to the target shelf are calculated to generate a basic path. The generated basic path is smoothed, and the current position of each AGV warehouse entry trolley, the congestion of the surrounding channel, the coordinates of the dynamic obstacle, the power of each AGV warehouse entry trolley, and the task urgency are updated to obtain parameters of a dynamic weight matrix. The basic path is split into multiple micro path segments at a predetermined interval, and a comprehensive score of each micro path segment is calculated. Each micro path segment with a score lower than a predetermined threshold is marked as a to-be-optimized segment. Each connected path within a predetermined range around each to-be-optimized segment is searched to generate multiple alternative micro path segments. The resilience value of each alternative micro path segment is calculated and arranged from high to low. The first ranked alternative micro path segment is selected as a replacement path. The replacement path is embedded in the basic path, and the junction of the replacement path and the basic path is corrected by a smoothing algorithm to generate a complete path.

[0019] It should be further noted that the specific calculation formula of the actual cost is as follows: In the formula, represents the actual cost, represents the weight coefficient of the th edge in the path, represents the actual physical length of the th edge in the basic path, The specific calculation formula of the Manhattan distance is as follows: In the formula, denotes Manhattan distance, denotes the node coordinates in the warehouse plane coordinate system, denotes the reference point coordinates of the target storage area . The specific calculation formula of the toughness value is: In the formula, denotes the toughness value, denotes the width adaptation coefficient, denotes the historical stability coefficient, denotes the time compatibility coefficient.

[0020] The control navigation module is used to calculate the wheel speed and steering instruction of each AGV, and to control each AGV to avoid obstacles in real time. The communication coordination module is used to build a communication foundation for task-energy-traffic flow coupling scheduling, and to generate a resolution strategy.

[0021] Specifically, each node in the complete path is extracted in real time, and each path node is time predicted to generate a corresponding predicted arrival time window. At the same time, the reservation information of each AGV is obtained, and is synchronized to the external central scheduling system and adjacent AGVs in the path overlap area through a wireless network. The receiver writes the information into a local path reservation cache table, compares the path node overlap situation, and if multiple AGVs appear in the same path node within the time window, conflict prediction is performed. According to the preset division rule, the conflict level of the current AGV is judged according to high, medium and low levels, and according to the task urgency, energy criticality, path replaceability, path toughness and historical waiting factor of each conflict AGV, the corresponding resource priority score is calculated, and is arranged from high to low. At the same time, according to the ranking order, the original path of the AGV whose score is higher than the preset threshold is reserved, and the paths of other AGVs are adjusted.

[0022] The resource allocation module is used to dynamically allocate the use permission and time quota of resources according to the reservation request sent by each AGV; the positioning adjustment module is used to obtain the visual features of the shelf, and to calculate the pose deviation between each AGV and the target storage location.

[0023] Specifically, image data of the front-end structure of the shelving is collected, and coordinate information is obtained based on the image data of each shelving front-end structure. Then, the image coordinates are converted into spatial poses in the coordinate system of each AGV inbound vehicle through calibration parameters to output the corresponding three-dimensional coarse positioning information. The docking posture of each AGV relative to the cargo location is obtained in history and compared with the self-pose of each AGV inbound vehicle and the pose of the cargo location in the three-dimensional coarse positioning information. The pose deviation between each AGV inbound vehicle and the target cargo location is calculated. The calculated pose deviation is compared with the target pose of the cargo location to calculate the compensation adjustment amount required for each AGV inbound vehicle. The pose of each AGV inbound vehicle is then finely corrected based on the calculated compensation adjustment amount.

[0024] Furthermore, this implementation requires further explanation of the specific formula for calculating the pose deviation: The specific formula for calculating position deviation is as follows: In the formula, This indicates the positional deviation. Indicates the actual position of the AGV (Automated Guided Vehicle) trolley entering the warehouse. coordinate, Indicates the actual position of each AGV (Automated Guided Vehicle) trolley entering the warehouse. coordinate, This indicates the desired position of each AGV (Automated Guided Vehicle) inbound trolley. coordinate, This indicates the desired position of each AGV (Automated Guided Vehicle) inbound trolley. coordinate; The specific formula for calculating attitude angle deviation is as follows: In the formula, Indicates the attitude angle deviation. This indicates the actual attitude angle of each AGV entering the warehouse. This represents the expected attitude angle of each AGV entering the warehouse; The specific formula for calculating the compensation adjustment amount is as follows: In the formula, This represents the pose compensation adjustment amount, which includes three components, corresponding to X-axis position compensation, Y-axis position compensation, and attitude angle compensation, respectively. This indicates the X and Y axis position compensation ratio coefficients. This represents the attitude angle compensation ratio coefficient. The X and Y coordinates represent the orientation of the target cargo location. The current actual X and Y coordinates of the AGV inbound vehicle. Target attitude angle of the cargo location The current actual attitude angle of each AGV entering the warehouse.

[0025] The leveling execution module is used to control each AGV warehouse entry trolley to perform dynamic leveling and execute pallet storage and retrieval operations; the energy recovery and storage module is used to convert waste heat into electrical energy and store the generated electrical energy; the energy management module is used to preheat or assist in cooling the battery packs of each AGV warehouse entry trolley.

[0026] Example 2 Reference Figure 2 A method for planning the inbound path of an AGV (Automated Guided Vehicle) inbound vehicle is described, and the specific steps of the planning method are as follows: Collect and parse the inbound task information, and generate the basic path for each AGV inbound vehicle based on the static warehouse map.

[0027] The system collects the location and surrounding environment information of each AGV entering the warehouse in real time, and evaluates and adjusts the generated basic path.

[0028] Collect the path reservation information sent by each AGV inbound vehicle, predict conflicts based on the path reservation information, and coordinate various conflicts.

[0029] The system collects real-time data on resource occupancy and resource usage requests and reservations from each AGV (Automated Guided Vehicle) for inbound transport, and then dynamically allocates quotas.

[0030] Specifically, the system collects and assigns corresponding identifiers to various management resources, detects in real time whether AGVs are occupying resources within the management resource area, retrieves the occupancy records of various resources within one hour from the local resource database, and calculates the average occupancy time and peak usage periods. It collects resource reservation requests from each AGV and arranges them in ascending order, marking each request as pending. Then, it evaluates the priority of each resource reservation request and arranges them in descending order. Based on the occupancy status of various resources, it prioritizes each resource reservation request, allocates usage quotas to various resources, generates corresponding quota instructions, and sends these instructions to the corresponding AGVs. If an AGV with an allocated quota malfunctions, stops, or has its task canceled, the originally allocated quota is reclaimed and reassigned to the AGV with the highest priority in the resource reservation request.

[0031] Calculate the pose deviation between each AGV inbound trolley and the target storage location to generate the corresponding fine-tuning path.

[0032] Specifically, the system collects the reference pose data of the target storage location and the image feature points of the shelf, extracts the pixel coordinates of the shelf image feature points, corrects the pixel coordinates of the shelf image feature points using distortion coefficients, and then converts the corrected pixel coordinates into coordinates in a three-dimensional coordinate system using perspective projection formulas. The coordinates in the three-dimensional coordinate system are then converted into coordinates in the AGV storage vehicle coordinate system to obtain the current planar position of each AGV unloading vehicle. The system also calculates the pose deviation between each AGV storage vehicle and the target storage location. Simultaneously, based on the physical performance of each AGV storage vehicle and the environment of the target storage location, constraint parameters for fine-tuning the path are set. Based on the calculated pose deviation and the set constraint parameters for the fine-tuning path, a corresponding fine-tuning path is generated, and each fine-tuning path is verified in real time. If the verification passes, the fine-tuning path enters the execution phase; otherwise, it returns to be regenerated. Finally, the verified fine-tuning path is converted into drive wheel control commands and sent to the corresponding AGV storage vehicles.

[0033] It should be further explained that the specific formula for fine-tuning the path verification is as follows: In the formula, This indicates that each AGV's inbound trolley is in close contact with the first AGV during the fine-tuning Real-time distance between each surrounding obstacle The table shows the real-time coordinates of the AGV inbound trolley during the fine-tuning process. Indicates the first The coordinates of the obstacle.

Claims

1. An AGV (Automated Guided Vehicle) for warehouse entry, characterized in that, It includes a scheduling and planning module, a perception and reconstruction module, a control and navigation module, a communication and coordination module, a resource allocation module, a positioning and adjustment module, a leveling and execution module, a recovery and storage module, and an energy management module; The scheduling and planning module is used to construct a dynamic weight matrix and plan the basic path for each AGV to move into the warehouse. The perception reconstruction module is used to acquire the pose information of each AGV entering the warehouse and the surrounding environment information in real time, so as to dynamically generate the driving path. The control and navigation module is used to calculate the wheel speed and steering commands of each AGV inbound vehicle and control each AGV inbound vehicle to avoid obstacles in real time. The communication coordination module is used to build the communication foundation for the coupled scheduling of task-energy-traffic flow and generate resolution strategies; The resource allocation module is used to dynamically allocate resource usage permissions and time quotas based on the reservation requests sent by each AGV inbound vehicle. The positioning adjustment module is used to acquire the visual features of the shelf and calculate the pose deviation between each AGV inbound trolley and the target storage location. The leveling execution module is used to control each AGV inbound trolley to perform dynamic leveling and to perform pallet storage and retrieval operations. The energy recovery and storage module is used to convert waste heat into electrical energy and store the generated electrical energy; The energy management module is used to preheat or assist in cooling the battery packs of each AGV entering the warehouse.

2. The AGV warehouse entry cart according to claim 1, characterized in that, The specific steps of the perception reconstruction module in dynamically generating the driving path are as follows: S1.1: Obtain a static map of the warehouse, using aisle intersections, shelf gaps, and charging station locations as nodes, and connecting aisles between nodes as edges. Then, obtain the starting point and target storage area from the task instructions. Simultaneously, calculate the actual cost from the starting point to the corresponding node and the Manhattan distance from each node to the target storage location to generate a basic path. The specific formula for calculating the actual cost is as follows: In the formula, Indicates the actual cost, Indicates the first in the path The weight coefficient of the segment edge, Indicates the first in the basic path The actual physical length of the segment edge; The specific formula for calculating the Manhattan distance is as follows: In the formula, Indicates distance from Manhattan. Represents a node Coordinates in the warehouse plane coordinate system Indicates the target storage area The coordinates of the reference point; S1.2: Smooth the generated basic path, and then collect the current position of each AGV inbound vehicle, the congestion of the surrounding channels, the coordinates of dynamic obstacles, the battery level of each AGV inbound vehicle, and the task urgency update in real time, while obtaining the parameters of the dynamic weight matrix. S1.3: Divide the basic path into multiple micro-path segments according to a preset interval, calculate the comprehensive score of each micro-path segment, mark each micro-path segment with a score lower than a preset threshold as a segment to be optimized, and search for each connected path within a preset range around each segment to be optimized to generate multiple candidate micro-path segments. S1.4: Calculate the resilience value of each candidate micropath segment, rank them from highest to lowest, select the top-ranked candidate micropath segment as the alternative path, embed the alternative path into the base path, and simultaneously use a smoothing algorithm to correct the connection points between the alternative path and the base path to generate a complete path. The specific formula for calculating the resilience value is as follows: In the formula, Indicates the toughness value. Indicates the width adaptation factor. Represents the historical stability coefficient. This represents the time compatibility coefficient.

3. The AGV warehouse entry cart according to claim 2, characterized in that, The specific steps for the communication coordination module to generate the resolution strategy are as follows: S2.1: Extract each node in the complete path in real time, predict the time of each path node, generate the corresponding expected arrival time window, obtain the reservation information of each AGV inbound vehicle, and synchronize it to the external central dispatch system and adjacent AGV inbound vehicles in the path overlap area via wireless network. S2.2: The receiver writes the information into the local path reservation cache table, compares the path node overlap, and if multiple AGV inbound vehicles appear at the same time window on the same path node, then conflict prediction is performed, and the conflict level of the current AGV inbound vehicle is judged according to the preset division rules as high level, medium level and low level. S2.3: Calculate the corresponding resource priority score based on the task urgency, energy criticality, path substitutability, path resilience, and historical waiting factor of each conflicting AGV inbound vehicle, and sort them from high to low. At the same time, retain the original path of the AGV inbound vehicle with a score higher than the preset threshold according to the ranking order, and adjust the path of other AGV inbound vehicles.

4. The AGV warehouse entry cart according to claim 3, characterized in that, The specific steps for the positioning adjustment module to calculate the pose deviation between each AGV inbound trolley and the target cargo location are as follows: S3.1: Collect image data of the front structure of the shelf, obtain coordinate information based on the image data of the front structure of each shelf, and then convert the image coordinates into the spatial pose of each AGV inbound trolley in the coordinate system through calibration parameters, so as to output the corresponding three-dimensional coarse positioning information. S3.2: Obtain the docking posture of each AGV relative to the cargo location in history, and compare it with the self-pose of each AGV inbound vehicle and the pose of the cargo location in the 3D coarse positioning information. Calculate the pose deviation between each AGV inbound vehicle and the target cargo location. The specific formula for calculating the pose deviation is as follows: The specific formula for calculating position deviation is as follows: In the formula, This indicates the positional deviation. Indicates the actual position of the AGV (Automated Guided Vehicle) trolley entering the warehouse. coordinate, Indicates the actual position of each AGV (Automated Guided Vehicle) trolley entering the warehouse. coordinate, This indicates the desired position of each AGV (Automated Guided Vehicle) inbound trolley. coordinate, This indicates the desired position of each AGV (Automated Guided Vehicle) inbound trolley. coordinate; The specific formula for calculating attitude angle deviation is as follows: In the formula, Indicates the attitude angle deviation. This indicates the actual attitude angle of each AGV entering the warehouse. This represents the expected attitude angle of each AGV entering the warehouse; S3.3: Compare the calculated pose deviation with the target pose of the storage location, calculate the compensation adjustment required for each AGV inbound trolley, and make minor corrections to the pose of each AGV inbound trolley based on the calculated compensation adjustment. The specific formula for calculating the compensation adjustment is as follows: In the formula, This represents the pose compensation adjustment amount, which includes three components, corresponding to X-axis position compensation, Y-axis position compensation, and attitude angle compensation, respectively. Indicates the X and Y axis position compensation ratio coefficients. This represents the attitude angle compensation ratio coefficient. The X and Y coordinates represent the orientation of the target cargo location. The current actual X and Y coordinates of the AGV inbound vehicle. Target attitude angle of the cargo location The current actual attitude angle of each AGV entering the warehouse.

5. A method for planning the inbound path of an AGV (Automated Guided Vehicle) warehouse trolley, used to implement the function of the AGV warehouse trolley as described in any one of claims 1-4, characterized in that, The specific steps of this planning method are as follows: Ⅰ: Collect and parse the inbound task information, and generate the basic path for each AGV inbound vehicle based on the static warehouse map; II: Real-time collection of the location and surrounding environment information of each AGV entering the warehouse, evaluation and adjustment of the generated basic path; Ⅲ: Collect the path reservation information sent by each AGV inbound vehicle, predict conflicts based on the path reservation information, and coordinate various conflicts. IV: Collect real-time resource occupancy data and resource usage requests and reservations from each AGV (Automated Guided Vehicle) for inbound trolleys, and dynamically allocate quotas; V: Calculate the pose deviation between each AGV inbound trolley and the target storage location to generate the corresponding fine-tuning path.

6. The AGV warehousing vehicle path planning method according to claim 5, characterized in that, The specific steps for dynamic quota allocation described in step IV are as follows: S4.1: Collect and assign corresponding identifiers to various management resources, detect in real time whether there are AGV inbound vehicles occupying resources in the management resource area, and retrieve the occupancy records of various resources within 1 hour from the local resource database, and calculate the average occupancy time and peak usage period. S4.2: Collect resource reservation requests from each AGV inbound vehicle, arrange each resource reservation request in ascending order, mark each resource reservation request as pending, evaluate the priority of each resource reservation request, and arrange them in descending order. S4.3: Based on the resource occupancy status, prioritize each resource reservation request, allocate usage quotas for each type of resource, generate corresponding quota instructions, and then send the quota instructions to the corresponding AGV inbound vehicle. If the AGV inbound vehicle with the allocated quota malfunctions or the task is canceled, the originally allocated quota is reclaimed and reassigned to the AGV inbound vehicle with the highest priority of the resource reservation request.

7. The AGV warehousing vehicle path planning method according to claim 6, characterized in that, The specific steps for generating the corresponding fine-tuning path in step V are as follows: S5.1: Collect the reference pose data of the target location and the image feature points of the shelf, extract the pixel coordinates of the shelf image feature points, correct the pixel coordinates of the shelf image feature points through the distortion coefficient, and then convert the corrected pixel coordinates into coordinates in the three-dimensional coordinate system through the perspective projection formula. S5.2: Convert the coordinates in the three-dimensional coordinate system to the coordinates in the AGV loading vehicle coordinate system to obtain the current planar position of each AGV loading vehicle and calculate the pose deviation between each AGV loading vehicle and the target storage location. At the same time, set the constraint parameters for fine-tuning the path based on the physical performance of each AGV loading vehicle and the environment of the target storage location. S5.3: Based on the calculated pose deviation and the constraint parameters of the set fine-tuning path, a corresponding fine-tuning path is generated, and each fine-tuning path is verified in real time. If the verification passes, the fine-tuning path enters the execution stage; otherwise, it returns to be regenerated. Then, the fine-tuning path that passes the verification is converted into drive wheel control commands and sent to the corresponding AGV inbound vehicles. The specific formula for fine-tuning path verification is as follows: In the formula, This indicates that each AGV inbound trolley is in close contact with the first AGV during the fine-tuning process. Real-time distance between each surrounding obstacle The table shows the real-time coordinates of the AGV inbound trolley during the fine-tuning process. Indicates the first The coordinates of the obstacle.

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