Multi-task parallel processing AGV sorting robot intelligent scheduling method

Abstract

The invention provides a multi-task parallel processing AGV sorting robot intelligent scheduling method, and relates to the technical field of logistics sorting scheduling. First to-be-stacked information is reversely analyzed according to a standard stack-shaped template to obtain a logistics box arrival sequence queue; carrying out logistics task distribution optimization according to the connection time consumption topology and the logistics box arrival sequence queue, and outputting M parallel logistics task sequences; and carrying out logistics conflict compensation on the N groups of parallel logistics task sequences and the M parallel logistics task sequences, outputting M parallel logistics track sequences to schedule the M sorting robots, executing the M parallel logistics task sequences, and carrying out stacking delivery at the first sorting port. The technical problems of path conflict and frequent sorting congestion of the sorting robot in a complex scene of multi-task parallel in the prior art are solved. The technical effects of effectively reducing the risk of path conflict and congestion of the sorting port and enhancing the adaptive capacity and fault tolerance of the sorting robot under the complex scene of multi-task parallel are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics sorting and scheduling, and in particular to an intelligent scheduling method for an AGV sorting robot for multi-task parallel processing. Background Art

[0002] In intelligent warehousing and logistics systems, the coordinated scheduling of multiple AGV sorting robots has long faced the common problems of path planning conflicts and imbalance in task execution efficiency.

[0003] The existing technology adopts fixed priority rules or static time window allocation strategies, which are difficult to deal with the congestion problem caused by dynamic path intersection in high-concurrency task scenarios, especially in the sorting port area, which is prone to buffer overflow and task delay due to the instantaneous convergence of multiple AGVs.

[0004] At the same time, the traditional task scheduling mechanism lacks a structured connection with palletizing needs, and the temporal and spatial adaptability of the handling order and the palletizing level is poor, which often leads to safety hazards such as the displacement of the center of gravity of the pallet and the misalignment of the box stacking.

[0005] In addition, the split scheduling of sorting tasks and palletizing links leads to low resource utilization of the AGV cluster and inability to achieve load balancing throughout the entire process, which seriously restricts the overall throughput efficiency and operational stability of the warehouse automation system.

[0006] In summary, in the prior art, there are technical problems such as frequent path conflicts and sorting congestion of AGV sorting robots in complex scenarios with multiple tasks running in parallel, and the disconnection between the execution of handling and sorting tasks and the subsequent palletizing tasks. Summary of the invention

[0007] The present invention provides an intelligent scheduling method for an AGV sorting robot with multi-task parallel processing, which is used to solve the technical problems in the prior art that path conflicts and sorting congestion are frequent in AGV sorting robots in complex scenarios with multi-task parallel processing, and the execution of transporting and sorting tasks is disconnected from the subsequent palletizing tasks.

[0008] In view of the above problems, the present invention provides an intelligent scheduling method for an AGV sorting robot with multi-task parallel processing.

[0009] The intelligent scheduling method for the multi-task parallel processing AGV sorting robot provided by the present invention includes: interactively obtaining the first palletizing information of the first sorting port; reversely analyzing the first palletizing information according to the standard pallet shape template to obtain the arrival order queue of the logistics boxes; extracting the connection port position information of the automatic sorting area from the first palletizing information; constructing a connection time-consuming topology according to the connection port position information and the first sorting position of the first sorting port; optimizing the logistics task allocation according to the connection time-consuming topology and the arrival order queue of the logistics boxes, and outputting M parallel logistics task sequences; interactively obtaining N groups of parallel logistics task sequences of the remaining N sorting ports, compensating for logistics conflicts between the N groups of parallel logistics task sequences and the M parallel logistics task sequences, and outputting M parallel logistics trajectory sequences; using the M parallel logistics trajectory sequences to schedule M AGV sorting robots, execute the M parallel logistics task sequences, and perform palletizing and delivery at the first sorting port.

[0010] The technical solution provided in the present invention has at least the following technical effects or advantages: The method provided in the embodiment of the present invention interactively obtains the first palletizing information of the first sorting port; reversely analyzes the first palletizing information according to the standard pallet shape template to obtain the arrival order queue of the logistics boxes; extracts the connection port position information of the automatic sorting area from the first palletizing information; constructs a connection time-consuming topology according to the connection port position information and the first sorting position of the first sorting port; optimizes the logistics task allocation according to the connection time-consuming topology and the arrival order queue of the logistics boxes, and outputs M parallel logistics task sequences; interactively obtaining N groups of parallel logistics task sequences of the remaining N sorting ports, compensating for logistics conflicts between the N groups of parallel logistics task sequences and the M parallel logistics task sequences, and outputting M parallel logistics trajectory sequences; using the M parallel logistics trajectory sequences to schedule M AGV sorting robots, execute the M parallel logistics task sequences, and perform palletizing and delivery at the first sorting port. It achieves the technical effect of ensuring that the logistics boxes arrive and are stacked accurately in the preset order, significantly improving the stability of the palletizing structure and the space utilization rate, effectively reducing the risk of path conflicts and sorting port congestion, enhancing the adaptability and fault tolerance of the AGV sorting robot in complex scenarios of multi-task parallel processing, and ensuring the reliable operation of high-throughput sorting operations. Description of the Drawings

[0011] Figure 1 It is a schematic flow chart of the intelligent scheduling method for the multi-task parallel processing AGV sorting robot provided by the present invention; Figure 2 It is a schematic flow chart of obtaining the arrival order queue of the logistics boxes in the intelligent scheduling method for the multi-task parallel processing AGV sorting robot provided by the present invention. Detailed Embodiments

[0012] The present invention provides an intelligent scheduling method for an AGV sorting robot with multi-task parallel processing, which is used to solve the technical problems in the prior art that path conflicts and sorting congestion occur frequently for AGV sorting robots in complex scenarios with multi-task parallelism, and the handling and sorting tasks are disconnected from the later palletizing tasks. It achieves the technical effects of ensuring that the logistics boxes arrive and are stacked precisely in the preset order, significantly improving the stability of the palletizing structure and the space utilization rate, effectively reducing the risk of path conflicts and sorting port congestion, enhancing the adaptability and fault tolerance of the AGV sorting robot in complex scenarios with multi-task parallelism, and ensuring the reliable operation of high-throughput sorting operations.

[0013] Next, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention are shown in the accompanying drawings rather than all of them.

[0014] Embodiment, as Figure 1 shown, the present invention provides an intelligent scheduling method for an AGV sorting robot with multi-task parallel processing, and the method includes: A100: Obtain the first palletizing information of the first sorting port through interaction.

[0015] Specifically, in this embodiment, the first sorting port is any one of the N + 1 sorting ports in the automated sorting area. By interacting with the Warehouse Management System (WMS), the first palletizing information of the first sorting port is retrieved. The first palletizing information includes the total number (W) of logistics boxes to be palletized, the number of logistics boxes, the maximum base size, and the total mass of the logistics boxes, as well as the current connection ports of the W logistics boxes to be palletized in the automated sorting area, the weight distribution of each logistics box, the box size specifications, and other information.

[0016] A200: Reverse-analyze the first palletizing information according to the standard pallet shape template to obtain the logistics box arrival order queue.

[0017] In one embodiment, before reverse-analyzing the first palletizing information according to the standard pallet shape template to obtain the logistics box arrival order queue, the method step A200 provided by the present invention further includes: A200-1: Extract the palletizing requirement correlation features from the first palletizing information, where the palletizing requirement correlation features include the number of logistics boxes, the maximum base size, and the total mass of the logistics boxes.

[0018] A200-2: Match the pallet pattern template according to the palletizing requirement associated features to obtain the standard pallet pattern template.

[0019] In one embodiment, as Figure 2 shown, reverse-analyze the first palletizing information according to the standard pallet pattern template to obtain the arrival order queue of the logistics boxes. The method step A200 provided by the present invention further includes: A210: Decompose the standard pallet pattern template into the pallet pattern structure to obtain H palletizing level feature vectors of H virtual palletizing boxes.

[0020] A220: Extract W logistics box feature vectors of W logistics boxes to be palletized from the first palletizing information, where W≤H and H-W≤8.

[0021] A230: Use the W logistics box feature vectors to traverse the H palletizing level feature vectors for palletizing compatibility judgment, and screen and locate to obtain W palletizing space positions.

[0022] A240: After projecting the W palletizing space positions on the standard pallet pattern template, perform descending order fitting of the palletizing levels and output the arrival order queue of the logistics boxes.

[0023] Specifically, it should be understood that the number of logistics boxes represents the total number of logistics boxes to be palletized (denoted as W), which is used to determine the total volume of the required palletizing space. The maximum base size is defined as the maximum length and width value of the bottom surface of the logistics box, which is used to screen and match the appropriate pallet pattern template to avoid palletizing overrun problems. The total mass of the logistics boxes is obtained by accumulating the weights of all the logistics boxes to be palletized, which is used to verify whether the load-bearing capacity of the selected template meets the safety standards.

[0024] Based on this, this embodiment extracts the palletizing requirement associated features including the number of logistics boxes, the maximum base size, and the total mass of the logistics boxes from the first palletizing information.

[0025] At the same time, it should be understood that the data source of the palletizing requirement associated features in this embodiment is preferably obtained by scanning the RFID tags and barcodes of the logistics boxes during the warehousing period, and the obtained data is verified with the logistics box specification information in the warehousing database.

[0026] The pallet pattern template library pre-constructed in this embodiment contains a variety of template types that have passed the stability verification, such as rectangular array type, pyramid type, and staggered palletizing structures. Each template is associated with the number of levels, the size constraints of each level, the load-bearing threshold, and the space arrangement rules.

[0027] During the matching process, first, candidate templates with a base area greater than or equal to the maximum base size are screened out. Based on the total mass of the logistics boxes and the load-bearing threshold of the candidate templates, options with a total mass exceeding the maximum load-bearing capacity of the template are eliminated to avoid the instability of the palletizing structure. Finally, a template with the number of logistics boxes H that can be palletized by the palletizing structure satisfying H≥W and H - W≤8 is selected as the standard pallet pattern template (for example, a rectangular array template may be designed as 3 layers × 4 columns, H = 12).

[0028] Through the above matching mechanism, a standard pallet pattern template suitable for the current palletizing task is finally output, and its palletizing structure achieves an optimal balance among stability, space utilization rate, and operation feasibility.

[0029] Based on the obtained standard pallet pattern template, a structural analysis is performed on the standard pallet pattern template, which is split into H virtual palletizing boxes. Each box corresponds to an independent palletizing position preset in the template and is associated with a palletizing level feature vector. The feature vector includes the three-dimensional space positioning coordinates (X, Y, Z) of the box in the template coordinate system, the maximum length, width, and height dimension constraints of the logistics boxes allowed to be accommodated, the load-bearing capacity threshold of the level calculated based on the mechanical model, and the minimum horizontal and vertical spacing rules with adjacent boxes.

[0030] In this embodiment, through the above decomposition process, the standard pallet pattern template is transformed into a parametric model composed of H virtual boxes, providing a structured data basis for subsequent logistics box matching.

[0031] W logistics box feature vectors of the W logistics boxes to be palletized are extracted from the first palletizing information. The logistics box feature vector specifically includes the actual length, width, height dimension parameters, weight data, and center of gravity position coordinates of the logistics boxes to be palletized.

[0032] Among them, the total number of logistics boxes W satisfies the constraint conditions of W≤H and H - W≤8, where H is the preset capacity of the standard pallet pattern template (i.e., the total number of logistics boxes that the template can accommodate). This constraint ensures that the capacity of the selected template can fully accommodate all logistics boxes (W≤H), while limiting the number of unused empty box positions within 8 (H - W≤8), thereby maximizing the utilization rate of the storage space on the premise of avoiding palletizing failure.

[0033] Furthermore, the W logistics box feature vectors are sequentially traversed and matched with the H palletizing level feature vectors, and the compatible palletizing space positions are screened through the compatibility scoring algorithm.

[0034] Specifically, for each logistics container to be palletized, based on the logistics container feature vector and the palletizing level feature vector, calculate its matching score with each virtual container in terms of size constraint, load-bearing capacity, and adjacent spacing rules, and select the virtual container with the highest score as its palletizing target position. If multiple logistics containers compete for the same virtual container, prioritize them based on the weight of the logistics container and the center-of-gravity stability. The logistics container with a larger weight or a lower tolerance for center-of-gravity deviation is preferentially allocated to the bottom-layer container with a stronger load-bearing capacity. Finally, determine the unique palletizing space positions for all W logistics containers and output the W palletizing space positions.

[0035] After mapping the W palletizing space positions to the standard pallet shape template, preferentially add the logistics containers corresponding to the bottom-layer containers to the logistics container arrival order queue, and then sequentially add the upper-layer logistics containers in ascending order of the layer number. For multiple containers in the same layer, arrange the logistics container delivery order in ascending order of the X-axis (from left to right) and Y-axis (from front to back) of the spatial coordinates, and finally output the logistics container arrival order queue.

[0036] Based on the reverse parsing and feature matching mechanism of the standard pallet shape template, combined with the compatibility judgment of the physical attributes of the logistics container and the template layer constraints, this embodiment screens out the palletizing space positions suitable for each logistics container, and generates the logistics container arrival order queue through descending-order fitting of the layers, achieving the direct technical effect of improving the stability of the palletizing structure, and indirectly achieving the indirect technical effect of maximizing the utilization rate of the storage space while ensuring the stability of the palletizing structure and improving the overall sorting efficiency.

[0037] A300: Extract the connection port position information of the automated sorting area from the first palletizing information.

[0038] Specifically, in this embodiment, based on the W target sorting port codes of the W logistics containers to be palletized in the first palletizing information, associate their corresponding connection port physical coordinates to obtain the connection port position information. The connection port position information includes the two-dimensional plane coordinates (X, Y) of the W connection ports. Since the AGV sorting robot can only connect one logistics container at a time, the same connection port may be repeatedly allocated to multiple logistics containers, resulting in duplicate coordinate entries in the W connection port position information.

[0039] A400: Construct a connection time-consuming topology based on the connection port position information and the first sorting position of the first sorting port.

[0040] In one embodiment, according to the connection port position information and the first sorting position of the first sorting port, construct a connection time-consuming topology. The method step A400 provided by the present invention further includes: A410: Decompose the connection port position information to obtain the connection port position information of the W logistics containers to be palletized.

[0041] A420: Construct W connection movement trajectories based on the first sorting position and the position information of W connection ports.

[0042] A430: Fit and output W reference connection time-consuming based on the reference AGV displacement speed and the W connection movement trajectories.

[0043] A440: Construct W child nodes according to the position information of the W connection ports, and construct a central node according to the first sorting position.

[0044] A450: Quantify the topological connection lines according to the W reference connection time-consuming, construct the in-degree connection from the W child nodes to the central node, and complete the construction of the connection time-consuming topology.

[0045] Specifically, in this embodiment, through the mapping relationship between the logistics box ID and the connection port ID, decompose the position information of the connection port, and determine the connection port position (X p , Y p ) of each logistics box, and obtain the position information of the W connection ports of the W palletizing logistics boxes.

[0046] Based on the sorting port coordinates corresponding to the first sorting position and the coordinates of the W connection ports, use the A* path planning algorithm to generate W connection movement trajectories.

[0047] It should be understood that each connection movement trajectory is composed of a series of path points, and the path point coordinates are generated by a rasterized map or a continuous coordinate space interpolation algorithm, and avoid fixed obstacles (such as shelves, charging stations) in the warehouse. Furthermore, according to the reference AGV displacement speed and the W connection movement trajectories, W (theoretical) reference connection time-consuming are fitted and output.

[0048] Abstract the W connection ports as the child nodes of the topological network. The attributes of each node include the connection port coordinates and the reference connection time-consuming. Take the first sorting port as the central node, and its attributes include the sorting port coordinates, processing rate, and buffer capacity. The connection relationship between nodes is defined by the physical path from the connection port to the sorting port, forming a star-shaped topological structure with the sorting port as the convergence center. Take the W reference connection time-consuming as the initial edge weights from the child nodes to the central node, establish the in-degree connection relationship, and complete the construction of the connection time-consuming topology.

[0049] This embodiment constructs a connection time-consuming topology based on the logistics trajectory and the logistics time-consuming, achieving the technical effect of providing a reference for subsequent parallel logistics task allocation.

[0050] A500: Optimize the logistics task allocation according to the connection time-consuming topology and the logistics box arrival order queue, and output M parallel logistics task sequences.

[0051] In one embodiment, based on the connection time-consuming topology and the arrival order queue of the logistics boxes, the logistics task allocation is optimized, and M parallel logistics task sequences are output. The method step A500 provided by the present invention further includes: A510: Based on the M AGV sorting robots, decompose the arrival order queue of the logistics boxes to obtain multiple adjacent local arrival order queues.

[0052] A520: According to the multiple local arrival order queues, perform connection delay superposition decomposition on the connection time-consuming topology to obtain multiple updated time-consuming sub-topologies.

[0053] A530: Based on the M AGV sorting robots, split the multiple updated time-consuming sub-topologies to obtain M logistics task time limit sequences.

[0054] A540: Map and add the W connection movement trajectories to the M logistics task time limit sequences, and output the M parallel logistics task sequences, where W≥25M.

[0055] In one embodiment, according to the multiple local arrival order queues, perform connection delay superposition decomposition on the connection time-consuming topology to obtain multiple updated time-consuming sub-topologies. The method step A520 provided by the present invention further includes: A521: According to the mapping relationship between the multiple local arrival order queues and the W palletizing logistics boxes in the connection time-consuming topology, decompose the connection time-consuming topology into multiple task time-consuming sub-topologies.

[0056] A522: Interactively obtain the arrival time interval constraint of the first sorting port.

[0057] A523: According to the multiple local arrival order queues, map and superimpose the arrival time interval constraint on the multiple task time-consuming sub-topologies to obtain multiple updated time-consuming sub-topologies.

[0058] In one embodiment, map and add the W connection movement trajectories to the M logistics task time limit sequences, and output the M parallel logistics task sequences. The method step A540 provided by the present invention further includes: A541: According to the mapping relationship between the M parallel logistics task sequences and the W palletizing logistics boxes, combine the W connection movement trajectories to obtain M connection task trajectory sequences.

[0059] A542: Store the M connection task trajectory sequences and the M logistics task time limit sequences in space-time alignment to generate the M parallel logistics task sequences.

[0060] Specifically, in this embodiment, to adapt to the parallel processing capabilities of M AGV sorting robots, the arrival order queue of the logistics boxes is divided into multiple adjacent local arrival order queues in chronological order.

[0061] The decomposition rule of the queue is based on the number M of AGV sorting robots and the preset single-AGV task cycle length, ensuring that the number of logistics boxes contained in each local arrival order queue matches the AGV processing capacity. For example, if the total number of tasks W = 100 and M = 4, the queue is divided into 25 local arrival order queues (each local arrival order queue contains 4 logistics boxes), and the time windows of adjacent queues are seamlessly connected to avoid task gaps.

[0062] According to the logistics box IDs in the local arrival order queue, locate their corresponding connection port nodes in the connection time-consuming topology, and extract these nodes and their connecting edges to form a local sub-topology, that is, the task time-consuming sub-topology.

[0063] Each task time-consuming sub-topology covers all the paths from the corresponding connection ports to the sorting ports involved in the local arrival order queue, and retains the weight calculation rule of the original topology, but limits its scope of action to the tasks within the current time window. In this embodiment, the connection time-consuming topology is decomposed into multiple task time-consuming sub-topologies, ensuring that subsequent optimizations are only targeted at local tasks and reducing the complexity of global calculations.

[0064] Interactively obtain the arrival time interval constraint of the first sorting port. The arrival time interval constraint includes the minimum safety time difference (such as 5 seconds) between adjacent AGVs arriving at the sorting port and the buffer capacity limit (such as the maximum number of logistics boxes processed simultaneously).

[0065] In the task time-consuming sub-topology, dynamically superimpose the time interval constraint based on the ranking order of the tasks in the local arrival order queue. Specifically, the ranking of each task in the local queue (such as the 1st, 2nd, etc.) determines its time interval compensation value. For the task ranked k (k = 1, 2,..., K), the edge weight update formula is: . Among them, is the arrival time interval constraint, It is the sequential number of the tasks in the local queue. For example, the baseline time of the task ranked first in a local queue is 30 seconds, and the updated time is 30+1×5=35 seconds. The time taken by the task ranked second is 30+2×5=40 seconds, and so on. This mechanism ensures that the tasks on the same path gradually increase the time intervals in the order of arrival, avoiding instantaneous congestion at the sorting port due to the concentrated arrival of multiple tasks. Based on this, according to the multiple local arrival order queues, the arrival time interval constraints are superimposed on the multiple task time-consuming sub-topology mappings to obtain multiple updated time-consuming sub-topologies. It should be understood that the updated time-consuming sub-topology is used to limit the logistics transportation speed of the AGV sorting robot to ensure that the order of the logistics boxes that finally arrive at the sorting port meets the logistics box arrival order queue.

[0066] A greedy algorithm is used to split multiple time-consuming sub-topologies into M logistics task time-limit sequences. Each sequence corresponds to a task list of an AGV, including the logistics box ID, docking path, start time and latest completion time to be processed.

[0067] When splitting, tasks with similar time consumption and low path conflict are preferentially assigned to the same AGV sorting robot, and the total amount of tasks of each AGV sorting robot is consistent (for example, if the total number of tasks W=100 and M=4, the queue is divided into 25 local arrival order queues, then the task amount of each AGV sorting robot is 25, and each task comes from the 25 update time-consuming sub-topologies corresponding to the 25 local arrival order queues).

[0068] According to the task list of each AGV in the M parallel logistics task sequences (i.e., the set of logistics box IDs assigned to it), the W docking movement trajectories are reorganized into M docking task trajectory sequences according to the AGV number. For example, if the task list of AGV1 contains logistics boxes B1, B5, B9, etc., the path segments corresponding to these logistics boxes are extracted from the W trajectories and spliced into the complete docking trajectory of AGV1 in the order of task start time.

[0069] The coordinates (X, Y) of the path points in each docking task trajectory sequence are compared with the corresponding task time limit (such as the start time t start , deadline t end ) and write it into the scheduling database. For example, the trajectory point (X1, Y1) of an AGV corresponds to the time 08:00:00, and the point (X2, Y2) corresponds to 08:00:05, with an error tolerance of ±300 milliseconds.

[0070] Based on this, the M parallel logistics task sequences are generated by aligning the M docking task trajectory sequences with the M logistics task time limit sequences in time and space and storing them.

[0071] This embodiment reorganizes the docking movement trajectory according to the AGV task list to generate continuous and conflict-free path instructions for each AGV, and then aligns the storage with the task execution time limit in time and space to ensure that the path execution time strictly matches the task window.

[0072] A600: Interactively obtain N groups of parallel logistics task sequences of the remaining N sorting ports, perform logistics conflict compensation on the N groups of parallel logistics task sequences and M parallel logistics task sequences, and output M parallel logistics trajectory sequences.

[0073] In one embodiment, N groups of parallel logistics task sequences of the remaining N sorting ports are interactively obtained, logistics conflicts are compensated for the N groups of parallel logistics task sequences and the M parallel logistics task sequences, and M parallel logistics trajectory sequences are output. The method step A600 provided by the present invention also includes: A610: After the N groups of parallel logistics task sequences and the M parallel logistics task sequences are aligned in time sequence, spatial movement trajectory mapping is performed to locate multiple trajectory conflicting spatiotemporal nodes.

[0074] A620: Extract multiple docking movement trajectories and multiple logistics task time limits from the M parallel logistics task sequence mappings based on the multiple trajectory conflict space-time nodes.

[0075] A630: Taking the time limits of the multiple logistics tasks as constraints, locally update the multiple docking movement trajectories according to the multiple trajectory conflict time-space nodes, and output multiple updated movement trajectories, wherein the multiple updated movement trajectories have multiple updated AGV displacement speeds.

[0076] A640: Based on the multiple trajectory conflicting space-time nodes, the multiple updated mobile trajectories and the multiple updated AGV displacement speeds are covered to the M parallel logistics task sequences, and the M parallel logistics trajectory sequences are output.

[0077] In one embodiment, taking the time limits of the multiple logistics tasks as constraints, locally updating the multiple docking movement trajectories according to the multiple trajectory conflicting spatiotemporal nodes, and outputting multiple updated movement trajectories, the method step A630 provided by the present invention further includes: A631: Frame multiple trajectory compensation areas at the multiple trajectory conflict time and space nodes.

[0078] A632: Using the multiple trajectory compensation areas as compensation space limitations, locally updating the multiple connecting movement trajectories according to the multiple trajectory conflicting spatiotemporal nodes, and outputting multiple groups of alternative movement trajectories.

[0079] A633: Interactively obtain the ultimate AGV displacement speed.

[0080] A634: Calculate multiple sets of alternative AGV displacement speeds according to the multiple logistics task time limits and multiple sets of alternative movement trajectories.

[0081] A635: Screen out the multiple updated movement trajectories from the multiple sets of alternative movement trajectories according to the deviation scale between the multiple sets of alternative AGV displacement speeds and the limit AGV displacement speed.

[0082] In one embodiment, Q times the reference AGV displacement speed is the limit AGV displacement speed, where Q ∈ [1.2, 2.6].

[0083] In this embodiment, to ensure that the AGV sorting robot strictly executes according to the task time limit, based on the path trajectory and time window in the logistics task sequence of each AGV, calculate the corresponding logistics speed control sequence.

[0084] Specifically, for each path segment in the task sequence (such as the movement trajectory from the connection port to the sorting port), dynamically set the AGV displacement speed according to the ratio of the path length to the time window. For example, if the length of a certain path segment is 30 meters and the time window is 20 seconds, the theoretical speed of the AGV on this segment is 1.5 m / s. For N sets of parallel logistics task sequences at other sorting ports, use the same speed control logic to generate the corresponding logistics speed control sequences, and obtain N sets of logistics speed control sequences.

[0085] Through unified timestamps and global coordinate systems, align the M main sorting port task sequences and N sets of other sorting port task sequences in space and time. Based on the aligned task data, perform spatial movement trajectory mapping to detect the space-time nodes where different AGVs cross paths or overlap regions within the same time period. For example, if AGV1 is planned to pass through the coordinate (X1, Y1) at 08:00:10, and AGV2 passes through the same coordinate at 08:00:12, mark this area as a potential conflict node.

[0086] For the detected trajectory conflict space-time nodes, extract the affected connection movement trajectories and their associated logistics task time limits from the M main sorting port task sequences. For example, if a certain conflict node involves task T1 of AGV1 (path trajectory P1, time window 08:00:00 - 08:00:30), then extract the path point set and time constraint data of P1.

[0087] Taking the conflict space-time node as the center, expand a certain range outward (such as a circular area with a radius of 2 meters) and define it as the trajectory compensation area. This area covers the time window when the conflict occurs (such as 08:00:10 - 08:00:15) and the spatial range (such as the coordinate (X 1±2 , Y 1±2 )) and serves as the operation boundary for trajectory update.

[0088] The size of the compensation area is dynamically set according to the braking distance and maximum steering angle of the AGV to ensure the physical feasibility of the bypass path. For example, if the maximum braking distance of the AGV is 1.5 meters, the radius of the compensation area is at least 2 meters to reserve a safety margin.

[0089] Taking the multiple trajectory compensation areas as the compensation space limit, locally update the multiple connection movement trajectories according to the multiple trajectory conflict spatio-temporal nodes, and output multiple groups of alternative movement trajectories to resolve conflicts. Each group of trajectories is generated by the A* algorithm combined with a dynamic obstacle map, and avoids the conflict paths of other AGVs. The path length increment does not exceed 20% of the original path, and the steering angle conforms to the mechanical limit of the AGV sorting robot during driving (such as a maximum steering angle of 45 degrees).

[0090] Interactively obtain the limit AGV displacement speed, and the Q times of the reference AGV displacement speed is the limit AGV displacement speed, where Q ∈ [1.2, 2.6].

[0091] According to the multiple logistics task time limits and multiple groups of alternative movement trajectories, calculate multiple groups of alternative AGV displacement speeds. According to the deviation scale between the multiple groups of alternative AGV displacement speeds and the limit AGV displacement speed, select the multiple updated movement trajectories with the largest deviation scale from the multiple groups of alternative movement trajectories with respect to the limit AGV displacement speed.

[0092] Based on the multiple trajectory conflict spatio-temporal nodes, cover the multiple updated movement trajectories and multiple updated AGV displacement speeds to the M parallel logistics task sequences, so as to replace the local path segments and speed commands corresponding to the conflict nodes, and output the M parallel logistics trajectory sequences.

[0093] This embodiment achieves the technical effects of improving the collaborative ability of multiple AGV sorting robots for multi-task parallel processing, reducing sorting path conflicts, and improving the execution efficiency of sorting tasks.

[0094] A700: Use the M parallel logistics trajectory sequences to schedule M AGV sorting robots, execute the M parallel logistics task sequences, and perform palletizing and delivery at the first sorting port.

[0095] Specifically, in this embodiment, the scheduling module issues the generated M parallel logistics trajectory sequences to M AGV sorting robots. The M AGV sorting robots transport the logistics boxes from the connection port to the sorting port in a preset order based on the path point coordinates, timestamps, and speed commands in the corresponding M parallel logistics trajectory sequences.

[0096] When the AGV sorting robot arrives at the sorting port, the logistics boxes are accurately stacked according to the levels and coordinates of the standard stack template by the robotic arm or conveyor device. The bottom boxes are placed first and stacked layer by layer upwards. At the same time, the stacking position is corrected in real time through the pressure sensor and the vision positioning system to prevent the stack from tilting or collapsing. M AGV sorting robots cooperate at the sorting port to complete the delivery of all logistics boxes, forming a stable and optimally space-utilized stacking structure.

[0097] This embodiment achieves the technical effects of ensuring that the logistics boxes arrive and are stacked accurately in the preset order, significantly improving the stability of the stacking structure and the space utilization rate, effectively reducing the risk of path conflicts and sorting port congestion, enhancing the adaptability and fault tolerance of the AGV sorting robot in complex scenarios with multi-task parallelism, and ensuring the reliable operation of high-throughput sorting operations.

[0098] Any of the above methods or steps can be stored as computer instructions or programs in various types of computer memories, and the computer instructions or programs are recognized by various types of computer processors, thereby implementing any of the above methods or steps.

[0099] Based on the above specific embodiments of the present invention, without departing from the principle of the present invention, any improvements and modifications made by those skilled in the art to the present invention shall fall within the patent protection scope of the present invention.

Claims

1. An intelligent scheduling method for an AGV sorting robot with multi-task parallel processing, characterized in that, The method includes: Interactively obtaining the first palletizing information of the first sorting outlet; Reverse-analyzing the first palletizing information according to the standard pallet shape template to obtain the arrival order queue of the logistics boxes; Extracting the position information of the connection ports in the automated sorting area from the first palletizing information; Constructing a connection time-consuming topology according to the connection port position information and the first sorting position of the first sorting outlet; Optimizing the logistics task allocation according to the connection time-consuming topology and the arrival order queue of the logistics boxes, and outputting M parallel logistics task sequences; Interactively obtaining N groups of parallel logistics task sequences of the remaining N sorting outlets, compensating for logistics conflicts between the N groups of parallel logistics task sequences and the M parallel logistics task sequences, and outputting M parallel logistics trajectory sequences; Using the M parallel logistics trajectory sequences to schedule M AGV sorting robots, execute the M parallel logistics task sequences, and perform palletizing and delivery at the first sorting outlet.

2. The intelligent scheduling method of the multi-task parallel processing AGV sorting robot according to claim 1, characterized in that, Reverse-analyzing the first palletizing information according to the standard pallet shape template to obtain the arrival order queue of the logistics boxes, the method includes: Decomposing the standard pallet shape template into the pallet shape structure to obtain H palletizing level feature vectors of H virtual palletizing boxes; Extracting W logistics box feature vectors of W logistics boxes to be palletized from the first palletizing information, where W ≤ H and H - W ≤ 8; Using the W logistics box feature vectors to traverse the H palletizing level feature vectors to perform palletizing compatibility judgment, and screening and positioning to obtain W palletizing space positions; After projecting the W palletizing space positions on the standard pallet shape template, performing descending order fitting of the palletizing levels, and outputting the arrival order queue of the logistics boxes.

3. The intelligent scheduling method of the multi-task parallel processing AGV sorting robot according to claim 1, characterized in that, Constructing a connection time-consuming topology according to the connection port position information and the first sorting position of the first sorting outlet, the method includes: Decomposing the connection port position information to obtain W connection port position information of W logistics boxes to be palletized; Constructing W connection movement trajectories according to the first sorting position and the W connection port position information; Fitting and outputting W reference connection time consumptions according to the reference AGV displacement speed and the W connection movement trajectories; Constructing W child nodes according to the W connection port position information and a central node according to the first sorting position; Quantifying the topological connections according to the W reference connection time consumptions, constructing the in-degree connections from the W child nodes to the central node, and completing the construction of the connection time-consuming topology.

4. The intelligent scheduling method of the AGV sorting robot for multi-task parallel processing according to claim 3, characterized in that, Optimizing the logistics task allocation according to the connection time-consuming topology and the arrival order queue of the logistics boxes, and outputting M parallel logistics task sequences, the method includes: Based on the M AGV sorting robots, decomposing the arrival order queue of the logistics boxes to obtain multiple adjacent local arrival order queues; According to the multiple local arrival order queues, performing connection time delay superposition decomposition on the connection time-consuming topology to obtain multiple updated time-consuming sub-topologies; Splitting the multiple updated time-consuming sub-topologies based on the M AGV sorting robots to obtain M logistics task time limit sequences; Mapping and adding the W connection movement trajectories to the M logistics task time limit sequences, and outputting the M parallel logistics task sequences, where W ≥ 25M.

5. The intelligent scheduling method of the multi-task parallel processing AGV sorting robot according to claim 4, characterized in that, Interactively obtain N groups of parallel logistics task sequences for the remaining N sorting ports, perform logistics conflict compensation on the N groups of parallel logistics task sequences and M parallel logistics task sequences, and output M parallel logistics trajectory sequences. The method includes: After time-series alignment of the N groups of parallel logistics task sequences and M parallel logistics task sequences, perform spatial movement trajectory mapping to locate multiple trajectory conflict time-space nodes; Extract multiple connection movement trajectories and multiple logistics task time limits from the M parallel logistics task sequences according to the multiple trajectory conflict time-space nodes; With the multiple logistics task time limits as constraints, locally update the multiple connection movement trajectories according to the multiple trajectory conflict time-space nodes, and output multiple updated movement trajectories, where the multiple updated movement trajectories have multiple updated AGV displacement speeds; According to the multiple trajectory conflict time-space nodes, cover the multiple updated movement trajectories and multiple updated AGV displacement speeds to the M parallel logistics task sequences, and output the M parallel logistics trajectory sequences.

6. The intelligent scheduling method of the multi-task parallel processing AGV sorting robot according to claim 4, characterized in that, According to the multiple local arrival order queues, perform connection delay superposition decomposition on the connection time-consuming topology to obtain multiple updated time-consuming sub-topologies. The method includes: According to the mapping relationship between the multiple local arrival order queues and the W palletizing logistics boxes in the connection time-consuming topology, decompose the connection time-consuming topology into multiple task time-consuming sub-topologies; Interactively obtain the arrival time interval constraint of the first sorting port; According to the multiple local arrival order queues, superimpose the arrival time interval constraint on the multiple task time-consuming sub-topologies in the mapping to obtain multiple updated time-consuming sub-topologies.

7. The intelligent scheduling method of the multi-task parallel processing AGV sorting robot according to claim 4, characterized in that, Map and add the W connection movement trajectories to the M logistics task time limit sequences, and output the M parallel logistics task sequences. The method includes: According to the mapping relationship between the M parallel logistics task sequences and the W palletizing logistics boxes, combine the W connection movement trajectories to obtain M connection task trajectory sequences; Spatiotemporally align and store the M connection task trajectory sequences with the M logistics task time limit sequences to generate the M parallel logistics task sequences.

8. The intelligent scheduling method of the multi-task parallel processing AGV sorting robot according to claim 5, characterized in that, With the multiple logistics task time limits as constraints, locally update the multiple connection movement trajectories according to the multiple trajectory conflict time-space nodes, and output multiple updated movement trajectories. The method includes: Define multiple trajectory compensation regions at the multiple trajectory conflict time-space nodes; With the multiple trajectory compensation regions as compensation space limits, locally update the multiple connection movement trajectories according to the multiple trajectory conflict time-space nodes, and output multiple groups of alternative movement trajectories; Interactively obtain the limit AGV displacement speed; Calculate multiple groups of alternative AGV displacement speeds according to the multiple logistics task time limits and multiple groups of alternative movement trajectories; According to the deviation scale between the multiple groups of alternative AGV displacement speeds and the limit AGV displacement speed, select the multiple updated movement trajectories from the multiple groups of alternative movement trajectories.

9. The intelligent scheduling method of the multi-task parallel processing AGV sorting robot according to claim 8, characterized in that, The Q times of the reference AGV displacement speed is the limit AGV displacement speed, where Q ∈ [1.2, 2.6].

10. The intelligent scheduling method of the multi-task parallel processing AGV sorting robot according to claim 2, characterized in that, Reverse-analyze the first palletizing information to be palletized according to the standard pallet pattern template to obtain a queue of the arrival order of the logistics boxes. Before that, the method includes: Extract the palletizing requirement-related features from the first palletizing information to be palletized, where the palletizing requirement-related features include the number of logistics boxes, the maximum base size, and the total mass of the logistics boxes; Match the pallet pattern template according to the palletizing requirement-related features to obtain the standard pallet pattern template.

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