A method, system and storage medium for dynamic channel planning based on robot task type
Patent Information
- Application Number
- CN202610973797.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-01
- Publication Date
- 2026-10-30
AI Technical Summary
不同任务对通道的承载能力、行驶速度、安全间距等要求不同：传统路径规划方法将所有AGV机器人视为等同，所有路径资源对所有AGV机器人开放，导致不同类型的AGV机器人在同一通道上竞争，增加了路径冲突的概率
[0034]本发明根据机器人当前任务类型，动态确定允许使用的多个通道类型进行路径规划（例如执行重型搬运任务的AGV可使用重型专用通道，也可使用通用通道），而不是所有路径资源对所有AGV开放，有效避免不同类型AGV机器人的路径冲突，显著提高了通道利用率。对规划好的通行路径进行锁定，防止多车同时占用同一路径造成死锁或碰撞，提高了系统安全性。
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Figure CN123014349A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automated guided vehicle (AGV) scheduling technology, specifically to a channel dynamic planning method, system, and storage medium based on robot task types. Background Technology
[0002] In warehouse AGV scheduling applications, multiple AGV robots are typically deployed to collaboratively complete tasks such as material handling and goods sorting. Different AGV robots may undertake different types of tasks, such as handling heavy goods, handling light goods, or performing routine inspections. Different tasks have different requirements for the carrying capacity of the passageway, travel speed, and safety distance: traditional path planning methods treat all AGV robots as equal, and all path resources are open to all AGV robots, leading to different types of AGV robots competing for the same passageway, increasing the probability of path conflicts.
[0003] Therefore, it is necessary to propose further solutions to address the aforementioned technical problems. Summary of the Invention
[0004] This invention provides a channel dynamic planning method and system based on robot task types to solve the above-mentioned technical problems.
[0005] The objective of this invention is achieved through the following technical solution:
[0006] This invention provides a dynamic channel planning method based on robot task type, the method comprising the following steps:
[0007] Step SA001: Obtain the current task of the AGV robot and determine the task type corresponding to the current task;
[0008] Step SA002: Construct a dynamic map, which includes multiple nodes and paths connecting adjacent nodes; wherein each path is configured with an edge type attribute, which indicates the type of task that is allowed to pass.
[0009] Step SA003: Based on the task type, determine the set of target edge types that allow the AGV robot to pass;
[0010] Step SA004: Based on the target edge type set, execute the path search algorithm to search the dynamic map for a sequence of passable nodes from the starting node to the target node, where the edge type attribute of each edge belongs to the target edge type set;
[0011] Step SA005: Perform a locking operation on the passage path corresponding to the passable node sequence to prevent other AGV robots from using the locked passage path;
[0012] Step SA006: Generate navigation path instructions based on the locked sequence of passable nodes to control the AGV robot to move;
[0013] Step SA007: Based on the AGV robot reaching the target node, unlock the corresponding passage path.
[0014] Furthermore, the edge type attribute includes a first edge type, a second edge type, and a third edge type; the task type includes heavy cargo handling tasks, light cargo handling tasks, and general operation tasks.
[0015] Furthermore, the matching rule between the edge type attribute and the task type is as follows:
[0016] The first edge type corresponds to EdgeType=0, which allows AGV robots performing ordinary tasks, light handling tasks, and heavy handling tasks to pass through;
[0017] The second edge type corresponds to EdgeType=1, which only allows AGV robots performing light handling tasks to pass through;
[0018] The third edge type corresponds to EdgeType=2, which only allows AGV robots performing heavy handling tasks to pass through.
[0019] Furthermore, the travel path with the edge type attribute of the first edge type is configured as a general channel;
[0020] Paths with the edge type attribute set to the second edge type are configured as lightweight channels;
[0021] Paths with the edge type attribute set to the third edge type are configured as heavy passages.
[0022] Furthermore, the path search algorithm is either the A* algorithm or the Dijkstra algorithm.
[0023] Furthermore, in step SA005, when performing a locking operation on the passage path, a locking identifier and locking time are set based on the locked passage path, and the identifier of the AGV robot that locked the path is recorded.
[0024] Furthermore, in step SA007, when the locking time exceeds a preset threshold and the AGV robot still has not reached the target node, the lock is automatically released and an abnormal alarm message is generated, and the AGV robot is marked as disconnected, and its travel path is replanned.
[0025] The present invention also provides a channel dynamic planning system based on robot task type, for implementing the method described in any of the above, the system comprising:
[0026] The task acquisition module is used to acquire the current task of the AGV robot and determine its task type;
[0027] The map building module is used to build dynamic maps, in which each travel path is configured with an edge type attribute;
[0028] The path planning module is used to determine the allowed target edge types based on the task type and execute the path search algorithm to generate a sequence of traversable nodes that contains only the traversable paths of the target edge types.
[0029] The path locking module is used to perform a locking operation on the passage path corresponding to the sequence of passable nodes;
[0030] The navigation control module is used to generate navigation path instructions based on the locked node sequence to control the AGV robot's movement.
[0031] The unlocking module is used to unlock the AGV robot after it reaches the target node.
[0032] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above methods.
[0033] The beneficial technical effects achieved by this invention are as follows.
[0034] This invention dynamically determines multiple permissible channel types for path planning based on the robot's current task type (e.g., an AGV performing heavy-duty handling tasks can use a dedicated heavy-duty channel or a general-purpose channel), instead of opening all path resources to all AGVs. This effectively avoids path conflicts between different types of AGV robots and significantly improves channel utilization. Locking the planned travel paths prevents multiple vehicles from simultaneously occupying the same path, thus preventing deadlocks or collisions and improving system safety. Attached Figure Description
[0035] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0036] Figure 1 This is one of the flowcharts of a channel dynamic planning method based on robot task type according to the present invention;
[0037] Figure 2 This is the target edge type matching process when the AGV robot task type is a light cargo handling task in this invention;
[0038] Figure 3 This is a schematic diagram showing that the travel path in the dynamic map constructed by this invention is configured with an edge type attribute. Detailed Implementation
[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0040] like Figure 1-3 As shown, this invention provides a dynamic channel planning method based on robot task type, the method comprising the following steps:
[0041] Step SA001: Obtain the current task of the AGV robot and determine the task type corresponding to the current task; specifically, the central scheduling system issues task instructions to the AGV robot, which include information such as cargo weight, starting point, and ending point. Based on cargo weight or task attributes, the system classifies tasks into three types: heavy cargo handling tasks (cargo weight greater than a preset threshold), light cargo handling tasks (cargo weight less than or equal to the threshold), and ordinary operation tasks (no cargo handling, such as inspection, empty vehicle return, etc.).
[0042] Step SA002: Construct a dynamic map, which includes multiple nodes and paths connecting adjacent nodes. Each path is configured with an edge type attribute, which indicates the permitted task type. Specifically, the edge type attribute includes a first edge type, a second edge type, and a third edge type. The task types include heavy cargo handling tasks, light cargo handling tasks, and general operation tasks. Preferably, the dynamic map is stored in a graph data structure, containing multiple nodes and edges connecting the nodes. Each node corresponds to a location where the AGV can dock; each edge corresponds to a path between two nodes. Preferably, each edge is configured with an "edge type attribute," the values of which have the following meanings:
[0043] The first edge type corresponds to EdgeType=0: general channel, which allows AGV robots performing ordinary tasks, light handling tasks and heavy handling tasks to pass through;
[0044] The second edge type corresponds to EdgeType=1: Lightweight passage, which only allows AGV robots performing light handling tasks to pass through;
[0045] The third edge type corresponds to EdgeType=2: Heavy-duty passage, which only allows AGV robots performing heavy handling tasks to pass through.
[0046] Step SA003: Based on the task type, determine the set of target edge types that allow the AGV robot to pass through. Specifically, based on the task type determined in step SA001, query the predefined matching rules. If the task type is "normal operation," the set of allowed edge types is {0}; if the task type is "light handling," the set of allowed edge types is {0,1}; if the task type is "heavy handling," the set of allowed edge types is {0,2}. It can be seen that in this scheme, each task type corresponds to multiple target edge types. The advantage of this design is that AGV robots performing light handling tasks can use either the light handling lane or, when the light handling lane is congested, the general lane; the same applies to heavy handling AGVs. AGV robots performing normal tasks can only use the general lane, avoiding the occupation of light or heavy handling lane resources.
[0047] Step SA004: Based on the target edge type set, execute a path search algorithm to search the dynamic map for a sequence of traversable nodes from the starting node to the target node, where each edge's type attribute belongs to the target edge type set. Specifically, the path search algorithm is either the A* algorithm or Dijkstra's algorithm. Preferably, when the allowed target edge type set contains multiple edge types, the path search algorithm prioritizes dedicated edge types (edge types that strictly match the task type), and only selects a general path when a traversable path of a dedicated edge type is unreachable.
[0048] Step SA005: Perform a locking operation on the passageway corresponding to the passable node sequence, prohibiting other AGV robots from using the locked passageway; specifically, mark all edges corresponding to the passable node sequence as "locked". Preferably, set a locking identifier and locking time based on the locked passageway, and record the identifier of the AGV robot that locked the path; after locking, other AGV robots will directly exclude these edges when planning their paths, thereby avoiding conflicts.
[0049] Step SA006: Generate navigation instructions based on the locked sequence of passable nodes to control the AGV robot's movement; specifically, generate specific navigation instructions to move from node A to node B at a speed of 1m / s, and send them to the AGV robot for execution.
[0050] Step SA007: Based on the AGV robot's arrival at the target node, unlock the corresponding travel path. Specifically, after the AGV robot arrives at the target node, it sends an arrival signal to the central scheduling system. The scheduling system clears the lock markers of all edges locked by the AGV, restoring it to an unlocked state for subsequent use by other AGVs. Preferably, in step SA007, if the locking time exceeds a preset threshold and the AGV robot has still not arrived at the target node, the lock is automatically unlocked, an abnormal alarm message is generated, and the AGV robot is marked as out of contact, and its travel path is replanned.
[0051] The present invention also provides a channel dynamic planning system based on robot task type, for implementing the method described in any of the above embodiments, the system comprising:
[0052] The task acquisition module is used to acquire the current task of the AGV robot and determine its task type; based on this, it can connect with the scheduling system to receive AGV task instructions in real time and parse the task type.
[0053] The map building module is used to build dynamic maps, in which each travel path is configured with an edge type attribute; accordingly, it is responsible for storing and managing the dynamic map, including node coordinates, edge connection relationships, and the EdgeType attribute of each edge.
[0054] The path planning module is used to determine the allowed target edge types based on the task type and execute a path search algorithm to generate a sequence of traversable nodes containing only the paths of the target edge types. Based on this, the A* or Dijkstra algorithm is implemented to determine the set of allowed target edge types based on the task type and output the sequence of traversable nodes.
[0055] The path locking module is used to perform a locking operation on the passage path corresponding to the sequence of passable nodes; thereby, it manages the locking status of the path and provides locking, unlocking, and timeout detection functions.
[0056] The navigation control module is used to generate navigation path instructions based on the locked node sequence to control the AGV robot's movement.
[0057] The unlocking module is used to release the lock after the AGV robot reaches the target node. Based on this, the node sequence is converted into motion control commands and sent to the corresponding AGV robot.
[0058] The unlocking module is used to release the lock after the AGV robot reaches the target node. Accordingly, it performs the unlocking operation in response to the AGV's arrival event or timeout event.
[0059] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above. More specifically, examples include non-volatile memory (ROM, flash memory), hard disks, optical disks, etc., having a computer program stored thereon. When the program is executed by a processor (such as an industrial computer, embedded controller), it can implement all the steps in the above method embodiments.
[0060] In summary, this application can be widely applied to scenarios requiring collaborative operations of multiple AGV robots, such as warehousing and logistics, and factories. By dynamically matching task types with channel types, it improves channel utilization and system throughput, reduces traffic congestion and deadlock risks, and has significant industrial practical value.
[0061] The foregoing has provided a detailed description of a channel dynamic planning method, system, and storage medium based on robot task types provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas and methods of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
[0062] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A channel dynamic planning method based on robot task types, characterized in that, The method includes the following steps: Step SA001: Obtain the current task of the AGV robot and determine the task type corresponding to the current task; Step SA002: Construct a dynamic map, which includes multiple nodes and paths connecting adjacent nodes; wherein each path is configured with an edge type attribute, which indicates the type of task that is allowed to pass. Step SA003: Based on the task type, determine the set of target edge types that allow the AGV robot to pass; Step SA004: Based on the target edge type set, execute the path search algorithm to search the dynamic map for a sequence of passable nodes from the starting node to the target node, where the edge type attribute of each edge belongs to the target edge type set; Step SA005: Perform a locking operation on the passage path corresponding to the passable node sequence to prevent other AGV robots from using the locked passage path; Step SA006: Generate navigation path instructions based on the locked sequence of passable nodes to control the AGV robot to move; Step SA007: Based on the AGV robot reaching the target node, unlock the corresponding passage path.
2. The channel dynamic planning method based on robot task type according to claim 1, characterized in that, The edge type attributes include a first edge type, a second edge type, and a third edge type; the task types include heavy cargo handling tasks, light cargo handling tasks, and general operation tasks.
3. The channel dynamic planning method based on robot task type according to claim 2, characterized in that, The matching rule between the edge type attribute and the task type is as follows: The first edge type corresponds to EdgeType=0, which allows AGV robots performing ordinary tasks, light handling tasks, and heavy handling tasks to pass through; The second edge type corresponds to EdgeType=1, which only allows AGV robots performing light handling tasks to pass through; The third edge type corresponds to EdgeType=2, which only allows AGV robots performing heavy handling tasks to pass through.
4. The channel dynamic planning method based on robot task type according to claim 3, characterized in that, The path with the edge type attribute set to the first edge type is configured as a general passage; Paths with the edge type attribute set to the second edge type are configured as lightweight channels; Paths with the edge type attribute set to the third edge type are configured as heavy passages.
5. The channel dynamic planning method based on robot task type according to claim 1, characterized in that, The path search algorithm is either the A* algorithm or the Dijkstra algorithm.
6. The channel dynamic planning method based on robot task type according to claim 1, characterized in that, In step SA005, when performing a locking operation on the passage path, a locking identifier and locking time are set based on the locked passage path, and the identifier of the AGV robot that locked the path is recorded.
7. The channel dynamic planning method based on robot task type according to claim 6, characterized in that, In step SA007, when the locking time exceeds the preset threshold and the AGV robot still has not reached the target node, the lock is automatically released and an abnormal alarm message is generated. The AGV robot is then marked as disconnected and its travel path is replanned.
8. A channel dynamic planning system based on robot task types, characterized in that, include: The task acquisition module is used to acquire the current task of the AGV robot and determine its task type; The map building module is used to build dynamic maps, in which each travel path is configured with an edge type attribute; The path planning module is used to determine the allowed target edge types based on the task type and execute the path search algorithm to generate a sequence of traversable nodes that contains only the traversable paths of the target edge types. The path locking module is used to perform a locking operation on the passage path corresponding to the sequence of passable nodes; The navigation control module is used to generate navigation path instructions based on the locked node sequence to control the AGV robot's movement. The unlocking module is used to unlock the AGV robot after it reaches the target node.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.