Path planning method, path planning device, autonomous mobile system and storage medium

Abstract

The invention relates to a path planning method, a path planning device, an autonomous mobile system and a storage medium, and the method comprises the steps: determining a starting point and an end point of path planning according to a current node of the autonomous mobile device in a topological map; determining a communication relationship between adjacent nodes in the topological map; carrying out deep search on the communication relation of each node from the starting point to the ending point, and generating a target sequence node set; and determining a full-coverage node set according to a communication relationship between adjacent nodes in the target sequence node set, and determining a full-coverage path according to the full-coverage node set. According to the method, node full-coverage path planning based on the topological map can be realized on the basis of communication relation analysis of each node of the topological map.

Description

Technical Field

[0001] The present application relates to the field of path planning, and in particular to a path planning method, a path planning device, an autonomous mobile system, and a storage medium. Background Art

[0002] Autonomous mobile devices such as Automated Guided Vehicles (AGVs) are often responsible for towing or carrying goods in industrial operations. They usually plan automatic driving routes from the starting point to the end point based on preset maps. Among them, since autonomous mobile devices such as AGVs often strictly follow the map route, their tires will leave marks on the route under high-density work. Therefore, full coverage path planning of the map is required to clean the marks on the route. In addition, the correction of node coordinates and the inspection of fixed nodes in special scenarios require AGVs to be able to plan paths with full coverage of the nodes.

[0003] Currently, related technologies typically use a known grid map. When the grid is empty, a route starting point is set; when the grid contains an obstacle, a route endpoint is set. The resulting route is then divided into cleaning areas based on the obstacle information, and the cleaning areas are then connected using the nearest neighbor principle. This approach is not suitable for AGV scenarios that rely primarily on topological maps, and cannot meet the requirements for planning paths that fully cover topological map nodes.

[0004] There is currently no effective solution to the problem in related technologies that it is impossible to achieve full coverage of path planning for topology map nodes. Summary of the Invention

[0005] In this embodiment, a path planning method, a path planning device, an autonomous mobile system and a storage medium are provided to solve the problem in related technologies that path planning for full coverage of topological map nodes cannot be achieved.

[0006] In a first aspect, this embodiment provides a path planning method for an autonomous mobile device, the method comprising:

[0007] Determining the starting point and end point of the path planning according to the node where the autonomous mobile device is currently located in the topological map;

[0008] Determining connectivity relationships between adjacent nodes in the topological map;

[0009] Performing a depth search of the connectivity relationship of each node from the starting point to the end point to generate a target sequential node set;

[0010] A full coverage node set is determined according to the connectivity relationship between adjacent nodes in the target sequential node set, and a full coverage path is determined according to the full coverage node set.

[0011] In some embodiments, determining the starting point and the end point of the path planning according to the node where the autonomous mobile device is currently located in the topological map includes:

[0012] Obtaining a current position of the autonomous mobile device;

[0013] According to the current position, it is determined whether the autonomous mobile device is located on a node of the topological map; if so, the starting point and end point of the path planning are determined according to the node where the autonomous mobile device is currently located in the topological map.

[0014] In some embodiments, determining the connectivity relationship between adjacent nodes in the topological map includes:

[0015] According to the topology map, it is determined whether adjacent nodes have a unidirectional connectivity relationship or a bidirectional connectivity relationship.

[0016] In some embodiments, performing a deep search of connectivity relationships of nodes from the starting point to the end point to generate a target sequential node set includes:

[0017] Based on the depth-first search algorithm, starting from the starting point, adjacent nodes with connectivity are searched in sequence toward the end point to generate a target sequential node set.

[0018] In some embodiments, determining a full coverage node set based on connectivity relationships between adjacent nodes in the target sequential node set, and determining a full coverage path based on the full coverage node set includes:

[0019] The two adjacent nodes in the target sequential node set are taken as target node pairs in turn, and it is determined whether there is a unidirectional connectivity relationship or a bidirectional connectivity relationship between the target node pairs; if so, the target node pair is added to the full coverage node set, and the planned path between the target node pairs is determined based on the connectivity relationship between the target nodes.

[0020] In some embodiments, if the target node pair does not have a unidirectional connectivity relationship and does not have a bidirectional connectivity relationship, the method further includes:

[0021] According to a preset path search algorithm, determine whether there is a plannable path between the target node pair; if so, generate a planned path between the target node pair, and add the target node pair to the full coverage node set;

[0022] Otherwise, it is confirmed that there is no connectivity between the target node pair.

[0023] In some embodiments, determining a full coverage path according to the full coverage node set includes:

[0024] A full coverage path is determined according to the planned paths between the nodes in the full coverage node set.

[0025] In a second aspect, a path planning device is provided in this embodiment for use with an autonomous mobile device, comprising: a first determination module, a second determination module, a search module, and a path generation module; wherein:

[0026] The first determining module is configured to determine a starting point and an end point of the path planning according to the node where the autonomous mobile device is currently located in the topological map;

[0027] The second determining module is used to determine the connectivity relationship between adjacent nodes in the topological map;

[0028] The search module is configured to perform a depth search of the connectivity relationship of each node from the starting point to the end point to generate a target sequential node set;

[0029] The path generation module is used to determine a full coverage node set according to the connectivity relationship between adjacent nodes in the target sequential node set, and determine a full coverage path according to the full coverage node set.

[0030] In a third aspect, an autonomous mobile system is provided in this embodiment, comprising: an autonomous mobile device and a processor; the autonomous mobile device and the processor are communicatively connected; and the processor is configured to execute the path planning method described in the first aspect above.

[0031] In a fourth aspect, a storage medium is provided in this embodiment, on which a computer program is stored. When the program is executed by a processor, the path planning method described in the first aspect is implemented.

[0032] Compared to related technologies, this embodiment provides a path planning method, a path planning device, an autonomous mobile system, and a storage medium. The path planning method determines the starting and ending points of the path planning based on the node the autonomous mobile device is currently located at in the topological map; determines the connectivity relationships between adjacent nodes in the topological map; performs a deep search of the connectivity relationships of each node from the starting point to the end point to generate a target sequential node set; determines a fully covered node set based on the connectivity relationships between adjacent nodes in the target sequential node set, and determines a fully covered path based on the fully covered node set. This method can implement node-wide coverage path planning based on the topological map based on the analysis of the connectivity relationships of each node in the topological map.

[0033] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0035] Figure 1 This is a hardware structure block diagram of a terminal of the path planning method according to an embodiment of the present application;

[0036] Figure 2 is a flow chart of a path planning method according to an embodiment of the present application;

[0037] Figure 3 This is a schematic diagram of a topological map node in an embodiment of the present application;

[0038] Figure 4 This is a schematic diagram of a node connectivity relationship in an embodiment of the present application;

[0039] Figure 5 This is a schematic diagram of a node connection relationship in an embodiment of the present application;

[0040] Figure 6 This is a schematic diagram of a full coverage path generation embodiment of the present application;

[0041] Figure 7 It is a schematic diagram of a raster map in the related art;

[0042] Figure 8 This is a schematic diagram of a topological map applicable to the embodiments of the present application;

[0043] Figure 9 This is a schematic diagram of the positional relationship between an autonomous mobile device and a node according to an embodiment of the present application;

[0044] Figure 10 is a flow chart of a path planning method according to some embodiments of the present application;

[0045] Figure 11 is a structural block diagram of a path planning device according to an embodiment of the present application;

[0046] Figure 12 It is a structural diagram of the autonomous mobile system of an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0048] Unless otherwise defined, technical or scientific terms used in this application shall have the ordinary meanings as understood by persons of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," "these," and similar expressions in this application do not denote limitations on quantity and may be singular or plural. The terms "comprise," "include," "have," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include unlisted steps or modules (units) or other steps or modules (units) inherent to the process, method, product, or device. The terms "connected," "connected," "coupled," and similar expressions used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. As used in this application, "plurality" means two or more. "And / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone; A and B exist simultaneously; or B exists alone. Generally, the character " / " indicates that the objects in the preceding and following relationship are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0049] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 FIG is a block diagram of the hardware structure of the terminal of the path planning method of this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1 The processor 102 (only one is shown) and a memory 104 for storing data, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The terminal may also include a transmission device 106 for communication functions and an input / output device 108. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0050] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the path planning method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0051] Transmission device 106 is used to receive or transmit data via a network. This network may include a wireless network provided by the terminal's communications provider. In one embodiment, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0052] In this embodiment, a path planning method is provided. Figure 2 is a flow chart of the path planning method of this embodiment. Figure 2 As shown, the process includes the following steps:

[0053] Step S210 , determining the starting point and the end point of the path planning according to the node where the autonomous mobile device is currently located in the topology map.

[0054] The autonomous mobile device can specifically be a device that uses an automatic guidance method to achieve unmanned driving and autonomous completion of mobile tasks, such as an AGV, an autonomous mobile robot (AMR), or an automatic navigation forklift. For the sake of convenience, the following mainly uses AGV as an example. A topological map is an environmental model used to describe the key nodes (such as sites, intersections) and their connection relationships in the workspace of an autonomous mobile device. A topological map can abstract a complex physical environment into a graph structure of nodes and edges, thereby simplifying path planning. Therefore, in order to perform full coverage path planning for an AGV based on a topological map, it is necessary to first obtain a topological map of the AGV in the corresponding operating scenario.

[0055] The starting and ending points for full coverage path planning can be determined by traversing the nodes of the topological map. Specifically, the starting and ending points of the full coverage path can be determined by determining that the AGV is at a certain node on the topological map. For example, the node currently located by the AGV is used as the starting point, and the node farthest from the current node is used as the ending point. Figure 3 A schematic diagram of a topology map node in this embodiment, such as Figure 3 As shown in the figure, the pentagon represents the AGV, and each circle with a letter number represents a node, such as node A to node L. The lines represent the connectivity between the nodes, and the arrows represent the connectivity direction. Figure 3 Node A where the AGV is located is used as the starting point of the full coverage path planning, and node L is used as the end point of the full coverage path. It is necessary to generate a path that can cover all nodes from node A to node L.

[0056] Step S220: Determine the connectivity relationship between adjacent nodes in the topology map.

[0057] In the topological map, adjacent nodes may have a one-way connectivity, a two-way connectivity, or a disconnected connectivity. Figure 4 This is a schematic diagram of node connectivity in this embodiment. Figure 4 As shown, there is a bidirectional connectivity relationship between node A and node B, a unidirectional connectivity relationship between node A and node C, and node D is independent of nodes A, B, and C. By traversing the topology map, it can be determined whether each adjacent node has a unidirectional connectivity relationship, a bidirectional connectivity relationship, or no connectivity relationship.

[0058] Among them, parent-child node attributes can be constructed based on the connectivity relationship between nodes. For example, Figure 4 If nodes A and B have a bidirectional connectivity, then they are each other's parent and child nodes. If node A is unidirectionally connected to node C, but node C cannot be connected to node A, then node A is the parent of node C. If nodes B and C cannot be connected, then there is no parent-child relationship between nodes BC. If node D is independent of the other nodes, then there is no parent-child relationship between node D and the other nodes.

[0059] Step S230 , performing a depth search of the connectivity relationship of each node from the starting point to the end point, and generating a target sequential node set.

[0060] Based on a preset search algorithm, starting from a starting point, nodes with a connected relationship can be searched layer by layer. The search algorithm can be an algorithm suitable for tree structure search or graph structure search. Specifically, the parent-child node set can be searched starting from the starting point. If the node currently being searched has child nodes, the child nodes of the current node are searched continuously until the node currently being searched no longer has no child nodes. In this way, the target sequential node set can be generated according to the search order. Figure 5 This is a schematic diagram of a node connection relationship in this embodiment, such as Figure 5 As shown, starting from node A, a deep search of the connectivity relationship of each node is performed, and the parent-child node set is searched, then the target sequential node set can be found as: "ABCDE".

[0061] Step S240 , determining a full coverage node set based on the connectivity relationship between adjacent nodes in the target sequential node set, and determining a full coverage path based on the full coverage node set.

[0062] The target sequential node set can be traversed sequentially to determine whether any two adjacent nodes are connected, and a path between them can be planned. If a path can be planned between the two nodes, they are connected and can be added to the fully covered node set. If a node is not connected to any other nodes, it is an isolated node and needs to be filtered out. Finally, the fully covered path can be determined based on the planned paths between all nodes in the fully covered node set.

[0063] If two nodes have the above-mentioned parent-child node relationship, then the two nodes have a connectivity relationship. Figure 6 This is a schematic diagram of a full coverage path generation in this embodiment, combined with Figure 5 and Figure 6 , the corresponding target sequential node set is "ABCDE", then traverse the target sequential node set, and judge whether node A and node B, node B and node C, node C and node D, node D and node E are parent-child nodes; if so, add the two nodes to the full coverage node set. For example, node A and node B are parent-child nodes, and node B and node C are also parent-child nodes, then add them to the full coverage node set, and there are paths "AB" and "BA" between node A and node B, and paths "BC" and "CB" between node B and node C. If node C and node D are not parent-child nodes, then based on the preset path planning algorithm, it can be judged whether the path between node C and node D can be successfully planned. If so, it indicates that there is connectivity between node C and node D, and node C and node D are added to the full coverage node set. Otherwise, confirm that there is no connectivity between node C and node D. In Figure 6In the example, the path from node C to node D can be "ABCBAD", so node D can also be added to the full coverage node set. Then, the full coverage path is determined based on the paths between the nodes in the full coverage node set. For example, Figure 6 The full coverage path in can be "ABCBADE".

[0064] In related technologies, a known grid map is generally used. When the grid is empty, the route starting point is set. When the grid is an obstacle, the route end point is set. The generated route is divided into cleaning areas based on the obstacle information, and the cleaning areas are then connected according to the nearest neighbor principle. This method of related technology is more functionally suitable for operating scenarios of equipment such as cleaning robots, and has higher requirements for grid maps and robot positioning. In AGV scenarios that rely on topological maps as the main map, the path planning requirement of full coverage of topological map nodes cannot be met. Figure 7 Schematic diagram of a grid map in related technology. Figure 8 This is a schematic diagram of a topological map applicable to this embodiment. Figure 7 In the figure, black rectangles represent obstacles, white rectangles represent the feasible area, and the letters D and T correspond to grid cells, for example, T1 to T7 and D1 to D7. Line segments A1B1, A2B2, A3B3, A4B4, A5B5, A6B6, and A7B7 represent planned routes. The distance between two routes is the robot's width. This shows that in grid maps, the relevant technology aims to use routes to fully cover the feasible area of the grid map. Figure 8 In , circles with letter numbers represent nodes, e.g. Figure 8 From node A to node L in the figure, the line segment with an arrow represents the connection relationship between the nodes. Figure 8 In this topological map, the robot moves along the necessary path through the lines with direction constraints between nodes A, B, C, and D. Therefore, if the relevant technology requires the robot to fully cover the map, Figure 8 Therefore, the full coverage path planning method of the related technology cannot meet the planning requirements of the topology map nodes.

[0065] To this end, this embodiment implements full-coverage path planning for topological maps. By determining and searching for node connectivity, this enables full-coverage path planning for autonomous mobile devices traveling along fixed routes or passing through necessary waypoints. This provides a method for autonomous mobile devices based on topological maps to fully traverse every topological node. Furthermore, the path nodes generated by this embodiment are continuous and can be directly used by robots. A method with lower repetition rates can be selected for path coverage to filter out isolated nodes.

[0066] Therefore, through the above steps S210 to S240, the starting point and end point of the path planning are determined based on the node currently located by the autonomous mobile device in the topological map; the connectivity relationship between each adjacent node in the topological map is determined; a deep search of the connectivity relationship of each node is performed from the starting point to the end point to generate a target sequential node set; based on the connectivity relationship between each adjacent node in the target sequential node set, a full coverage node set is determined, and based on the full coverage node set, a full coverage path is determined. This can achieve node full coverage path planning based on the topological map based on the analysis of the connectivity relationship of each node in the topological map.

[0067] In one embodiment, based on the above step S210, determining the starting point and end point of the path planning according to the node where the autonomous mobile device is currently located in the topology map may specifically include:

[0068] Obtain the current position of the autonomous mobile device; based on the current position, determine whether the autonomous mobile device is located at a node in the topological map; if so, determine the starting point and end point of the path planning based on the node where the autonomous mobile device is currently located in the topological map.

[0069] First, the current position of the autonomous mobile device in the topological map can be determined. The current position represents the position and posture of the autonomous mobile device in the map system at the current moment, which can be obtained by fusion of sensor data such as inertial measurement unit, lidar, and visual sensor. Based on the current position and the position information of each node in the topological map, it is determined whether the autonomous mobile device is located at a certain node in the topological map. Figure 3 In the example, the autonomous mobile device is located on node A. Figure 9 This is a schematic diagram of the position relationship between an autonomous mobile device and a node in this embodiment. Figure 9 As shown, the pentagon represents the location of the autonomous mobile device, the circle with a letter number represents each node in the topological map, and the line segment with an arrow represents the connection between the nodes, indicating the connectivity relationship between the nodes. Figure 9 , it can be seen that the autonomous mobile device is not located at any node on the topology map at this time. At this time, the starting point of the full coverage path cannot be determined. Therefore, it is necessary to end the planning of the full coverage path until the autonomous mobile device moves to a certain node.

[0070] In this embodiment, whether to start full coverage path planning and determining the start and end points of the full coverage path planning are determined based on the current position of the autonomous mobile device, which can improve the efficiency of the full coverage path planning.

[0071] Additionally, in one embodiment, determining the connectivity relationship between adjacent nodes in the topological map may specifically include:

[0072] According to the topological map, determine whether there is a unidirectional or bidirectional connectivity relationship between adjacent nodes. Among them, the topological map can be represented by a directed graph (including nodes and edges). Therefore, the connectivity relationship between adjacent nodes in the topological map can be determined based on whether the connection edge between the nodes is a unidirectional edge, a bidirectional edge, or has no edge. Alternatively, the connectivity relationship between adjacent nodes can be determined based on the node adjacency list or adjacency matrix. Then, the parent-child node attributes are constructed based on this connectivity relationship. For example, in Figure 4 In the example, if nodes A and B are bidirectionally connected to each other, they are each other's parent and child nodes. If node A is unidirectionally connected to node C, node A is the parent node of node C.

[0073] This embodiment determines the connectivity between adjacent nodes based on the topology map, and can provide an accurate planning basis for full coverage path planning.

[0074] Furthermore, in one embodiment, a deep search of the connectivity relationship of each node is performed from the starting point to the end point to generate a target sequential node set, which may specifically include:

[0075] Based on the depth-first search algorithm, starting from the starting point, the adjacent nodes with connectivity are searched in sequence toward the end point to generate a target sequential node set. The depth-first search algorithm (DFS) can be used to start from the node (starting point) where the autonomous mobile device is currently located and search for parent and child nodes to obtain the target sequential node set. It can be understood that the target sequential node set is a DFS sequential node set. For example Figure 5 In the example, the DFS algorithm takes node A as the starting point, and the resulting DFS sequence nodes are "ABCDE".

[0076] Because the DFS algorithm is a vertically in-depth search algorithm, it can start from the starting node and follow a path as deeply as possible until it can no longer continue (i.e., reaches a leaf node or a dead end), then backtrack to the previous fork node and try other branches. Therefore, in this embodiment, the DFS algorithm is used to traverse and search parent and child nodes from the starting point, which can complete the structural depth traversal of parent and child nodes with high efficiency. As a result, the generated target sequential node set can reflect the connectivity relationship of the nodes in the original topological map, thereby improving the efficiency of node search and the accuracy of determining connectivity relationships.

[0077] Furthermore, in one embodiment, determining a full coverage node set based on connectivity relationships between adjacent nodes in the target sequential node set, and determining a full coverage path based on the full coverage node set may include:

[0078] The target sequential node set is sequentially treated as a target node pair, and the two adjacent nodes in the target sequential node set are determined to determine whether the target node pairs have a unidirectional or bidirectional connectivity relationship. If so, the target node pair is added to the fully covered node set, and the planned path between the target node pairs is determined based on the connectivity relationship between the target nodes. In other words, each pair of adjacent nodes in the target sequential node set is treated as a target node pair, and the relationship between the target node pairs is determined to determine whether the target node pairs have a unidirectional or bidirectional connectivity relationship. If so, the target node pair is added to the fully covered node set. For example, if the target sequential node set is "ABCDE", the target sequential node set is traversed, and the relationship between nodes A and B, B and C, C and D, and D and E is determined to be parent-child nodes. If so, the two nodes are added to the fully covered node set. For example, nodes A and B are first treated as a target node pair. After determining that nodes A and B are parent-child nodes, both nodes A and B are added to the fully covered node set. After that, take nodes B and C as a set of target node pairs. After determining that nodes B and C are also parent-child nodes, add node C to the full coverage node set, and so on. According to the connectivity relationship between nodes, determine the planning path between nodes, for example Figure 5 In the example, the paths between node A and node B include "AB" and "BA".

[0079] In this embodiment, by analyzing the connectivity relationship between adjacent nodes in the target sequential node set, a planned path can be quickly determined for nodes with direct connectivity, thereby improving the efficiency of full coverage path planning.

[0080] In one embodiment, if there is no unidirectional connectivity relationship and no bidirectional connectivity relationship between the target node pairs, the path planning method may further include:

[0081] According to the preset path search algorithm, determine whether there is a plannable path between the target node pair; if so, generate a planned path between the target node pair and add the target node pair to the full coverage node set; otherwise, confirm that there is no connectivity between the target node pair.

[0082] For target node pairs that do not have direct connectivity (one-way connectivity, two-way connectivity) in the target sequential node set, a path planning algorithm can be used to determine whether the two nodes of the target node pair have connectivity. Specifically, the fast A-star algorithm can be used to plan the path between the two nodes. For example, Figure 5Nodes C and D in the example do not have a direct connectivity relationship and are therefore not considered parent-child nodes. Therefore, the Fast A-Star algorithm can be used to plan a path between nodes C and D. If a path is successfully planned, it indicates that nodes C and D are connected, so node D is added to the fully covered node set. If a path is not successfully planned, it indicates that nodes C and D are not connected, so node D cannot be added to the fully covered node set.

[0083] In this embodiment, path planning can be implemented for nodes that do not have direct connectivity through a path planning algorithm, thereby completing full coverage planning for each node on the topological map, accurately filtering out isolated and unreachable nodes, and reducing the repetition rate of path nodes.

[0084] In one embodiment, determining a full coverage path based on a full coverage node set may include:

[0085] The full coverage path is determined based on the planned paths between each node in the full coverage node set. For nodes with direct connectivity, a corresponding planned path can be directly generated based on the connectivity relationship. For nodes that do not have a direct connectivity but are actually connected, a path can also be successfully planned using a path planning algorithm, such as the aforementioned fast A-star algorithm. Therefore, in the full coverage node set, the paths between each node are continuous and can be directly used by autonomous mobile devices. The paths filter out unreachable nodes, and the repetition rate of path nodes is low.

[0086] Figure 10 is a flow chart of a path planning method in some embodiments, such as Figure 10 As shown, the path planning method includes the following steps:

[0087] Step S1001, obtaining the current posture of the AGV.

[0088] Step S1002: Determine whether the AGV is located on a node of the topology map based on the current position; if so, execute step S1003; otherwise, end the process.

[0089] Step S1003: construct parent-child node attributes according to the connectivity relationship between adjacent nodes in the topological map.

[0090] Step S1004: Using the DFS algorithm, starting from the node where the AGV is currently located, search for the parent and child node sets to obtain the DFS sequential node set.

[0091] Step S1005, traverse the DFS sequential node set to determine whether the two adjacent nodes are parent-child nodes; if so, execute step S1006; otherwise, execute step S1007.

[0092] Step S1006: Add the node to the full coverage node set. Execute step S1009.

[0093] Step S1007: Use the fast A-star algorithm to connect the two adjacent nodes.

[0094] Step S1008, determine whether the fast A-star algorithm is successful, if so, execute step S1006; otherwise, end the process.

[0095] Step S1009, determine whether the DFS traversal is completed; if so, end the process; otherwise, return to step S1005.

[0096] The above steps S1001 to S1009 can achieve full coverage path planning based on the topological map, and can fully cover the nodes when the AGV is traveling on a fixed route or must pass through necessary waypoints. This embodiment can provide a method for autonomous mobile devices such as AGVs based on topological maps to traverse each topological node with full coverage. The generated path nodes are continuous and can be used directly by robots. In addition, a method with a lower repetition rate is selected for the path coverage method, and isolated nodes are filtered out. Therefore, all nodes on the global topological map can be reached, meeting the full coverage requirements in scenarios such as fixed node inspection, fixed route cleaning, and correction of other data according to topological nodes.

[0097] This embodiment also provides a path planning device for implementing the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated. The terms "module," "unit," "subunit," etc. used below may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0098] Figure 11 is a structural block diagram of the path planning device 11 of this embodiment, as shown in FIG. Figure 11 As shown, the path planning device 11 includes: a first determination module 112, a second determination module 114, a search module 116, and a path generation module 118; wherein:

[0099] The first determination module 112 is used to determine the starting point and end point of the path planning based on the node where the autonomous mobile device is currently located in the topological map; the second determination module 114 is used to determine the connectivity relationship between adjacent nodes in the topological map; the search module 116 is used to perform a deep search of the connectivity relationship of each node from the starting point to the end point to generate a target sequential node set; the path generation module 118 is used to determine the full coverage node set based on the connectivity relationship between adjacent nodes in the target sequential node set, and determine the full coverage path based on the full coverage node set.

[0100] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0101] This embodiment also provides an autonomous mobile system. Figure 12 is a structural diagram of the autonomous mobile system 12 of this embodiment, as shown in FIG. Figure 12 As shown, the autonomous mobile system 12 includes: an autonomous mobile device 122 and a processor 124; the autonomous mobile device 122 and the processor 124 are in communication with each other; and the processor 124 is used for the path planning method provided in any of the above embodiments.

[0102] It should be noted that, for specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0103] In addition, in conjunction with the path planning method provided in the above embodiments, a storage medium may be provided in this embodiment to implement the path planning method. The storage medium stores a computer program that, when executed by a processor, implements any one of the path planning methods in the above embodiments.

[0104] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0105] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0106] Obviously, the accompanying drawings are merely examples or embodiments of the present application. A person skilled in the art can also apply the present application to other similar situations based on these drawings without inventive effort. Furthermore, it is understandable that, although the work involved in this development process may be complex and lengthy, certain design, manufacturing, or production changes based on the technical content disclosed in this application are merely routine technical means for a person skilled in the art and should not be considered to constitute a deficiency in the disclosure of the present application.

[0107] The term "embodiment" as used in this application refers to specific features, structures, or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily mean that the embodiment is the same, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is understood, either explicitly or implicitly, by those skilled in the art that the embodiments described in this application can be combined with other embodiments when there is no conflict.

[0108] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A path planning method, characterized in that: For an autonomous mobile device, the method comprises: Determining the starting point and end point of the path planning according to the node where the autonomous mobile device is currently located in the topological map; Determining connectivity relationships between adjacent nodes in the topological map; Performing a depth search of the connectivity relationship of each node from the starting point to the end point to generate a target sequential node set; A full coverage node set is determined according to the connectivity relationship between adjacent nodes in the target sequential node set, and a full coverage path is determined according to the full coverage node set.

2. The path planning method according to claim 1, characterized in that: Determining the starting point and the end point of the path planning according to the node where the autonomous mobile device is currently located in the topological map includes: Obtaining a current position of the autonomous mobile device; According to the current position, it is determined whether the autonomous mobile device is located on a node of the topological map; if so, the starting point and end point of the path planning are determined according to the node where the autonomous mobile device is currently located in the topological map.

3. The path planning method according to claim 1, wherein: Determining the connectivity relationship between adjacent nodes in the topological map includes: According to the topology map, it is determined whether adjacent nodes have a unidirectional connectivity relationship or a bidirectional connectivity relationship.

4. The path planning method according to claim 1, wherein: Performing a deep search of the connectivity relationship of each node from the starting point to the end point to generate a target sequential node set, including: Based on the depth-first search algorithm, starting from the starting point, adjacent nodes with connectivity are searched in sequence toward the end point to generate a target sequential node set.

5. The path planning method according to claim 1, wherein: Determining a full coverage node set according to connectivity relationships between adjacent nodes in the target sequential node set, and determining a full coverage path according to the full coverage node set, including: The two adjacent nodes in the target sequential node set are taken as target node pairs in turn, and it is determined whether there is a unidirectional connectivity relationship or a bidirectional connectivity relationship between the target node pairs; if so, the target node pair is added to the full coverage node set, and the planned path between the target node pairs is determined based on the connectivity relationship between the target nodes.

6. The path planning method according to claim 5, characterized in that: If the target node pair does not have a unidirectional connectivity relationship and does not have a bidirectional connectivity relationship, the method further includes: According to a preset path search algorithm, determine whether there is a plannable path between the target node pair; if so, generate a planned path between the target node pair, and add the target node pair to the full coverage node set; Otherwise, it is confirmed that there is no connectivity between the target node pair.

7. The path planning method according to claim 5, characterized in that: Determining a full coverage path according to the full coverage node set includes: A full coverage path is determined according to the planned paths between the nodes in the full coverage node set.

8. A path planning device, characterized in that: For an autonomous mobile device, comprising: a first determination module, a second determination module, a search module, and a path generation module; wherein: The first determining module is configured to determine a starting point and an end point of the path planning according to the node where the autonomous mobile device is currently located in the topological map; The second determining module is used to determine the connectivity relationship between adjacent nodes in the topological map; The search module is configured to perform a depth search of the connectivity relationship of each node from the starting point to the end point to generate a target sequential node set; The path generation module is used to determine a full coverage node set according to the connectivity relationship between adjacent nodes in the target sequential node set, and determine a full coverage path according to the full coverage node set.

9. An autonomous mobile system, characterized in that: include: autonomous mobile devices and processors; The autonomous mobile device is communicatively connected to the processor; The processor is configured to execute the path planning method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the path planning method according to any one of claims 1 to 7 are implemented.

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