Path planning method and device

Abstract

This application discloses a path planning method and apparatus. The method includes searching for child nodes of a current node; calculating the cost of each child node, wherein, when the automated guided vehicle is in a loaded state, the cost of each child node includes the cost of shelf adjustments during the process of the automated guided vehicle moving from the current node to each child node; determining the next node of the current node based on the costs of the child nodes; and completing path planning based on the next node. This application can prevent frequent shelf adjustments during a planned path, thereby ensuring smooth and fluid operation of the path.

Description

Technical Field

[0001] The present application relates to the technical field of path planning, and in particular to a path planning method and device. Background Art

[0002] In recent years, the rapid expansion of Automated Guided Vehicles (AGVs) in the industrial sector has significantly driven the advancement of Industry 4.0. AGVs are increasingly appearing in factories and workshops, gradually replacing manual labor and becoming one of the most critical pieces of equipment in smart factories. However, current path planning methods for AGVs are not very intelligent, resulting in low operational efficiency. Summary of the Invention

[0003] The present application provides a path planning method and device, which can prevent frequent adjustments of shelves in a planned path, so as to ensure smooth and fluent operation of the path as much as possible.

[0004] To achieve the above objectives, the present application provides a path planning method, which includes:

[0005] Search for the child nodes of the current node;

[0006] Calculate the cost of each child node, wherein when the automated guided vehicle is in a loaded state, the cost of each child node includes the shelf adjustment cost in the process of the automated guided vehicle moving from the current node to each child node;

[0007] Determine the next node of the current node based on the cost of the child nodes;

[0008] Based on the next node, path planning is completed.

[0009] The shelf adjustment cost includes the shelf motion state switching cost and / or the shelf rotation cost;

[0010] The shelf rotation cost is positively correlated with the shelf rotation angle.

[0011] Among them, searching for the child nodes of the current node previously included:

[0012] Determine whether it is possible to move from the current node to the target point by rotating in place and / or moving in a straight line;

[0013] If yes, confirm that the path planning is completed;

[0014] If not, perform a search for the child nodes of the current node;

[0015] Based on the next node, complete the path planning, including:

[0016] Set the next node as the current node and return to the step of determining whether it is possible to move from the current node to the target point by rotating in place and / or moving in a straight line until the path planning is completed.

[0017] The target point is the node that automatically guides the car back to the predetermined track line.

[0018] If yes, confirm that the path planning is complete, including:

[0019] If so, the search path before the current node and the target point is spliced ​​together, and the search path is used as the path to automatically guide the car back to the predetermined track line.

[0020] Among them, determining whether it is possible to move from the current node to the target point by rotating in place and / or moving in a straight line previously includes:

[0021] When the AGV is in an unloaded state and the destination of the AGV is the center of the shelf, the midpoint of the shelf entrance is used as the target point;

[0022] The method also includes: splicing a straight line path from the target point to the center point of the shelf and the search path to obtain a path of the automatic guided vehicle from the current position of the automatic guided vehicle to the end point.

[0023] The next node of the current node is determined based on the cost of the child node, including:

[0024] The child node with the smallest cost among all the child nodes of the current node is selected as the next node.

[0025] Among them, searching for the child nodes of the current node includes:

[0026] Search all candidate child nodes of the current node;

[0027] Eliminate all candidate child nodes that have been searched and nodes with collision risks to obtain the child node set of the current node.

[0028] The method further includes:

[0029] If, based on the obstacle data and the body state of the automated guided vehicle, it is determined that the automated guided vehicle and the rack loaded thereon may collide with an obstacle while moving from the current node to the candidate child node, then the candidate child node is a node with a collision risk.

[0030] To achieve the above objectives, the present application also provides an electronic device, which includes a processor; the processor is used to execute instructions to implement the above method.

[0031] To achieve the above objectives, the present application also provides a computer-readable storage medium for storing instructions / program data, which can be executed to implement the above method.

[0032] When planning a path, the present application can search for the child nodes of the current node after confirming the current node, so as to determine the next node of the current node based on the costs of all the child nodes of the current node, and then complete the path planning based on the next node; and when the automatic guided vehicle is in a loaded state, the cost of each child node includes the cost of adjusting the shelf in the process of the automatic guided vehicle moving from the current node to each child node. In this way, the adjustment cost of the shelf is taken into account during path planning, which can prevent frequent adjustments of the shelf in the planned path, so as to ensure the smoothness and smooth operation of the path as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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:

[0034] Figure 1 This is a flow chart of an implementation method of the path planning method of the present application;

[0035] Figure 2 This is a flow chart of another embodiment of the path planning method of the present application;

[0036] Figure 3 This is a process framework diagram of an implementation method of the path planning method of the present application;

[0037] Figure 4 This is a schematic structural diagram of an embodiment of the electronic device of the present application;

[0038] Figure 5 It is a structural diagram of an embodiment of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application. In addition, unless otherwise specified (for example, "or in addition" or "or in an alternative"), the term "or" as used herein refers to a non-exclusive "or" (that is, "and / or"). Furthermore, the various embodiments described herein are not necessarily mutually exclusive, because some embodiments can be combined with one or more other embodiments to form new embodiments.

[0040] Specific as Figure 1 As shown, the path planning method of this embodiment includes the following steps. The path planning method of this application can be applied to scenarios where a lifting AGV or a differential AGV operates in an environment such as a logistics warehouse factory, but is of course not limited thereto. It should be noted that the following step numbers are only used to simplify the description and are not intended to limit the order in which the steps are executed. The steps of this embodiment can be arbitrarily changed in order without violating the technical concept of this application.

[0041] S101: Search for child nodes of the current node.

[0042] During path planning, after confirming the current node, the child nodes of the current node may be searched to determine the next node of the current node based on all the child nodes of the current node, and then the path planning is completed based on the next node.

[0043] Initially, the starting node (i.e., the current position of the automatic guided vehicle) can be used as the current node. Then, the path planning method of this embodiment is used to determine the next node based on the current node, and then the next node is used as the current node. Then, if the updated current node does not meet the preset conditions, the path planning method of this embodiment can be continued to determine the next node of the current node until the latest current node meets the preset conditions. If the target point can be reached directly from the current node, the current node meets the preset conditions. In this way, a path from the starting point to the target point can be planned in the above manner, thereby completing the path planning.

[0044] In addition, when the current node in step S101 is the starting node, before step S101, it is also possible to first confirm whether the current node meets the preset conditions; if the current node does not meet the preset conditions, step S101 is executed to plan a path from the starting node to the target point based on the method of the implementation mode of the present application; if the current node meets the preset conditions, the connection between the starting node and the target point is directly used as the path from the starting node to the target point.

[0045] In one implementation, the adjacent nodes of the current node may be used as child nodes of the current node.

[0046] In another implementation, the adjacent nodes of the current node may be used as all candidate child nodes of the current node; then, nodes that have been searched and nodes with collision risks are screened out from all candidate child nodes, and the remaining child nodes are used as all child nodes of the current node.

[0047] When the automatic guided vehicle is a lifting automatic guided vehicle, the method for determining the adjacent nodes of the current node may be: based on the formulas x1=x0+step*cosθ0, y1=y0+step*sinθ0 and θ1=θ0+Δθ, the adjacent nodes of the current node are determined;

[0048] Among them, (x0, y0, θ0) represent the horizontal coordinate, vertical coordinate and angular orientation of the current node in the world coordinate system, (x1, y1, θ1) represent the horizontal coordinate, vertical coordinate and angular orientation of the adjacent node in the world coordinate system, step represents the step size of the search (for example, it can be set to 0.05m), Δθ (where Δθ=θ1-θ0) represents the rotation angle value of the adjacent node. For example, the number of adjacent nodes in the forward direction of the current node can be set to 3. In this case, Δθ can be (Δθ t ,0,-Δθ t ), Δθ t A value of 0.08 or so can be used. By negating the step in the above formula, the adjacent nodes in the backward direction of the current node can be generated.

[0049] In addition, if there is a candidate child node located in the determined path from the starting node (which may be the current position of the automatic guided vehicle) to the current node, then the candidate child node is a node that has been searched.

[0050] If the automated guided vehicle collides with a fixed obstacle or a mobile obstacle such as another automated guided vehicle while moving from the current node to the candidate child node, the candidate child node is a node with a collision risk.

[0051] Furthermore, considering that when the automatic guided cart is in a loaded state, the shelves placed on the automatic guided cart may also be at risk of colliding with obstacles, when confirming the child nodes of the current node, it is possible to determine whether the automatic guided cart and the shelves loaded on it will collide with obstacles in the process of moving from the current node to the candidate child node based on the obstacle data and the body state of the automatic guided cart; if the automatic guided cart and the shelves loaded on it will collide with obstacles, the candidate child node is a node with a collision risk. In this way, the movement of the shelves is taken into account during path planning, so that the planned path can prevent the shelves from colliding with obstacles, thereby ensuring the safety of the goods on the automatic guided cart.

[0052] Among them, the body state of the automatic guided vehicle can include the posture of the automatic guided vehicle (AGV) (absolute coordinates and orientation angle in the world coordinate system) and the body outline. If it is in a loaded state, it can also include the posture of the loaded shelf (absolute coordinates and orientation angle in the world coordinate system) and the outline of the loaded shelf.

[0053] In addition, the above-mentioned obstacle data may include point cloud data of obstacles collected by obstacle avoidance sensors or laser sensors.

[0054] Specifically, before step S101, the point cloud data of the obstacle in the current frame can be obtained, and the point cloud data can be converted into a coordinate point cloud in the world coordinate system and stored so that the point cloud data of the obstacle can be used later to confirm whether the candidate child node has a collision risk. In addition, it can also facilitate the subsequent use of the point cloud data of the obstacle to confirm whether the current node meets the preset conditions.

[0055] In addition, the above-mentioned obstacle data may also include position information of other automated guided vehicles (which may include posture information and / or contour information), so as to avoid the automated guided vehicle from colliding with the automated guided vehicle running nearby.

[0056] S102: Calculate the cost of each child node.

[0057] After searching for the child nodes of the current node, the costs of all the child nodes of the current node may be calculated, so that the next node of the current node can be determined based on the costs of the child nodes of the current node.

[0058] Among them, when the automatic guided vehicle is in a loaded state, the cost of each sub-node may include the shelf adjustment cost in the process of the automatic guided vehicle moving from the current node to each sub-node. In this way, the shelf adjustment cost is taken into account during path planning, which can prevent frequent adjustments of shelves in the planned path, so as to ensure the smoothness and smooth operation of the path as much as possible.

[0059] The shelf adjustment cost may include a shelf motion state switching cost and / or a shelf rotation cost.

[0060] In the process of moving from the current node to each child node, each shelf rotation may generate a shelf rotation cost.

[0061] The cost of each shelf rotation is positively correlated with the rotation angle of the shelf. Specifically, it can be calculated by ks*Δθ h The formula calculates the cost of each shelf rotation. Among them, ks is the cost coefficient, which can be set according to the actual situation and is not limited here. For example, ks can be 5 or 10. h is the rotation angle of the shelf. Specifically, shelf rotation can include two states: keeping the shelf orientation unchanged in the world coordinate system (SHELF_KEEP) or following the AGV swing (SHELF_FOLLOW); each time a child node is generated, if the child node is SHELF_KEEP, the shelf rotation angle Δθ h is -Δθ (i.e., opposite to the direction of vehicle body rotation); if the child node is SHELF_FOLLOW, the shelf does not rotate but swings along with the vehicle body.

[0062] Additionally, when moving from the current node to each child node, if the shelf switches its motion state from following the AGV's swing to maintaining its orientation in the world coordinate system, a shelf motion state switching cost is generated. This shelf motion state switching cost can be a preset value, which can be set based on actual conditions and is not limited here; for example, it can be 5.

[0063] In addition, the cost of each child node may also include a path distance cost and / or a heuristic function. The path distance cost of a child node may be the cost from the current node or the starting node to the child node. The heuristic function may be a heuristic function of the shortest path cost from the child node to the destination point.

[0064] Specifically, the cost of each child node may be equal to the sum of the shelf motion state switching cost, the shelf rotation cost, the path distance cost, and the heuristic function.

[0065] S103: Determine the next node of the current node based on the cost of the child nodes.

[0066] After determining the cost of each child node of the current node, the next node of the current node can be determined based on the cost of the child nodes.

[0067] Optionally, the child node with the lowest cost among all child nodes can be used as the next node of the current node to minimize the cost of the planned path and enable the automatic guided vehicle to run smoothly and quickly to the target point.

[0068] In other implementations, it may be determined whether there is a child node that meets preset conditions among all child nodes of the current node; if so, the child node with the smallest cost among all child nodes of the current node that meet the preset conditions is used as the next node of the current node.

[0069] S104: Complete path planning based on the next node.

[0070] After determining the next node of the current node based on the above steps, path planning can be completed based on the determined next node.

[0071] Optionally, the current node can be updated to the next node, and then it is determined whether the updated current node meets the preset conditions. If so, the process returns to step S101 until the current node meets the preset conditions. If the target point can be reached directly from the current node, the current node meets the preset conditions. In this way, a path from the starting point to the target point can be planned in the above manner, thereby completing the path planning.

[0072] Specifically, it can be confirmed whether it is possible to move from the current node to the target point by rotating in place and / or moving in a straight line; if so, it is confirmed that the current node meets the preset conditions, otherwise the current node does not meet the preset conditions. In this way, by rotating in place and moving in a straight line, planning failures due to encountering obstacles in a limited space can be avoided, and the success rate and efficiency of path planning can be improved. In addition, this method can be applied to differential AGVs that have the characteristic of easy in-place rotation, and has strong applicability.

[0073] The target point may be the end point of the automated guided vehicle, and the path from the current position of the automated guided vehicle to the end point may be determined by full search planning.

[0074] Of course, the target point can also be a node on the predetermined trajectory that the AGV returns to. Thus, after the path planning method of this embodiment determines the current node that meets the preset conditions, the search path before the current node and the target point can be concatenated. That is, the search path before the current node and the path between the current node and the target point are concatenated, and the search path is then used as the path for the AGV to return to the predetermined trajectory. The predetermined trajectory can be a topological route map composed of connected nodes and their connectivity for subsequent tasks. The predetermined trajectory information can also include node information, such as shelf points and ordinary path points. The connectivity between two adjacent nodes is formed by straight lines or third-order Bezier curves. Thus, by setting the target point as a node on the predetermined trajectory that the AGV returns to, the path planning method of this embodiment can automatically plan a path from the AGV's current position back to the predetermined trajectory when the AGV encounters an obstacle or is manually manipulated onto an off-track path. This allows the AGV to circumvent the obstacle and reach the target state on the trajectory. Furthermore, the combination of topological routing and local planning significantly improves efficiency and reduces the performance overhead associated with local planning.

[0075] Furthermore, based on the topological roadmap composed of nodes and their connectivity, each route segment can be assigned a width value, introducing the concept of lanes. Based on the set width values ​​and node coordinate information, a grayscale channel map is generated and stored in PGM format. This allows the user to determine whether the current node meets the preset conditions by using the width value of each route to determine whether the current node can move directly to the target point without collision. Furthermore, when searching for children of the current node, the width value of each route can be used to determine whether the candidate child node poses a collision risk.

[0076] In addition, considering the terminal position and the load status of the AGV, the movement of the AGV can be refined into multiple scenarios;

[0077] (1) Unloaded rack loading scenario

[0078] Since the size of the shelf is generally slightly larger than the size of the AGV, it is necessary to ensure that the route is straight when entering the shelf. During planning, it is necessary to calculate the intermediate target point of the shelf entrance based on the size and posture of the shelf, and use the intermediate target point of the shelf entrance as the target point. In this way, the planning of this scene can be divided into two sections, the first section is the path from the current position of the automatic guided vehicle to the target point, and the second section is the path from the target point to the center of the shelf. The first section uses the path planning method of this embodiment for search planning, and the second section connects the target point and the center point of the shelf in a straight line to generate a path; the path formed by splicing the search path and the straight line path from the target point to the center point of the shelf is the path of the automatic guided vehicle from the current position of the automatic guided vehicle to the end position.

[0079] (1.2) Unloaded shelf scene

[0080] Considering that the current posture is not necessarily in the center of the shelf, it is necessary to avoid the shelf legs according to the data of the laser sensor. Therefore, the path planning method of this embodiment is directly used for search planning, and the target point is the end position of the automatic guided vehicle.

[0081] (1.3) Load status in and out of the shelf scene

[0082] Considering the densely packed racks in the racking area, the planned route must ensure a straight path in and out of the loaded racks without colliding with adjacent racks. Therefore, in this scenario, a straight path is required for the first leg of the route out of a rack and the last leg of the route in. The remaining routes are searched and planned using the path planning method of this embodiment, with the target points set based on the actual situation.

[0083] (1.4) Normal operation area scenario: Search planning is used, and the target point is the end point of the automatic guided vehicle.

[0084] In this embodiment, during path planning, after confirming the current node, the child nodes of the current node can be searched so as to determine the next node of the current node based on the costs of all child nodes of the current node, and then complete the path planning based on the next node; and when the automatic guided vehicle is in a loaded state, the cost of each child node includes the shelf adjustment cost in the process of the automatic guided vehicle moving from the current node to each child node. In this way, the shelf adjustment cost is taken into account during path planning, which can prevent frequent adjustments of the shelves in the planned path, so as to ensure the smoothness and smooth operation of the path as much as possible.

[0085] In addition, specifically Figure 2As shown, another embodiment of the path planning method includes the following steps. It should be noted that the following step numbers are only used to simplify the description and are not intended to limit the execution order of the steps. The steps of this embodiment can be changed in any order without violating the technical concept of this application.

[0086] S201: Put the starting node into a priority list and an unsearched node list.

[0087] Before step S201, a priority queue may be defined. Nodes in the priority queue are sorted by their cost, with the first value in the priority queue being the node with the lowest cost. A list of unsearched nodes and a list of searched nodes may also be defined.

[0088] S202: If the unsearched node list is not empty, set the first value of the priority list to the current node, put it into the search node list, and delete it from the priority list.

[0089] The purpose of adding the current node to the search node list is to indicate that the current node has been searched, so as to avoid using the current node as a searched child node again in the future, reduce the number of nodes for cost calculation, and improve path planning efficiency.

[0090] In addition, if the unsearched node list is empty, it means that the search has failed and the path has not been successfully planned, and the path planning method of this embodiment is completed. In the case of unsuccessful path planning, a failure alarm can be returned to allow the administrator to handle the fault as soon as possible.

[0091] S203: Confirm whether it is possible to move from the current node to the target point by rotating in place and / or moving in a straight line.

[0092] If it is confirmed that the current node can be moved to the target point by rotating in place and / or moving in a straight line, that is, it is confirmed that the current node meets the preset conditions, and the process goes to step S205 to splice and form a path from the starting node to the target point; if it is confirmed that the current node cannot be moved to the target point by rotating in place and / or moving in a straight line, that is, it is confirmed that the current node does not meet the preset conditions, and the process goes to step S204 to search for the child nodes of the current node, and then find the next node of the current node until the path planning is completed.

[0093] S204: Search for child nodes of the current node, and put the child nodes into a priority list and an unsearched node list.

[0094] Candidate child nodes of the current node may be generated first, and then each candidate child node may be tested to determine the child nodes of the current node, and then the child nodes may be placed in a priority list and an unsearched node list, and then the process returns to step S202.

[0095] Specifically, if the candidate child node is already in the list of searched nodes, it means that the candidate child node has been searched, and the candidate child node is screened out; if there is a collision risk for the candidate child node, that is, in the process of the automatic guided vehicle from the current node to the candidate child node, the outline of the automatic guided vehicle (shelf and vehicle body) will collide with an obstacle, then there is a collision risk for the candidate child node, and the candidate child node is screened out; the remaining candidate child nodes are used as child nodes of the current node.

[0096] S205: Connect the search path between the current node and the target point, and use the search path as the path for the automatic guided vehicle from the starting node to the target point.

[0097] When the current node meets the preset conditions, the search path before the current node and the target point is spliced, and the search path is used as the path for the automatic guided vehicle from the starting node to the target point. That is, the path planning method of this embodiment is successfully used to plan the path, so that the searched path node sequence can be output.

[0098] Furthermore, the process of the path planning method of the present application can be as follows: Figure 3 First, the data processing and management layer verifies and processes the input information such as the prior channel map (such as topological route map and / or grayscale channel map, etc.), obstacle avoidance sensor data, other equipment contour information, reference path information and current vehicle body status, and then the planning decision layer uses Figure 1 or Figure 2 The hybrid A* search path planning method shown plans and outputs a path from the start node to the end point.

[0099] See also Figure 4 , Figure 4 2 is a schematic diagram of the structure of an embodiment of the electronic device 20 of the present application. The electronic device 20 of the present application includes a processor 22, which is used to execute instructions to implement the method of any of the above embodiments of the present application and any non-conflicting combination thereof.

[0100] The processor 22 may also be referred to as a CPU (Central Processing Unit). The processor 22 may be an integrated circuit chip having signal processing capabilities. The processor 22 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor, or the processor 22 may be any conventional processor.

[0101] The electronic device 20 may further include a memory 21 for storing instructions and data required for the processor 22 to operate.

[0102] See also Figure 5 , Figure 5 Schematic diagram of the structure of the computer-readable storage medium in the embodiment of the present application. The computer-readable storage medium 30 of the embodiment of the present application stores instruction / program data 31, which, when executed, implements the method provided by any embodiment of the above-mentioned method of the present application and any non-conflicting combination. Among them, the instruction / program data 31 can form a program file and be stored in the above-mentioned storage medium 30 in the form of a software product, so that a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) executes all or part of the steps of the various embodiments of the present application. The aforementioned storage medium 30 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a computer, server, mobile phone, tablet and other devices.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0104] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0105] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0106] The above is only an implementation method of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of this application, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A path planning method, characterized in that: The method comprises: Search for the child nodes of the current node; Calculating the cost of each of the child nodes, wherein when the automated guided vehicle is in a loaded state, the cost of each of the child nodes includes a shelf adjustment cost during the process of the automated guided vehicle moving from the current node to each of the child nodes, the shelf adjustment cost including a shelf motion state switching cost and / or a shelf rotation cost, the shelf motion state switching cost refers to the cost of transforming from the shelf following the swing of the automated guided vehicle to maintaining the shelf orientation unchanged in the world coordinate system, and the shelf rotation cost is positively correlated with the rotation angle of the shelf; Determining the next node of the current node based on the cost of the child node; Based on the next node, path planning is completed.

2. The method according to claim 1, characterized in that The search for the child nodes of the current node previously includes: Determine whether it is possible to move from the current node to the target point by rotating in place and / or moving in a straight line; If yes, confirm that the path planning is completed; If not, perform the search for the child nodes of the current node; The completing path planning based on the next node includes: The next node is used as the current node, and the process returns to the step of determining whether the target point can be moved from the current node by rotating in situ and / or moving in a straight line, until the path planning is completed.

3. The method according to claim 2, characterized in that The target point is the node where the automatic guided vehicle returns to the predetermined track line. If so, the path planning is confirmed to be complete, including: If so, the search path between the current node and the target point is spliced ​​together, and the search path is used as the path for the automatic guided vehicle to return to the predetermined track line.

4. The method according to claim 2, characterized in that The determining whether the movement from the current node to the target point can be performed by rotating in place and / or moving in a straight line comprises: When the automatic guided vehicle is in an unloaded state and the end point of the automatic guided vehicle is the center point of the shelf, the midpoint of the shelf entrance is used as the target point; The method further includes: combining a search path from the current position of the automated guided vehicle to the target point and a straight line path from the target point to the center point of the shelf to obtain a path of the automated guided vehicle from the current position of the automated guided vehicle to the end point.

5. The method according to claim 1, wherein The determining the next node of the current node based on the cost of the child node includes: The child node with the smallest cost among all the child nodes of the current node is used as the next node.

6. The method according to claim 1, characterized in that The searching for child nodes of the current node includes: Search all candidate child nodes of the current node; Nodes that have been searched and nodes with collision risks are eliminated from all candidate child nodes to obtain a child node set of the current node.

7. The method according to claim 6, characterized in that The method further comprises: If, based on the obstacle data and the body state of the automatic guided vehicle, it is determined that the automatic guided vehicle and the rack loaded thereon may collide with an obstacle during the process of moving from the current node to the candidate child node, then the candidate child node is a node with a collision risk.

8. An electronic device, characterized in that: The electronic device comprises a processor, and the processor is configured to execute instructions to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions / program data, and the instructions / program data are configured to be executed to implement the method according to any one of claims 1 to 7.

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