Path planning method and device
By generating multiple first paths and adjusting the target node locations in the e-commerce business based on the shelf position information of the order goods, and generating a second path, thereby determining the target paths in multiple paths, the problem of low efficiency of sorter path planning is solved, and more efficient and accurate path planning is achieved.
Patent Information
- Application Number
- CN202510244044.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-28
- Publication Date
- 2025-06-20
AI Technical Summary
In e-commerce business, when sorters need to plan the path to obtain orders from multiple shelf locations, many orders are prone to multiple turnovers and detours, which seriously affects efficiency.
By determining M first paths based on the location information of the shelf where the goods are located in the order, and adjusting the target node position for each first path, at least one second path is generated, thereby determining the target path among the M first paths and N second paths.
This method effectively improves the efficiency and accuracy of path planning, prevents paths from falling into local optimization, and reduces the retracement and detours of sorters.
Smart Images
Figure CN120176668A_ABST
Abstract
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] With the continuous development of computer equipment, e-commerce is also gradually developing. In e-commerce business, people place orders online or offline, and the sorters pick up the ordered goods from different shelves according to the order information. Since these shelves are distributed in various locations of the forward warehouse, the sorters need to be familiar with the shelf locations of the goods and plan a reasonable path sequence. However, when there are many goods in the order, even experienced sorters can hardly avoid multiple turns and detours, which seriously affects the efficiency of the sorters.
[0003] Therefore, how to plan the path based on the shelf location corresponding to the goods in the order is a technical problem that needs to be solved urgently. Summary of the invention
[0004] The embodiments of the present invention provide a path planning method and device for determining a target path based on M first paths and N second paths corresponding to the M first paths, which can effectively improve the efficiency and accuracy of path planning.
[0005] In a first aspect, an embodiment of the present invention provides a path planning method, the method comprising: determining M first paths according to location information of multiple shelves where goods in an order are located, each node in the M first paths corresponding to one shelf among the multiple shelves; the M first paths include M1 first paths and M2 first paths obtained according to the M1 first paths; the first nodes in the M1 first paths are the same, and the first node of at least one path in the M2 first paths is different from the first node in the M1 first path; for each first path in the M first paths, adjusting the position of the target node in the first path to obtain at least one second path corresponding to the first path; the position of the target node in the first path satisfies a first preset condition, and the position of the target node in the second path satisfies a second preset condition; determining the target path from the M first paths and the N second paths corresponding to the M first paths.
[0006] Using the above method, according to the location information of multiple shelves where the goods in the order are located, M first paths are determined. The M first paths include M1 first paths and M2 first paths obtained from the M1 first paths. For each of the M first paths, derivation is performed, and the target nodes in each first path are adjusted to generate at least one second path, so that the paths have diversity, prevent falling into local optimality, and there is no need to traverse all paths. The target path determined among the M first paths and N second paths can effectively improve the efficiency and accuracy of path planning.
[0007] In an alternative embodiment, the position of the target node in the first path satisfies a first preset condition, including at least one of the following:
[0008] The distance between the target node and the adjacent node in the first path is greater than the distance between any other two nodes in the first path;
[0009] The sum of the distance between the target node and the previous node and the distance between the target node and the next node minus the distance between the previous node and the next node of the target node is greater than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node minus the distance between the previous node and the next node of the middle node among any three nodes in the first path;
[0010] The sum of the distance between the target node and the previous node and the distance between the target node and the next node is greater than the sum of the distance between any node and the previous node and the distance between the any node and the next node in the first path.
[0011] In an alternative embodiment, the position of the target node in the second path satisfies a second preset condition, including at least one of the following:
[0012] The distance between the target node and the adjacent node in the second path is less than the distance between any other two nodes in the second path;
[0013] The sum of the distance between the target node and the previous node and the distance between the target node and the next node minus the distance between the previous node and the next node of the target node is less than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node minus the distance between the previous node and the next node of the middle node among any three nodes in the second path;
[0014] The sum of the distance between the target node and the previous node and the distance between the target node and the next node is less than the sum of the distance between any node in the second path and the previous node and the distance between the any node and the next node.
[0015] In an alternative embodiment, according to the location information of multiple shelves where the goods in the order are located, determining M first paths includes: randomly selecting a shelf from the multiple shelves as the starting node of the M1 first paths; according to the distances between the remaining shelves in the multiple shelves and the starting node, selecting multiple shelves from the remaining shelves as the next nodes of the starting node respectively, and so on, to obtain the M1 first paths.
[0016] By using the above method, multiple first paths can be generated, ensuring the diversity of the first paths.
[0017] In an alternative embodiment, according to the location information of multiple shelves where the goods in the order are located, determining M first paths further includes: for each of the M1 first paths, segmenting the first path into multiple sub-paths according to the distances between adjacent nodes in the first path, and then reordering the multiple sub-paths to obtain the M2 first paths.
[0018] By using the above method, since the starting nodes of the M1 first paths are the same, after splitting and reordering the M1 paths, it can be ensured that at least one starting node of the M2 first paths is different from the starting nodes of the M1 first paths, avoiding the final target path falling into a local optimum due to incorrect selection of the starting node.
[0019] In an alternative embodiment, determining a target path from the M first paths and the N second paths corresponding to the M first paths includes: determining the target path from the M first paths and the N second paths corresponding to the M first paths according to at least one of the distance, load-bearing condition, and distance between the end node and a preset position of each path in the M first paths and the N second paths.
[0020] In a second aspect, an embodiment of the present invention provides a path planning device, and the device includes:
[0021] A determination module, configured to determine M first paths according to the location information of multiple shelves where the goods in the order are located, each node in the M first paths corresponding to one of the multiple shelves; the M first paths include M1 first paths and M2 first paths obtained according to the M1 first paths; the first nodes in the M1 first paths are the same, and the first node of at least one path in the M2 first paths is different from the first nodes in the M1 first paths;
[0022] An adjustment module, configured to, for each of the M first paths, adjust the position of a target node in the first path to obtain at least one second path corresponding to the first path; the position of the target node in the first path satisfies a first preset condition, and the position of the target node in the second path satisfies a second preset condition;
[0023] A processing module, configured to determine a target path from the M first paths and the N second paths corresponding to the M first paths.
[0024] The position of the target node in the first path satisfying the first preset condition includes at least one of the following:
[0025] The distance between the target node and an adjacent node in the first path is greater than the distance between any other two nodes in the first path;
[0026] The sum of the distance between the target node and the previous node and the distance between the target node and the next node minus the distance between the previous node and the next node of the target node is greater than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node minus the distance between the previous node and the next node of the middle node among any three nodes in the first path;
[0027] The sum of the distance between the target node and the previous node and the distance between the target node and the next node is greater than the sum of the distance between any node and the previous node and the distance between the any node and the next node in the first path.
[0028] In an optional implementation manner, the position of the target node in the second path satisfying the second preset condition includes at least one of the following:
[0029] The distance between the target node and an adjacent node in the second path is less than the distance between any other two nodes in the second path;
[0030] The sum of the distance between the target node and the previous node and the distance between the target node and the next node, minus the distance between the previous node and the next node of the target node, is less than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node among any three nodes in the second path, minus the distance between the previous node of the middle node and the next node of the middle node;
[0031] The sum of the distance between the target node and the previous node and the distance between the target node and the next node is less than the sum of the distance between any node in the second path and the previous node and the distance between the any node and the next node.
[0032] In an alternative embodiment, the determining module is specifically configured to randomly select a shelf from the multiple shelves as the head node of the M1 first paths; according to the distances between the remaining shelves in the multiple shelves and the head node, select multiple shelves from the remaining shelves as the next nodes of the head node respectively, and so on, to obtain the M1 first paths.
[0033] In an alternative embodiment, the determining module is further configured to, for each of the M1 first paths, segment the first path into multiple sub-paths according to the distances between adjacent nodes in the first path, and then reorder the multiple sub-paths to obtain the M2 first paths.
[0034] In an alternative embodiment, the processing module is specifically configured to determine a target path from the M first paths and the N second paths corresponding to the M first paths according to at least one of the distance, load-bearing condition, and distance between the end node and a preset position of each path in the M first paths and the N second paths.
[0035] In a third aspect, the present application provides a path planning device, including:
[0036] A memory for storing program instructions;
[0037] A processor for calling the program instructions stored in the memory and executing the steps included in the method according to any one of the first aspects according to the obtained program instructions.
[0038] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is caused to execute the method according to any one of the first aspects.
[0039] In a fifth aspect, the present application provides a computer program product, comprising: a computer program code, when the computer program code is run on a computer, the computer executes any one of the methods described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.
[0041] Figure 1 A flowchart corresponding to the path planning method provided in an embodiment of the present invention;
[0042] Figure 2 A flowchart for determining M1 first paths provided by an embodiment of the present invention;
[0043] Figure 3 A schematic diagram of a path planning device provided by an embodiment of the present invention;
[0044] Figure 4 A schematic diagram of a path planning device provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0045] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention. In the embodiments of the present invention, multiple refers to two or more. The words "first", "second", etc. are only used to distinguish the purpose of description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.
[0046] As mentioned above, since the shelves corresponding to the goods in the order are distributed in various locations in the forward warehouse, the pickers need to be familiar with the shelf locations where the goods are located and plan a reasonable path sequence. However, when there are many goods in the order, even experienced pickers can hardly avoid multiple turns and detours, which seriously affects the efficiency of the pickers.
[0047] Currently, for the path planning problem of sorters, the greedy strategy is usually adopted to determine the target path. First, a shelf position corresponding to a randomly selected commodity is used as the starting node. Each time, a shelf position corresponding to a commodity that is closest to the commodity corresponding to the previous node is selected as the next node, and so on to generate the target path. However, since the starting node is randomly selected in the target path determined by this method, it is easy to fall into a local optimum.
[0048] Based on this, an embodiment of the present invention provides a path planning method. This method determines M first paths according to the position information of multiple shelves where the commodities in the order are located, and for each of the M first paths, adjusts the target nodes in each first path to generate at least one second path, so that the paths have diversity and prevent falling into a local optimum. At this time, the target path determined among the M first paths and the N second paths effectively improves the efficiency and accuracy of path planning.
[0049] The following describes in detail the method provided in the embodiments of the present application with reference to specific embodiments.
[0050] Figure 1 It is a flowchart corresponding to the path planning method provided in the embodiments of the present invention. This method can be executed by a path planning device. As Figure 1 shown, this method includes the following steps:
[0051] Step 101, the path planning device determines M first paths according to the position information of multiple shelves where the commodities in the order are located.
[0052] Specifically, the path planning device obtains a user order, which includes multiple commodities. For each commodity in the order, it obtains the shelf position information where each commodity is located. The path planning device determines M first paths according to the position information of multiple shelves where the commodities in the order are located. Each node in each of the M first paths corresponds to one of the multiple shelves. The M first paths include M1 first paths and M2 first paths obtained from the M1 first paths.
[0053] Optionally, Figure 2 It is a flowchart for determining M1 first paths provided in the embodiments of the present application. As Figure 2 shown, the steps for determining M1 first paths include:
[0054] Step 201, the path planning device randomly selects a shelf from multiple shelves as the starting node of the M1 first paths.
[0055] Step 202, the path planning device selects multiple shelves from the remaining shelves as the next nodes of the starting node according to the distances between the remaining shelves in the multiple shelves and the starting node.
[0056] For example, assume that the currently obtained order includes 7 items, and the corresponding shelves for these 7 items are A, B, C, D, E, F, and G respectively. Randomly select one shelf from these 7 shelves as the starting node. Assume the starting node is D. At this time, calculate the distances between the remaining 6 nodes and the starting node D, and sort them from near to far. The distance sorting is C, E, A, B, F, G. Multiple shelves can be selected as the next node of the starting node. The C node, which is the first nearest to the starting node D, or the E node, which is the second nearest, can be selected as the next node of the starting node D. At this time, the two nodes that derive two first paths are D→C or D→E. At this time, to determine the next node of the node path D→C and the next node of the path D→E, that is, to select the node that is the first nearest or the second nearest to the C shelf from the unselected shelves as the next node of C. At this time, the derived paths are D→C→A and D→C→B; to select the node that is the first nearest or the second nearest to the E shelf from the unselected shelves as the next node of E. At this time, the derived paths are D→E→F and D→E→C; and so on, M1 first paths can be derived, and the starting nodes of the M1 paths are the same. It should be noted that in the above example, selecting the first nearest node or the second nearest node as the next node is only an example, and multiple nodes can also be selected, which is not limited here.
[0057] Step 203, and so on, the path planning device obtains M1 first paths.
[0058] It should be noted that the starting nodes of the above-obtained M1 first paths are all the same. Since each node in the M1 first paths is determined based on the distance from the previous node, that is to say, the determination of the next node is related to the previous node.
[0059] Optionally, since the items in the front warehouse are distributed in multiple areas, including refrigerated, frozen, and normal temperature areas, the starting nodes of the M1 first paths generated by the above steps 201-203 are all the same, and the determined M1 paths all start from a certain shelf in the same area of the front warehouse, without considering the sorting effect starting from different areas. Therefore, the M1 first paths can be processed to obtain M2 first paths.
[0060] Specifically, for each of the M1 first paths, according to the distances between adjacent nodes in the first path, the first path is segmented into multiple sub-paths, and then the multiple sub-paths are re-sorted to obtain M2 first paths. For example, assume that one of the M1 first paths is D→E→F→A→C→B→G. Calculate the distances between adjacent nodes in this path, that is, calculate the distances between D and E, E and F, F and A, A and C, C and B, and B and G respectively. Assume that the distance between E and F is the largest and the distance between C and B is the second largest. At this time, it can be cut between E and F and between C and B, and the first path is cut into 3 segments to obtain 3 sub-paths. The 3 sub-paths are D→E, F→A→C, and B→G respectively. Re-sort the 3 sub-paths to obtain M2 first paths, such as F→A→C→B→G→D→E, etc. At this time, the starting node of at least one of the M2 first paths is different from the starting node of the M1 first paths. In this way, an initial solution with a greater direction change can be obtained.
[0061] Optionally, before segmenting the first path into multiple sub-paths and sorting them, the node order of each sub-path can be adjusted again using the greedy algorithm.
[0062] Step 102, the path planning device adjusts the positions of the target nodes in each of the M first paths to obtain at least one second path corresponding to each first path.
[0063] Specifically, for each of the M first paths, the path planning device determines the position of the target node in the first path according to the first preset condition, deletes it from the first path, and determines the position where the target node is added according to the second preset condition, that is, the position of the target node in the second path. For example, assume that the first path is D→E→F→A→C→B→G and the target node is node E. According to the second preset condition, it is determined that the position where the target node is added is between C and B. At this time, one of the second paths corresponding to this first path is D→F→A→C→E→B→G. Each first path corresponds to at least one second path, and the M first paths correspond to N second paths, where N is an integer greater than or equal to M.
[0064] Among them, the position of the target node in the first path satisfies the first preset condition, including at least one of the following:
[0065] The distance between the target node and the adjacent node in the first path is greater than the distance between any other two nodes in the first path. For example, if the first path is D→E→F→A→C→B→G, assuming that the distance between node E and node F is greater than the distance between any two nodes in the first path, then node E is the target node in the first path.
[0066] The sum of the distance between the target node and the previous node and the distance between the target node and the next node, minus the distance between the previous node and the next node of the target node, is greater than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node of the middle node among any three nodes in the first path, minus the distance between the previous node and the next node of the middle node. For example, if the first path is D→E→F→A→C→B→G, assuming that node E is the target node, then the sum of the distance between node E and node D and the distance between node E and node F, minus the distance between node D and node F, is greater than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node of the middle node among any three nodes in the first path, minus the distance between the previous node and the next node of the middle node.
[0067] The sum of the distance between the target node and the previous node and the distance between the target node and the next node is greater than the sum of the distance between any node and the previous node and the distance between any node and the next node in the first path. For example, if the first path is D→E→F→A→C→B→G, assuming that the sum of the distance between node E and node D and the distance between node E and node F is greater than the sum of the distance between any node and the previous node and the distance between any node and the next node in the first path, then node E is the target node.
[0068] The position of the target node in the second path satisfies the second preset condition, including at least one of the following:
[0069] The distance between the target node and the adjacent node in the second path is less than the distance between any other two nodes in the second path. For example, if the second path is D→F→A→C→E→B→G, assuming that the distance between node E and node B is less than the distance between any two nodes in the second path, then node E is the target node in the second path.
[0070] The sum of the distance between the target node and the previous node and the distance between the target node and the next node, minus the distance between the previous node and the next node of the target node, is less than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node among any three nodes in the second path, minus the distance between the previous node of the middle node and the next node of the middle node. For example, the second path is D→F→A→C→E→B→G. Assuming that node E is the target node, at this time, the sum of the distance between node E and node C and the distance between node E and node B, minus the distance between node C and node B, is greater than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node among any three nodes in the first path, minus the distance between the previous node of the middle node and the next node of the middle node.
[0071] The sum of the distance between the target node and the previous node and the distance between the target node and the next node is less than the sum of the distance between any node and the previous node and the distance between any node and the next node in the second path. For example, the second path is D→F→A→C→E→B→G. Assuming that the sum of the distance between node E and node C and the distance between node E and node B is greater than the sum of the distance between any node and the previous node and the distance between any node and the next node in the first path, at this time, node E is the target node.
[0072] Step 103, the path planning device determines a target path from M first paths and N second paths corresponding to the M first paths.
[0073] Specifically, the path planning device determines a target path from M first paths and N second paths corresponding to the M first paths according to at least one of the distance, load-bearing condition, and the distance between the end node and the preset position of each path in the M first paths and the N second paths. For example, the distances of the M first paths and the N second paths corresponding to the M first paths can be calculated, and the path with the shortest distance can be selected as the target path; or, the weight of each commodity can be multiplied by the path distance corresponding to each commodity, and the path with the shortest load-bearing distance can be selected as the target path among the M first paths and the N second paths corresponding to the M first paths; or, the distances between the end nodes of the M first paths and the N second paths corresponding to the M first paths and the sorting table can be calculated, and the path with the farthest distance can be selected as the target path. When the path planning device determines a target path from M first paths and N second paths according to multiple items of the distance, load-bearing condition, and the distance between the end node and the preset position of each path in the M first paths and the N second paths. The corresponding weights can be set according to the priorities of the distance, load-bearing condition, and the distance between the end node and the preset position of each path to determine the target path.
[0074] Using the above method, according to the location information of the goods in multiple shelves in the order, M first paths are determined. The M first paths include M1 first paths and M2 first paths obtained from the M1 first paths. For each of the M first paths, derivation is performed, the target nodes in each first path are adjusted, and at least one second path is generated, so that the paths have diversity, preventing falling into local optimality. Without traversing all paths, the target path determined among the M first paths and N second paths can effectively improve the efficiency and accuracy of path planning.
[0075] Based on the same technical concept, an embodiment of the present invention further provides a path planning device 3000. Figure 3 A schematic diagram of the path planning device provided for the implementation of the present invention is shown in Figure 3 As shown, the device includes:
[0076] A determination module 301, configured to determine M first paths according to the location information of the goods in multiple shelves in the order. Each node in the M first paths corresponds to one of the multiple shelves; the M first paths include M1 first paths and M2 first paths obtained from the M1 first paths; the first nodes in the M1 first paths are the same, and the first node of at least one path in the M2 first paths is different from the first nodes in the M1 first paths;
[0077] An adjustment module 302, configured to, for each of the M first paths, adjust the position of the target node in the first path to obtain at least one second path corresponding to the first path; the position of the target node in the first path satisfies a first preset condition, and the position of the target node in the second path satisfies a second preset condition;
[0078] A processing module 303, configured to determine a target path from the M first paths and the N second paths corresponding to the M first paths.
[0079] The position of the target node in the first path satisfying the first preset condition includes at least one of the following:
[0080] The distance between the target node and an adjacent node in the first path is greater than the distance between any other two nodes in the first path;
[0081] The sum of the distance between the target node and the previous node and the distance between the target node and the next node, minus the distance between the previous node and the next node of the target node, is greater than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node among any three nodes in the first path, minus the distance between the previous node and the next node of the middle node;
[0082] The sum of the distance between the target node and the previous node and the distance between the target node and the next node is greater than the sum of the distance between any node in the first path and the previous node and the distance between the any node and the next node.
[0083] In an alternative embodiment, the position of the target node in the second path satisfies a second preset condition, including at least one of the following:
[0084] The distance between the target node and the adjacent node in the second path is less than the distance between any other two nodes in the second path;
[0085] The sum of the distance between the target node and the previous node and the distance between the target node and the next node, minus the distance between the previous node and the next node of the target node, is less than the sum of the distance between the middle node and the previous node and the distance between the middle node and the next node among any three nodes in the second path, minus the distance between the previous node and the next node of the middle node;
[0086] The sum of the distance between the target node and the previous node and the distance between the target node and the next node is less than the sum of the distance between any node in the second path and the previous node and the distance between the any node and the next node.
[0087] In an alternative embodiment, the determining module 301 is specifically configured to randomly select a shelf from the multiple shelves as the head node of the M1 first paths; according to the distances between the remaining shelves in the multiple shelves and the head node, select multiple shelves from the remaining shelves as the next nodes of the head node respectively, and so on, to obtain the M1 first paths.
[0088] In an alternative embodiment, the determining module 301 is further configured to, for each of the M1 first paths, segment the first path into multiple sub-paths according to the distances between adjacent nodes in the first path, and then reorder the multiple sub-paths to obtain the M2 first paths.
[0089] In an alternative embodiment, the processing module 303 is specifically configured to determine a target path from the M first paths and the N second paths corresponding to the M first paths according to at least one of the distances, load-bearing conditions, and distances between the end nodes and a preset position of each path among the M first paths and the N second paths.
[0090] Based on the same concept, an embodiment of the present application further provides a schematic structural diagram of a path planning device, as Figure 4 shown. The device 4000 includes at least one processor 401 and a memory 402 connected to the at least one processor 401. In the embodiments of the present application, the specific connection medium between the processor 401 and the memory 402 is not limited. Figure 4 Taking the example that the processor 401 and the memory 402 are connected through a bus. The bus can be divided into an address bus, a data bus, a control bus, etc. In the embodiments of the present application, the memory 402 stores instructions executable by the at least one processor 401, and the at least one processor 401 can implement the steps of the above path planning method by executing the instructions stored in the memory 402.
[0091] Among them, the processor 401 is the control center of the computer device, and can connect various parts of the computer device through various interfaces and lines. By running or executing the instructions stored in the memory 402 and calling the data stored in the memory 402, resource settings can be performed. Optionally, the processor 401 may include one or more processing units. The processor 401 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above modem processor may not be integrated into the processor 401. In some embodiments, the processor 401 and the memory 402 can be implemented on the same chip, and in some embodiments, they can also be implemented on separate chips respectively.
[0092] The processor 401 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application specific integrated circuit (ASIC), a field programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.
[0093] The memory 402, being a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The memory 402 can include at least one type of storage medium. For example, it can include flash memory, hard disks, multimedia cards, card-type memories, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memories, magnetic disks, optical disks, and so on. The memory 402 is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 402 in the embodiments of the present application can also be a circuit or any other device capable of implementing a storage function, for storing program instructions and / or data.
[0094] Based on the same inventive concept, the embodiments of the present application provide a computer-readable storage medium. The computer program product includes: computer program code, which, when running on a computer, causes the computer to execute any of the path planning methods discussed above. Since the principle of the above computer-readable storage medium for solving problems is similar to that of the path planning method, the implementation of the above computer-readable storage medium can refer to the implementation of the method, and the repeated parts will not be elaborated.
[0095] Based on the same inventive concept, the embodiments of the present application further provide a computer program product. The computer program product includes: computer program code, which, when running on a computer, causes the computer to execute any of the path planning methods discussed above. Since the principle of the above computer program product for solving problems is similar to that of the path planning method, the implementation of the above computer program product can refer to the implementation of the method, and the repeated parts will not be elaborated.
[0096] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.
[0097] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.
[0098] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.
[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.
[0100] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.
Claims
1. A path planning method, characterized in that: The method comprises: According to the location information of multiple shelves where the goods in the order are located, M first paths are determined, each node in the M first paths corresponds to one shelf in the multiple shelves; the M first paths include M1 first paths and M2 first paths obtained according to the M1 first paths; the first nodes in the M1 first paths are the same, and the first node of at least one path in the M2 first paths is different from the first node in the M1 first path; For each first path of the M first paths, adjusting the position of the target node in the first path to obtain at least one second path corresponding to the first path; the position of the target node in the first path satisfies a first preset condition, and the position of the target node in the second path satisfies a second preset condition; A target path is determined from the M first paths and the N second paths corresponding to the M first paths, wherein M, N, M1 and M2 are all integers greater than or equal to 1.
2. The method according to claim 1, characterized in that The position of the target node in the first path satisfies a first preset condition, including at least one of the following: The distance between the target node and an adjacent node in the first path is greater than the distance between any other two nodes in the first path; The sum of the distance between the target node and the previous node and the distance between the target node and the next node minus the distance between the previous node and the next node of the target node is greater than the sum of the distance between the intermediate node and the previous node and the distance between the intermediate node and the next node among any three nodes in the first path minus the distance between the previous node of the intermediate node and the next node of the intermediate node; The sum of the distance between the target node and the previous node and the distance between the target node and the next node is greater than the sum of the distance between any node and the previous node and the distance between any node and the next node in the first path.
3. The method according to claim 1, characterized in that The position of the target node in the second path satisfies a second preset condition, including at least one of the following: The distance between the target node and an adjacent node in the second path is smaller than the distance between any other two nodes in the second path; The sum of the distance between the target node and the previous node and the distance between the target node and the next node minus the distance between the previous node and the next node of the target node is less than the sum of the distance between the intermediate node and the previous node and the distance between the intermediate node and the next node among any three nodes in the second path minus the distance between the previous node of the intermediate node and the next node of the intermediate node; The sum of the distance between the target node and the previous node and the distance between the target node and the next node is less than the sum of the distance between any node and the previous node and the distance between any node and the next node in the second path.
4. The method according to claim 1, characterized in that Determining M first paths according to location information of multiple shelves where commodities in the order are located includes: Randomly select a shelf from the multiple shelves as the first node of the M1 first paths; According to the distance between the remaining shelves in the plurality of shelves and the first node, multiple shelves are selected from the remaining shelves as the next nodes of the first node respectively, and so on, to obtain the M1 first paths.
5. The method according to claim 4, characterized in that Determining M first paths according to location information of multiple shelves where commodities in the order are located further includes: For each of the M1 first paths, the first path is segmented into multiple sub-paths according to distances between adjacent nodes in the first path, and the multiple sub-paths are then reordered to obtain the M2 first paths.
6. The method according to any one of claims 1 to 5, characterized in that Determining a target path from the M first paths and the N second paths corresponding to the M first paths includes: According to at least one of the distance, load condition and distance between the end node and the preset position of each path in the M first paths and the N second paths, a target path is determined from the M first paths and the N second paths corresponding to the M first paths.
7. A path planning device, characterized in that: The device comprises: A determination module, configured to determine M first paths according to location information of multiple shelves where commodities in the order are located, wherein each node in the M first paths corresponds to one shelf among the multiple shelves; the M first paths include M1 first paths and M2 first paths obtained according to the M1 first paths; the first nodes in the M1 first paths are the same, and the first node of at least one path in the M2 first paths is different from the first node in the M1 first path; an adjustment module, configured to adjust, for each of the M first paths, a position of a target node in the first path to obtain at least one second path corresponding to the first path; the position of the target node in the first path satisfies a first preset condition, and the position of the target node in the second path satisfies a second preset condition; The processing module is used to determine a target path from the M first paths and the N second paths corresponding to the M first paths.
8. A path planning device, characterized in that: The device comprises: A memory for storing program instructions; A processor is used to call the program instructions stored in the memory, and execute the steps included in any one of claims 1-6 according to the obtained program instructions.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the method according to any one of claims 1 to 6 is executed.
10. A computer program product, characterized in that The computer program product comprises a computer program code, which causes any one of claims 1 to 6 to be performed when the computer program code is run on a computer.