Goods picking robot control method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a control method and device of a picking robot, electronic equipment and a storage medium. The method comprises the following steps: under the condition that a picking robot receives a plurality of picking tasks, determining at least two candidate passing paths for the picking robot to move among a plurality of goods shelves of a warehouse according to the plurality of picking tasks; determining the path passing efficiency corresponding to the candidate passing path according to the passing influence factor corresponding to each candidate passing path; and determining a picking passage path from the plurality of candidate passage paths according to the path passage efficiency, and controlling the picking robot to move along the picking passage path so as to convey the to-be-picked articles. According to the technical scheme provided by the embodiment of the invention, the effect of determining the optimal order picking path on the basis of comprehensively considering the order picking task and the passage influence factors is realized, and further, the effect of ensuring high-efficiency order picking under the condition of adopting the optimal order picking path is achieved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to computer application technology, and in particular to a control method, device, electronic device, and storage medium for a picking robot. Background Art

[0002] With the advancement of warehouse management technology, some warehouses now send picking orders to delivery robots, which then roam the warehouse picking items according to the picking orders. When receiving a large number of picking orders or when there are a limited number of delivery robots, a delivery robot may be assigned multiple picking orders, and different picking orders may correspond to different picking locations. Therefore, planning efficient picking routes for delivery robots becomes crucial.

[0003] In related technologies, when a delivery robot processes multiple picking orders, one way is to determine the picking path according to the receipt time of each picking order. This method may make the delivery robot's walking path longer, resulting in lower picking efficiency; another way is to plan the shortest picking path based on multiple picking orders. However, due to the complex and changeable warehouse environment, it is difficult to achieve stable and efficient picking by only using distance to determine the optimal path. Summary of the Invention

[0004] The present disclosure provides a control method, device, electronic device and storage medium for a picking robot, so as to achieve the effect of determining the optimal picking path based on comprehensive consideration of picking tasks and traffic influencing factors, thereby achieving the effect of ensuring high-efficiency picking when adopting the optimal picking path.

[0005] In a first aspect, an embodiment of the present disclosure provides a control method for a picking robot, the method comprising:

[0006] When the picking robot receives multiple picking tasks, determining at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse based on the multiple picking tasks;

[0007] Determining the path traffic efficiency corresponding to each candidate traffic path according to the traffic influencing factors corresponding to each candidate traffic path, wherein the traffic influencing factors include at least one of the road layout information of the warehouse, road traffic information, and a collaboration influencing factor between the warehouse and the picker;

[0008] A picking path is determined from the plurality of candidate paths according to the path efficiency, and the picking robot is controlled to move along the picking path to transport items to be picked.

[0009] In a second aspect, an embodiment of the present disclosure further provides a control device for a picking robot, the device comprising:

[0010] a candidate path determination module, configured to determine, when a picking robot receives multiple picking tasks, at least two candidate paths for the picking robot to move between multiple shelves in a warehouse based on the multiple picking tasks;

[0011] a traffic efficiency determination module, configured to determine a path traffic efficiency corresponding to each candidate traffic path based on a traffic influencing factor corresponding to each candidate traffic path, wherein the traffic influencing factor includes at least one of road layout information of the warehouse, road traffic information, and a collaboration influencing factor with a picker;

[0012] The robot control module is used to determine a picking path from the multiple candidate paths according to the path efficiency, and control the picking robot to move along the picking path to transport the items to be picked.

[0013] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0014] one or more processors;

[0015] a storage device for storing one or more programs,

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the control method of the picking robot as described in any of the embodiments of the present disclosure.

[0017] In a fourth aspect, an embodiment of the present disclosure further provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute the control method of the picking robot as described in any one of the embodiments of the present disclosure.

[0018] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the control method of the picking robot as described in any one of the embodiments of the present disclosure.

[0019] The technical solution of the disclosed embodiment, when a picking robot receives multiple picking tasks, determines at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse according to the multiple picking tasks, thereby achieving the effect of preliminarily screening out candidate passage paths in combination with multiple picking orders. Furthermore, the path traffic efficiency corresponding to the candidate passage path is determined according to the traffic influencing factors corresponding to each candidate passage path, thereby achieving the effect of determining the path traffic efficiency in combination with the traffic influencing factors, and providing data support for the subsequent determination of the optimal picking passage path. Afterwards, a picking passage path is determined from the multiple candidate passage paths according to the path traffic efficiency, and the picking robot is controlled to move along the picking passage path to transport the items to be picked. This solves the problems of low picking efficiency in related technologies, or the difficulty in stably achieving high-efficiency picking by only using distance to determine the optimal path, and achieves the effect of determining the optimal picking path based on comprehensive consideration of picking tasks and traffic influencing factors, thereby achieving the effect of ensuring high-efficiency picking when the optimal picking path is adopted. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0021] Figure 1 A flowchart of a control method for a picking robot provided by an embodiment of the present disclosure;

[0022] Figure 2 A flowchart of another control method for a picking robot provided by an embodiment of the present disclosure;

[0023] Figure 3 A schematic structural diagram of a control device for a picking robot provided by an embodiment of the present disclosure;

[0024] Figure 4 A schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0026] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0027] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0028] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0029] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0030] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0031] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0032] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operations of the disclosed technical solution based on the prompt message.

[0033] As an optional but non-limiting implementation, in response to receiving a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0034] It is understandable that the above notification and user authorization process are merely illustrative and do not limit the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0035] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) must comply with the requirements of relevant laws, regulations and relevant provisions.

[0036] Before introducing the present technical solution, an example of the application scenario can be first described. The technical solution can be applied to any scenario where picking tasks need to be performed in a target area. In order to facilitate the description of the technical solution of the embodiment of the present disclosure, the technical solution of the embodiment of the present disclosure can be described by taking a warehousing scenario as an example. For example, in a warehousing scenario, a picking order can be sent to a delivery robot, and the delivery robot walks in the warehouse to pick according to the picking order. When a warehouse receives a large number of picking orders or there are limited delivery robots, a delivery robot may be required to process multiple picking orders that may be located at different picking locations. In the related art, when a delivery robot processes multiple picking orders, one way is to determine the picking path according to the receipt time of each picking order. This method may make the walking path of the delivery robot longer, resulting in lower picking efficiency; another way is to plan the shortest picking path based on multiple picking orders. However, due to the complex and changeable warehouse environment, it is difficult to stably achieve high-efficiency picking by only using distance to determine the optimal path.

[0037] At this time, based on the technical solution of the embodiment of the present disclosure, when the delivery robot receives multiple picking tasks, first, at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse are determined based on the multiple picking tasks. Afterwards, for each candidate passage path, the traffic influencing factors corresponding to the candidate passage path are determined, and the path traffic efficiency corresponding to the candidate passage path is determined based on the traffic influencing factors. Furthermore, a picking passage path can be determined from multiple candidate passage paths based on the path traffic efficiency corresponding to each candidate passage path. Then, the delivery robot can be controlled to move along the picking passage path to deliver items to be inspected. Thus, the effect of determining the optimal picking path based on comprehensive consideration of the picking tasks and traffic influencing factors is achieved, and then, the effect of ensuring high-efficiency picking is achieved when the optimal picking path is adopted.

[0038] Figure 1This is a flow chart of a control method for a picking robot provided in an embodiment of the present disclosure. The embodiment of the present disclosure is applicable to situations where an optimal picking path is determined and the picking robot is controlled to perform a picking task according to the optimal picking path. The method can be executed by a control device of the picking robot, which can be implemented in the form of software and / or hardware. Optionally, it can be implemented by an electronic device, which can be a mobile terminal, a PC or a server, etc.

[0039] like Figure 1 As shown, the method of this embodiment may specifically include:

[0040] S110 : When the picking robot receives multiple picking tasks, determine at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse according to the multiple picking tasks.

[0041] The picking robot includes at least one item storage container for carrying items to be picked corresponding to a picking task. The picking robot moves between multiple shelves in the warehouse to transport items. The shelves include multiple cargo storage compartments for storing different items. A picking task may be a task of picking corresponding items from the shelves and transporting them to a target location. Generally, when the picking robot receives a picking task, it can determine the items to be picked corresponding to the picking task, the storage location of the items on the shelves, and the target location to which the items are to be transported. Furthermore, a picking path can be determined based on the storage location, the current location of the picking robot, and the target location. The picking robot can then be controlled to move along the determined picking path to transport the items to be picked to the target location, thereby completing the picking task. In the disclosed embodiments, to improve picking efficiency, the picking robot can receive multiple picking tasks and, upon receiving multiple picking tasks, can transport the items to be inspected corresponding to the multiple picking tasks at once. A candidate path can be understood as a path that enables a picking robot to move between multiple shelves in a warehouse to transport items for inspection. A candidate path can be a path among multiple initial paths that meets preset picking criteria. Optionally, the preset picking criteria can include high picking efficiency or short picking time.

[0042] In the disclosed embodiment, when a picking robot receives multiple picking tasks, it can determine multiple picking paths based on the shelf locations of the items to be inspected in the multiple picking tasks and the shelf layout information in the warehouse. Furthermore, to improve picking efficiency, at least two paths can be determined from the multiple picking paths based on preset screening criteria, and the at least two determined paths can be used as candidate paths. Optionally, the preset screening criteria can include path length screening criteria, etc.

[0043] Optionally, at least two candidate passage paths are determined for the picking robot to move between multiple shelves in the warehouse based on multiple picking tasks, including: determining multiple target picking points corresponding to the multiple picking tasks, and determining multiple initial passage paths based on the multiple target picking points and road layout information in the warehouse; and determining at least two candidate passage paths from the multiple initial passage paths in order of the length of the initial passage paths from short to long.

[0044] Among them, the target picking point can be the storage point of the items to be inspected on the shelf, that is, the location of the cargo storage compartment where the items to be inspected are stored; or it can also be a temporary storage point for the items to be inspected, that is, the location where the items to be inspected are temporarily stored after being taken out of the shelf to wait for the picking robot. Road layout information can be used to characterize the layout of the traffic roads in the warehouse. It can be understood that in addition to including multiple shelves, the warehouse also includes roads that can lead to each shelf, and in order to improve the storage utilization rate of the warehouse and the execution efficiency of the picking task, the shelves and roads in the warehouse can be laid out, and the road information obtained after the layout is used as the road layout information. The initial traffic path can be a traffic path that includes multiple target picking points and conforms to the road layout information. In the embodiment of the present disclosure, when the picking robot receives multiple picking tasks, for each picking task, the target picking point corresponding to the picking task can be determined based on the items to be inspected corresponding to the picking task. Furthermore, multiple target picking points corresponding to multiple picking tasks can be determined. Furthermore, the road layout information in the warehouse can be obtained, and multiple passages that can cover the multiple target picking points can be determined based on the multiple target picking points and the road layout information. The determined passages can be used as initial passages.

[0045] The length of the initial pass path can be the sum of the lengths of the road sections traversed from the path start point to the path end point. The length of the initial pass path can be determined in a variety of ways, optionally by determining the Manhattan distance, that is, determining the Manhattan distance between the path start point and the path end point. Those skilled in the art will understand that the Manhattan distance, also known as the city block distance or L1 distance, is the sum of the distances between two points in the north-south direction and the east-west direction. In the embodiment of the present disclosure, a grid map can be constructed based on the road layout information and shelf layout information in the warehouse, and the picking points can be used as grids in the grid map. Furthermore, when multiple target picking points are determined, the point coordinates of each target picking point in the grid map can be determined separately. Afterwards, after obtaining multiple initial pass paths, for each initial pass path, the Manhattan distance of the initial pass path can be determined based on the point coordinates corresponding to each target picking point, and the obtained Manhattan distance can be used as the length of the initial pass path.

[0046] As an optional implementation of the embodiment of the present disclosure, when the picking robot receives multiple picking tasks, target picking points corresponding to the multiple picking tasks can be determined based on the multiple picking tasks. Furthermore, multiple initial traffic paths can be determined based on the target picking points and the road layout information of the warehouse. Afterwards, the length of each initial traffic path can be determined, and the multiple initial traffic paths can be sorted in order of length from short to long. Furthermore, at least two candidate traffic paths can be determined from the sorted multiple initial traffic paths. The advantage of this setting is that it realizes the effect of planning multiple initial paths in combination with multiple picking orders, and determining the candidate traffic paths based on the length of the initial paths, and further, realizes the effect of preliminarily screening the traffic paths based on the path length, ensuring that the path length of the finally determined picking path is within a shorter range.

[0047] In the embodiment of the present disclosure, there are multiple ways to determine at least two candidate passing paths from the sorted multiple initial passing paths. Each determination method will be described below.

[0048] The first determination method is: starting from the initial pass path ranked first, a preset number of initial pass paths are selected backward, and the selected initial pass paths are used as candidate pass paths.

[0049] Exemplarily, assuming that the preset number is two, after obtaining the sorted multiple initial pass paths, the initial pass paths ranked at the top two can be used as candidate pass paths.

[0050] The second determination method: for each sorted initial pass path, determine the length difference between the initial pass path and the initial pass path ranked first, and obtain multiple length differences; and use the initial pass path with a length difference not greater than a first preset value as a candidate pass path.

[0051] For example, assuming that the initial pass paths may include 5, the sorted initial pass paths are path 1 (28 meters), path 2 (25 meters), path 3 (30 meters), path 4 (31 meters) and path 5 (45 meters). Furthermore, taking the length corresponding to the shortest path 2 as the benchmark length, the length difference between path 1 and the benchmark length is 3 meters, the length difference between path 2 and the benchmark length is 0 meters, the length difference between path 3 and the benchmark length is 5 meters, the length difference between path 4 and the benchmark length is 6 meters, and the length difference corresponding to path 5 is 20 meters. Assuming that the first preset value is 5 meters, each length difference is compared with the first preset value, and it can be determined that the initial pass paths whose length difference is not greater than the first preset value may include path 1, path 2 and path 3. Furthermore, path 1, path 2 and path 3 can be used as candidate pass paths.

[0052] A third determination method is to determine an initial pass path having a length no greater than a second preset value from a plurality of initial pass paths, and use the determined initial pass path as a candidate pass path.

[0053] For example, assuming the second preset value is 35 meters, and continuing with the above example, the five initial pass paths whose lengths are no greater than the second preset value may include path 1, path 2, path 3, and path 4. Furthermore, path 1, path 2, path 3, and path 4 may be considered candidate pass paths.

[0054] S120: Determine the path traffic efficiency corresponding to each candidate traffic path according to the traffic influencing factors corresponding to each candidate traffic path.

[0055] Among them, traffic influencing factors include at least one of warehouse road layout information, road traffic information, and factors affecting collaboration with pickers. Traffic influencing factors can be understood as factors that affect path traffic efficiency or path traffic time. Traffic influencing factors can include positive and negative factors. Positive factors can improve path traffic efficiency or reduce path traffic time. Negative factors can reduce path traffic efficiency or increase path traffic time. Road traffic information can be understood as information that can characterize road traffic conditions. Road traffic information can be used to indicate the object types and / or total number of road-accessible objects allowed on the corresponding road, as well as road-accessible objects not allowed. Road traffic information can include at least the object types and / or total number of road-accessible objects included in the candidate traffic paths within a preset time period. Road-accessible objects can include picking robots and / or item transport vehicles, such as forklifts. Collaboration factors with pickers can be used to characterize the coordination and cooperation between the picker and the picking robot. Generally speaking, when using a picking robot to perform a picking task, the support and cooperation of a picker may be required to complete the picking task. For example, when the picking robot moves to the target picking point corresponding to the picking task, the picking robot itself may not be able to store the items to be inspected in the corresponding item storage container. In this case, it can wait for the picker to store the items to be inspected in the picking robot based on the picker. The collaboration influencing factors may at least include collaboration distance information, which is the shortest distance between the picker closest to the candidate passage path in the warehouse and the candidate passage path.

[0056] In the disclosed embodiment, for each candidate passage path, the initial passage time corresponding to the candidate passage path can be determined based on the length of the candidate passage path and the average passage speed of the picking robot. Furthermore, the passage influencing factors corresponding to the candidate passage path can be determined based on the road layout information, road passage information, and picker positioning information in the warehouse. Thereafter, the path passage efficiency corresponding to the candidate passage path can be determined based on the passage influencing factors and the initial passage time corresponding to the candidate passage path. Path passage efficiency can be understood as the ratio between the task volume of multiple picking tasks and the time taken to complete the multiple picking tasks.

[0057] It should be noted that a candidate passable path may be composed of multiple passable sections, and the traffic influencing factors corresponding to each passable section may be the same or different. Therefore, when determining the path impact efficiency corresponding to each candidate passable path, for each candidate passable path, the candidate passable path is divided into at least two candidate passable sections, and the traffic influencing factors corresponding to each candidate passable section are determined separately. Furthermore, for each candidate passable section, the section traffic efficiency corresponding to the candidate passable section can be determined based on the traffic influencing factors corresponding to the candidate passable section. Furthermore, the path traffic efficiency corresponding to the candidate passable path is determined based on the section traffic efficiencies corresponding to the multiple candidate passable sections.

[0058] It should also be noted that, for each candidate passable road section, the traffic influencing factors corresponding to the candidate passable road section may include one or more of the warehouse's road layout information, road traffic information, and collaboration influencing factors with pickers.

[0059] Optionally, when the traffic influencing factors corresponding to the candidate passable road section include one traffic influencing factor, the road section traffic efficiency corresponding to the candidate passable road section may be determined according to the traffic influencing factor.

[0060] Optionally, when the traffic influencing factors corresponding to the candidate passable road section include multiple traffic influencing factors, the influence parameters corresponding to each traffic influencing factor can be determined separately. Furthermore, the traffic efficiency of the road section corresponding to the candidate passable road section can be determined based on each traffic influencing factor and the corresponding influence parameter.

[0061] S130: Determine a picking path from multiple candidate paths based on the path efficiency, and control the picking robot to move along the picking path to transport the items to be picked.

[0062] The picking path is the movement path of the picking robot when executing the received multiple picking tasks.

[0063] In the disclosed embodiment, after determining the path efficiency corresponding to each candidate path, the highest efficiency value can be determined from the multiple path efficiencies, and the candidate path corresponding to the highest efficiency value can be used as the picking path. Furthermore, the picking robot can be controlled to move along the picking path, move to the target picking point corresponding to the picking task, and place the items to be inspected stored at the target picking point into the corresponding item storage container of the picking robot. This completes the process of transporting the items to be inspected.

[0064] It should be noted that when controlling a picking robot to move along a picking path, there may be obstacles in the path that hinder the robot's movement. In this case, in related technologies, even if an obstacle is detected, the picking robot may continue to move along the picking path, which may cause the robot to become stuck.

[0065] Based on this, in an embodiment of the present disclosure, after controlling the picking robot to move along the picking path, it also includes: when there are obstacles in the picking path that hinder the movement of the picking robot, controlling the picking robot according to a preset obstacle avoidance strategy.

[0066] Among them, the preset obstacle avoidance strategy includes waiting for the obstacle to be removed before passing, or updating the picking path and moving along the updated picking path.

[0067] It should be noted that when the preset obstacle avoidance strategy is to wait for the obstacle to move before passing, it can be indicated that the obstacles in the picking path are obstacles that can be removed in a shorter time; when the preset obstacle avoidance strategy is to update the picking path and move along the updated picking path, it can be indicated that the obstacles in the picking path are obstacles that cannot be removed in a shorter time.

[0068] As an optional implementation of the embodiment of the present disclosure, after controlling the picking robot to move along the picking path, if there is an obstacle in the picking path that hinders the movement of the picking robot, the obstacle can be matched with a pre-stored target obstacle type. Furthermore, if the type of the obstacle matches the target obstacle type, the preset obstacle avoidance strategy can be determined to update the picking path and move along the updated picking path, and the picking robot can be controlled according to the preset obstacle avoidance strategy. If the type of the obstacle does not match the target obstacle type, the preset obstacle avoidance strategy can be determined to wait for the obstacle to be removed before passing, and the picking robot can be controlled according to the preset obstacle avoidance strategy. The target obstacle type can be a type of obstacle that cannot be removed in a short period of time. Optionally, the target obstacle type can include no-entry signs and shelves, etc.

[0069] The technical solution of the disclosed embodiment, when a picking robot receives multiple picking tasks, determines at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse according to the multiple picking tasks, thereby achieving the effect of preliminarily screening out candidate passage paths in combination with multiple picking orders. Furthermore, the path traffic efficiency corresponding to the candidate passage path is determined according to the traffic influencing factors corresponding to each candidate passage path, thereby achieving the effect of determining the path traffic efficiency in combination with the traffic influencing factors, and providing data support for the subsequent determination of the optimal picking passage path. Afterwards, a picking passage path is determined from the multiple candidate passage paths according to the path traffic efficiency, and the picking robot is controlled to move along the picking passage path to transport the items to be picked. This solves the problems of low picking efficiency in related technologies, or the difficulty in stably achieving high-efficiency picking by only using distance to determine the optimal path, and achieves the effect of determining the optimal picking path based on comprehensive consideration of picking tasks and traffic influencing factors, thereby achieving the effect of ensuring high-efficiency picking when the optimal picking path is adopted.

[0070] Figure 2 A flow chart of another control method for a picking robot provided by an embodiment of the present disclosure. The technical solution of this embodiment provides a solution for determining the path traffic efficiency corresponding to a candidate traffic path on the basis of the above-mentioned embodiment. Optionally, for each candidate traffic path, the candidate traffic path is divided into at least two candidate traffic sections, and the traffic influencing factors corresponding to each candidate traffic section are determined respectively. Furthermore, for each candidate traffic section, the section traffic efficiency corresponding to the candidate traffic section is determined based on the traffic influencing factors corresponding to the candidate traffic section. Furthermore, the path traffic efficiency corresponding to the candidate traffic path is determined based on the section traffic efficiencies corresponding to multiple candidate traffic sections. For specific implementation methods, please refer to the description of this embodiment. Among them, the technical features that are the same or similar to those in the above-mentioned embodiments are not repeated here.

[0071] like Figure 2 As shown, the method of this embodiment may specifically include:

[0072] S210: When the picking robot receives multiple picking tasks, determine at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse according to the multiple picking tasks.

[0073] S220 . For each candidate passable path, divide the candidate passable path into at least two candidate passable road sections, and determine the passable influencing factors corresponding to each candidate passable road section.

[0074] The candidate passable path may be composed of at least two candidate passable road segments. For each candidate passable path, the candidate path may be divided into at least two candidate passable road segments according to a preset road segment division rule. Optionally, the preset road segment division rule may include division based on path inflection points or target picking locations.

[0075] As an optional implementation in the embodiment of the present disclosure, for each candidate passable path, all path inflection points included in the candidate passable path can be determined, and the section between two adjacent path inflection points can be used as a candidate passable section. In this way, at least two candidate passable sections can be obtained.

[0076] As another optional implementation in the embodiment of the present disclosure, for each candidate passable path, the section between two adjacent target picking points in the candidate passable path can be used as a candidate passable section. Thus, at least two candidate passable sections can be obtained.

[0077] Furthermore, after obtaining at least two candidate passable road sections corresponding to the candidate passable path, the traffic influencing factors corresponding to each candidate passable road section can be determined based on the road layout information, road traffic information, and picker location information in the warehouse. It should be noted that the traffic influencing factors corresponding to each candidate passable road section can include one or more.

[0078] It should be noted that, for the technical solution provided by the embodiment of the present disclosure, after the candidate passage path is divided into at least two candidate passage sections, it is also necessary to determine the passage influencing factors corresponding to each candidate passage section respectively, and the passage influencing factors include at least one of the warehouse's road layout information, road traffic information, and collaboration influencing factors with pickers. In practical applications, for warehouses, the path inflection point can be determined based on the road layout information, and, in some cases, the road traffic information may correspond to a fixed section in the warehouse. Therefore, in order to reduce the amount of calculation of the section traffic efficiency and improve the efficiency of determining the picking passage path, it is preferred that the candidate passage path be divided into at least two candidate passage sections based on the path inflection point.

[0079] S230 . For each candidate passable road section, determine the road section traffic efficiency corresponding to the candidate passable road section according to the traffic influencing factors corresponding to the candidate passable road section.

[0080] It should be noted that for each candidate traversable road segment, before determining the corresponding road segment efficiency, the length of the candidate traversable road segment and the corresponding average moving speed of the picking robot can be determined. Furthermore, the ratio between the road segment length and the average moving speed can be determined and used as the initial road segment travel time corresponding to the candidate traversable road segment.

[0081] The traffic efficiency of a road section may be the ratio between the total amount of picking tasks covered by the road section and the time taken to complete the picking tasks.

[0082] In the disclosed embodiments, for each candidate passable road segment, the corresponding road segment traffic efficiency can be determined based on the traffic influencing factors corresponding to the candidate passable road segment. If the traffic influencing factors corresponding to the candidate passable road segment include different types of traffic influencing factors, the corresponding road segment traffic efficiency is determined in different ways. The following describes the method for determining the road segment traffic efficiency corresponding to each traffic influencing factor.

[0083] Optionally, when the traffic influencing factors include the road layout information of the warehouse, the section traffic efficiency corresponding to each candidate traffic section is determined separately according to the road layout information.

[0084] Among them, the road layout information can be information that characterizes the fixed layout situation pre-set in the warehouse. The road layout information includes multiple passable road sections and the traffic layout parameters of each passable road section. The passable road section can be a passable road section in the warehouse. The traffic layout parameters can be understood as parameters that characterize the traffic and layout situation of the road section. The traffic layout parameters include at least one of road section size information, preset traffic objects, preset traffic speeds, and preset traffic directions. The road section size information may include road section length and / or road section width, etc. The preset traffic objects may be objects that are allowed to pass through the passable road section. Optionally, the preset traffic objects may include picking robots, pickers, and / or item transport vehicles, etc. The preset traffic speed may be the traffic speed range allowed for the passable road section, or it may be the maximum traffic speed allowed for the passable road section. The preset traffic direction may include one-way traffic or two-way traffic.

[0085] In the disclosed embodiment, when the traffic influencing factors include the warehouse's road layout information, the traffic layout parameters included in the road layout information may affect the section traffic efficiency of the candidate traffic section. Furthermore, the section traffic efficiency of the candidate traffic section can be determined based on the traffic layout parameters. This arrangement has the advantage of determining the section traffic efficiency of the traffic section when the traffic influencing factors include the road layout information, thereby utilizing the road layout information as a reference for determining the section traffic efficiency.

[0086] Optionally, the section traffic efficiency corresponding to the candidate passable section is determined based on the road layout information, including: determining the traffic layout parameters corresponding to the candidate passable section based on the road layout information in the warehouse, and determining the efficiency impact parameters corresponding to the traffic layout parameters; determining the section traffic efficiency of the candidate passable section based on the traffic layout parameters and efficiency impact parameters corresponding to the candidate passable section.

[0087] Among them, the efficiency impact parameter can be used to indicate the degree of influence of the traffic layout parameter on the traffic efficiency of the candidate traffic section. That is, the efficiency impact parameter can be understood as the parameter that the traffic layout parameter affects the traffic efficiency, that is, the efficiency impact parameter is applied to the traffic efficiency. In this case, the larger the efficiency impact parameter is, the higher the traffic efficiency is and the shorter the travel time is; the smaller the efficiency impact parameter is, the lower the traffic efficiency is and the longer the travel time is. In the embodiment of the present disclosure, the efficiency impact parameter can also be understood as the parameter that the traffic layout parameter affects the travel time of the candidate traffic section, that is, the efficiency impact parameter is applied to the travel time. In this case, the larger the efficiency impact parameter is, the greater the degree of influence of the traffic layout parameter on the travel time is, the longer the travel time is, and the lower the traffic efficiency is; the smaller the efficiency impact parameter is, the smaller the degree of influence of the traffic layout parameter on the travel time is, the shorter the travel time is, and the higher the traffic efficiency is. For example, taking the efficiency impact parameter as an example of the parameter affecting the travel time of the traffic layout parameters, assuming that the road width in the road dimension information corresponding to candidate passable road segment 1 is 1 meter, and the road width in the road dimension information corresponding to candidate passable road segment 2 is 2 meters, this indicates that the impact of the road dimension information corresponding to candidate passable road segment 1 on the travel time is greater than the impact of the road dimension information corresponding to candidate passable road segment 2 on the travel time. Therefore, it can be determined that the efficiency impact parameter corresponding to candidate passable road segment 1 is greater than or equal to the efficiency impact parameter corresponding to candidate passable road segment 2.

[0088] In the disclosed embodiment, for each traffic layout parameter, the parameter value of the traffic layout parameter corresponding to each traversable road segment in the warehouse can be determined based on the road layout information in the warehouse, and the efficiency impact parameter corresponding to each parameter value can be determined. Furthermore, the efficiency impact parameter can be associated with the traversable road segment and stored. Consequently, the efficiency impact parameter corresponding to the traffic layout parameter on each traversable road segment can be obtained.

[0089] As an optional implementation of the embodiment of the present disclosure, the traffic layout parameters corresponding to the candidate traffic sections can be determined based on the road layout information in the warehouse. Furthermore, in the case that the traffic layout parameters corresponding to the candidate traffic sections include one, the efficiency impact parameter corresponding to the traffic layout parameter can be determined, and based on the efficiency impact parameter and the predetermined initial section travel time, the section travel efficiency corresponding to the candidate traffic section can be determined. Exemplarily, taking the efficiency impact parameter as an example of a parameter that the traffic layout parameter affects the travel time, the product between the efficiency impact parameter and the initial section travel time can be determined, and then the inverse of the product can be used as the section travel efficiency. Alternatively, taking the efficiency impact parameter as an example of a parameter that the traffic layout parameter affects the travel efficiency, the inverse of the initial section travel time can be determined, and the inverse can be used as the initial section travel efficiency. Afterwards, the product between the efficiency impact parameter and the initial section travel efficiency can be determined, and the product can be used as the section travel efficiency.

[0090] If a candidate traversable road segment includes multiple traffic layout parameters, the efficiency impact parameter corresponding to each traffic layout parameter can be determined separately. Subsequently, the product of the multiple efficiency impact parameters can be determined, and the traffic efficiency of the road segment corresponding to the candidate traversable road segment can be determined based on this product and the predetermined initial road segment traffic efficiency. This arrangement has the advantage of determining the efficiency impact parameter based on the traffic layout parameters of the traversable road segment, achieving the goal of numerically representing traffic impact factors, thereby improving the accuracy of determining the road segment traffic efficiency.

[0091] Optionally, when the traffic influencing factors include the collaboration influencing factors with the pickers, the traffic efficiency of the candidate traffic sections is determined according to the collaboration influencing factors.

[0092] The collaboration influencing factors include at least collaboration distance information, where the collaboration distance information is the shortest distance that a picker closest to a candidate passage section in the warehouse moves to the candidate passage section.

[0093] In the embodiment of the present disclosure, when the traffic influencing factors include the collaboration influencing factors with the pickers, the collaboration distance information included in the collaboration influencing factors may affect the section traffic efficiency of the candidate traffic section. Furthermore, the section traffic efficiency corresponding to the candidate traffic section can be determined based on the collaboration distance information. The advantage of this arrangement is that it achieves the effect of determining the section traffic efficiency of the traffic section when the traffic influencing factors include the collaboration influencing factors with the pickers, and achieves the effect of using the collaboration influencing factors with the pickers as a reference for determining the section traffic efficiency.

[0094] Optionally, determining the section traffic efficiency of the candidate passable section based on the collaborative influence factors includes: determining a collaborative influence parameter corresponding to the collaborative distance information, and determining the section traffic efficiency of the candidate passable section based on the collaborative influence parameter.

[0095] Among them, the collaboration influence parameter is used to indicate the degree of influence of the picker on the picking efficiency. The assistance influence parameter can be understood as the parameter that the collaboration distance information affects the picking efficiency, that is, the assistance influence parameter is applied to the picking efficiency. In this case, the larger the collaboration influence parameter is, the higher the picking efficiency is and the shorter the picking time is; the smaller the assistance influence parameter is, the lower the picking efficiency is and the longer the picking time is. In the embodiment of the present disclosure, the assistance influence parameter can also be understood as the parameter that the collaboration distance information affects the picking time, that is, the collaboration influence parameter is applied to the picking time. In this case, the larger the collaboration influence parameter is, the longer the picking time is and the lower the picking efficiency is; the smaller the collaboration influence parameter is, the shorter the picking time is and the higher the picking efficiency is.

[0096] For example, taking the collaborative influence parameter as the parameter that affects the picking time of the collaborative distance information, assuming that the collaborative distance information corresponding to the candidate pass section 1 is 10 meters, and the collaborative distance information corresponding to the candidate pass section 2 is 5 meters, then the picking time corresponding to the candidate pass section 1 is greater than the picking time corresponding to the candidate pass section 2. It can be determined that the collaborative influence parameter corresponding to the candidate pass section 1 is greater than or equal to the collaborative influence parameter corresponding to the candidate pass section 2.

[0097] In the embodiment of the present disclosure, multiple collaborative distance information can be pre-set, and the collaborative impact parameter corresponding to each collaborative distance information can be determined according to the length of the collaborative distance information. Furthermore, the collaborative distance information and the corresponding collaborative impact parameter can be associated and stored.

[0098] As an optional implementation in the embodiment of the present disclosure, when determining the collaborative distance information corresponding to the candidate passable section, the collaborative influence parameter corresponding to the collaborative distance information can be determined based on the predetermined collaborative influence association relationship. Furthermore, the section traffic efficiency corresponding to the candidate passable section can be determined based on the collaborative influence parameter and the predetermined initial section traffic time. Exemplarily, the product of the collaborative influence parameter and the initial section traffic time can be determined, and the inverse of the product is used as the section traffic efficiency. The advantage of such a setting is that it realizes the effect of determining the collaborative influence parameter based on the collaborative distance information, achieves the effect of expressing the traffic influence factors by numerical values, and further improves the accuracy of determining the section traffic efficiency.

[0099] Optionally, when the traffic influencing factors include road traffic information of the candidate traffic path, the section traffic information corresponding to the candidate traffic section is determined, and the section traffic efficiency corresponding to the candidate traffic section is determined based on the section traffic information.

[0100] Among them, the road traffic information can be information that characterizes the road traffic conditions in the warehouse, that is, information that characterizes the real-time traffic conditions of the roads in the warehouse. The section traffic information is information that characterizes the real-time traffic conditions of the candidate traffic sections. The section traffic information at least includes the object type and / or the total number of road traffic objects included in the candidate traffic sections within a preset time period. The road traffic objects include picking robots and / or item transport tools. For example, the item transport tool can be a forklift. The total number of objects can be the number of all road traffic objects included, that is, the total number of picking robots and / or item transport tools included. The preset time period can be any time period between the receipt of the picking task and the expected completion of the picking task.

[0101] In the embodiment of the present disclosure, when the traffic influencing factors include road traffic information of the candidate passable path, the road traffic information in the warehouse can be obtained, and the section traffic information corresponding to the candidate passable section can be determined based on the road traffic information. Furthermore, the section traffic information can be analyzed to determine the road traffic conditions of the candidate passable section within a preset time period, and to determine the degree of influence of the road traffic conditions on the traffic efficiency. Furthermore, the section traffic efficiency corresponding to the candidate passable section can be determined. The advantage of this arrangement is that it achieves the effect of determining the section traffic efficiency of the passable section when the traffic influencing factors include the road traffic information of the candidate passable path, and achieves the effect of using the road traffic information as a reference for determining the section traffic efficiency.

[0102] In an embodiment of the present disclosure, if the road traffic information corresponding to a candidate passable road segment indicates that the only road-passing objects included in the candidate passable road segment during a preset time period are picking robots, the road traffic efficiency corresponding to the candidate passable road segment is relatively high. If the road traffic information corresponding to a candidate passable road segment indicates that the only road-passing objects included in the candidate passable road segment during a preset time period are picking machines and object transport vehicles, the road traffic efficiency corresponding to the candidate passable road segment is relatively low. The greater the total number of road-passing objects included in the candidate passable road segment during the preset time period in the road traffic information corresponding to the candidate passable road segment, the lower the corresponding road traffic efficiency.

[0103] As an optional implementation method in the embodiment of the present disclosure, a traffic impact parameter corresponding to the section traffic information is determined, and the section traffic efficiency of the candidate passable section is determined based on the traffic impact parameter. The traffic impact parameter characterizes the degree of influence of the section traffic information corresponding to the candidate passable section on the traffic efficiency. The traffic impact parameter can be understood as a parameter that the section traffic information affects the traffic efficiency, that is, the traffic impact parameter is applied to the traffic efficiency. In this case, the larger the traffic impact parameter is, the higher the traffic efficiency is and the shorter the travel time is; the smaller the traffic impact parameter is, the lower the traffic efficiency is and the longer the travel time is. In the embodiment of the present disclosure, the traffic impact parameter can also be understood as a parameter that the section traffic information affects the travel time of the candidate passable section, that is, the traffic impact parameter is applied to the travel time. In this case, the larger the traffic impact parameter, the greater the impact of the traffic information of the road section on the travel time, the longer the travel time, and the lower the traffic efficiency; the smaller the traffic impact parameter, the smaller the impact of the traffic information of the road section on the travel time, the shorter the travel time, and the higher the traffic efficiency.

[0104] For example, let's take the traffic impact parameter as a parameter that represents the effect of road section traffic information on travel time. Assume that the road section traffic information corresponding to candidate road section 1 includes road traffic objects of type picking robots and forklifts, with a total number of 5 objects; and the road section traffic information corresponding to candidate road section 2 includes road traffic objects of type picking robots, with a total number of 1 object. Furthermore, it can be determined that the impact of the road section traffic information corresponding to candidate road section 1 on travel time is greater than the impact of the road section traffic information corresponding to candidate road section 2 on travel efficiency, and thus the traffic impact parameter corresponding to candidate road section 1 is greater than the traffic impact parameter corresponding to candidate road section 2.

[0105] S240: Determine a path traffic efficiency corresponding to a candidate passable path based on the segment traffic efficiencies corresponding to the plurality of candidate passable road segments.

[0106] In the embodiment of the present disclosure, after obtaining the section traffic efficiency corresponding to each candidate passable section, the path traffic efficiency corresponding to the candidate passable path can be determined based on the multiple section traffic efficiencies.

[0107] As an optional implementation in the embodiment of the present disclosure, the traffic efficiencies of multiple road sections can be added together to determine the sum of the traffic efficiencies of the road sections. The sum of the determined efficiencies can then be used as the traffic efficiency of the path corresponding to the candidate traffic path.

[0108] S250: Determine a picking path from multiple candidate paths based on the path efficiency, and control the picking robot to move along the picking path to transport the items to be picked.

[0109] The technical solution of the embodiment of the present disclosure is as follows: when a picking robot receives multiple picking tasks, at least two candidate passage paths for the picking robot to move between multiple shelves in a warehouse are determined based on the multiple picking tasks. Furthermore, for each candidate passage path, the candidate passage path is divided into at least two candidate passage sections, and the passage influencing factors corresponding to each candidate passage section are determined respectively. Then, for each candidate passage section, the section passage efficiency corresponding to the candidate passage section is determined based on the passage influencing factors corresponding to the candidate passage section. Then, the path passage efficiency corresponding to the candidate passage path is determined based on the section passage efficiencies corresponding to the multiple candidate passage sections. Finally, a picking passage path is determined from the multiple candidate passage paths based on the path passage efficiency, and the picking robot is controlled to move along the picking passage path to transport items to be picked. This achieves the effect of dividing the path into multiple sections, determining the section passage efficiency, and determining the path passage efficiency based on the section passage efficiency, thereby improving the accuracy of determining the path passage efficiency, and thus achieving the effect of ensuring high-efficiency picking when the optimal passage path is adopted.

[0110] Figure 3 This is a schematic diagram of the structure of a control device for a picking robot provided by an embodiment of the present disclosure, such as Figure 3 As shown, the device includes: a candidate path determination module 310, a traffic efficiency determination module 320 and a robot control module 330.

[0111] Among them, the candidate path determination module 310 is used to determine at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse based on the multiple picking tasks when the picking robot receives multiple picking tasks; the passage efficiency determination module 320 is used to determine the path passage efficiency corresponding to the candidate passage path according to the passage influencing factors corresponding to each of the candidate passage paths, wherein the passage influencing factors include the road layout information of the warehouse, the road traffic information and at least one of the collaboration influencing factors with the pickers; the robot control module 330 is used to determine the picking passage path from the multiple candidate passage paths based on the path passage efficiency, and control the picking robot to move along the picking passage path to transport the items to be picked.

[0112] On the basis of the above-mentioned optional technical solutions, optionally, the traffic efficiency determination module 320 includes: an influencing factor determination submodule, a road section traffic efficiency determination submodule and a path traffic efficiency determination submodule.

[0113] an influencing factor determination submodule, configured to divide each candidate pass path into at least two candidate pass sections, and determine the pass influencing factor corresponding to each candidate pass section;

[0114] a road section traffic efficiency determination submodule, configured to determine, for each candidate passable road section, the road section traffic efficiency corresponding to the candidate passable road section according to the traffic influencing factors corresponding to the candidate passable road section;

[0115] The path traffic efficiency determination submodule is configured to determine the path traffic efficiency corresponding to the candidate pass path according to the segment traffic efficiencies corresponding to the plurality of candidate pass segments.

[0116] On the basis of the above-mentioned optional technical solutions, optionally, the road section traffic efficiency determination submodule includes: a first road section traffic efficiency determination unit.

[0117] The first determination unit of the road section traffic efficiency is used to determine the road section traffic efficiency corresponding to the candidate passable road section according to the road layout information when the traffic influencing factors include the road layout information of the warehouse, wherein the road layout information includes multiple passable road sections and the traffic layout parameters of each of the passable road sections, and the traffic layout parameters include at least one of road section size information, preset traffic objects, preset traffic speeds and preset traffic directions.

[0118] On the basis of the above-mentioned optional technical solutions, optionally, the first determining unit for the road section traffic efficiency includes: an influencing parameter determining subunit and a traffic efficiency determining subunit.

[0119] an influencing parameter determination subunit, configured to determine, based on the road layout information in the warehouse, traffic layout parameters corresponding to the candidate traffic section, and determine an efficiency influencing parameter corresponding to the traffic layout parameter, wherein the efficiency influencing parameter is used to indicate the degree of influence of the traffic layout parameter on the traffic efficiency of the candidate traffic section;

[0120] The traffic efficiency determination subunit is used to determine the section traffic efficiency of the candidate traffic section according to the traffic layout parameters and the efficiency influencing parameters corresponding to the candidate traffic section.

[0121] On the basis of the above-mentioned optional technical solutions, optionally, the road section traffic efficiency determination submodule includes: a second road section traffic efficiency determination unit.

[0122] The second determination unit of the road section traffic efficiency is used to determine the road section traffic efficiency of the candidate traffic section according to the collaborative influencing factors when the traffic influencing factors include the collaborative influencing factors with the pickers, wherein the collaborative influencing factors at least include collaborative distance information, and the collaborative distance information is the shortest distance from the picker closest to the candidate traffic section in the warehouse to the candidate traffic section.

[0123] On the basis of the above-mentioned optional technical solutions, optionally, the second determination unit of the section traffic efficiency is specifically used to determine the collaborative influence parameter corresponding to the collaborative distance information, and determine the section traffic efficiency of the candidate passage section according to the collaborative influence parameter, wherein the collaborative influence parameter is used to indicate the degree of influence of the picker on the picking efficiency.

[0124] On the basis of the above-mentioned optional technical solutions, optionally, the road section traffic efficiency determination submodule includes: a third road section traffic efficiency determination unit.

[0125] The third determination unit of the road section traffic efficiency is used to determine the road section traffic information corresponding to the candidate pass section when the traffic influencing factors include the road traffic information of the candidate pass path, and determine the road section traffic efficiency corresponding to the candidate pass section based on the road section traffic information, wherein the road section traffic information at least includes the object type and / or the total number of road traffic objects included in the candidate pass section within a preset time period, and the road traffic objects include picking robots and / or item transport tools.

[0126] On the basis of the above optional technical solutions, optionally, the candidate path determination module 310 includes: an initial path determination unit and a candidate path determination unit.

[0127] an initial path determination unit, configured to determine a plurality of target picking locations corresponding to the plurality of picking tasks, and a plurality of initial travel paths based on the plurality of target picking locations and road layout information in the warehouse;

[0128] The candidate path determining unit is configured to determine at least two candidate passing paths from the plurality of initial passing paths in descending order of length of the initial passing paths.

[0129] On the basis of the above-mentioned optional technical solutions, optionally, after controlling the picking robot to move along the picking path, the device further includes: a robot obstacle avoidance control module.

[0130] A robot obstacle avoidance control module is used to control the picking robot according to a preset obstacle avoidance strategy after controlling the picking robot to move along the picking path, if there is an obstacle in the picking path that hinders the movement of the picking robot, wherein the preset obstacle avoidance strategy includes waiting for the obstacle to be removed before passing, or updating the picking path and moving along the updated picking path.

[0131] The technical solution of the disclosed embodiment, when a picking robot receives multiple picking tasks, determines at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse according to the multiple picking tasks, thereby achieving the effect of preliminarily screening out candidate passage paths in combination with multiple picking orders. Furthermore, the path traffic efficiency corresponding to the candidate passage path is determined according to the traffic influencing factors corresponding to each candidate passage path, thereby achieving the effect of determining the path traffic efficiency in combination with the traffic influencing factors, and providing data support for the subsequent determination of the optimal picking passage path. Afterwards, a picking passage path is determined from the multiple candidate passage paths according to the path traffic efficiency, and the picking robot is controlled to move along the picking passage path to transport the items to be picked. This solves the problems of low picking efficiency in related technologies, or the difficulty in stably achieving high-efficiency picking by only using distance to determine the optimal path, and achieves the effect of determining the optimal picking path based on comprehensive consideration of picking tasks and traffic influencing factors, thereby achieving the effect of ensuring high-efficiency picking when the optimal picking path is adopted.

[0132] The control device of the picking robot provided in the embodiments of the present disclosure can execute the control method of the picking robot provided in any embodiment of the present disclosure, and has functional modules and beneficial effects corresponding to the execution method.

[0133] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present disclosure.

[0134] Figure 4 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 4 , which shows an electronic device (eg Figure 4 The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0135] like Figure 4As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the electronic device 500 are also stored in the RAM 503. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An edit / output (I / O) interface 505 is also connected to the bus 504.

[0136] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0137] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0138] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0139] The electronic device provided in the embodiment of the present disclosure and the control method of the picking robot provided in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0140] An embodiment of the present disclosure provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the control method of the picking robot provided in the above embodiment.

[0141] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0142] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0143] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0144] The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: when the picking robot receives multiple picking tasks, determines at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse based on the multiple picking tasks; determines the path traffic efficiency corresponding to the candidate passage path according to the traffic influencing factors corresponding to each of the candidate passage paths, wherein the traffic influencing factors include the road layout information of the warehouse, the road traffic information and at least one of the cooperation influencing factors with the pickers; determines a picking passage path from the multiple candidate passage paths based on the path traffic efficiency, and controls the picking robot to move along the picking passage path to transport the items to be picked.

[0145] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0147] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0148] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0149] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0150] According to one or more embodiments of the present disclosure, [Example 1] provides a control method for a picking robot, including:

[0151] When the picking robot receives multiple picking tasks, determining at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse based on the multiple picking tasks;

[0152] Determining the path traffic efficiency corresponding to each candidate traffic path according to the traffic influencing factors corresponding to each candidate traffic path, wherein the traffic influencing factors include at least one of the road layout information of the warehouse, road traffic information, and a collaboration influencing factor between the warehouse and the picker;

[0153] A picking path is determined from the plurality of candidate paths according to the path efficiency, and the picking robot is controlled to move along the picking path to transport items to be picked.

[0154] According to one or more embodiments of the present disclosure, [Example 2] provides the method of Example 1, further comprising:

[0155] Optionally, the path traffic efficiency corresponding to the candidate pass path is determined according to the traffic influencing factors corresponding to each candidate pass path, including: for each candidate pass path, dividing the candidate pass path into at least two candidate pass sections, and determining the traffic influencing factors corresponding to each candidate pass section; for each candidate pass section, determining the section traffic efficiency corresponding to the candidate pass section according to the traffic influencing factors corresponding to the candidate pass section; and determining the path traffic efficiency corresponding to the candidate pass path according to the section traffic efficiencies corresponding to multiple candidate pass sections.

[0156] According to one or more embodiments of the present disclosure, [Example 3] provides the method of Example 2, further comprising:

[0157] Optionally, the traffic efficiency of the section corresponding to the candidate passable section is determined based on the traffic influencing factors corresponding to the candidate passable section, including: when the traffic influencing factors include the road layout information of the warehouse, the traffic efficiency of the section corresponding to the candidate passable section is determined based on the road layout information, wherein the road layout information includes multiple passable sections and traffic layout parameters of each of the passable sections, and the traffic layout parameters include at least one of section size information, preset traffic objects, preset traffic speeds, and preset traffic directions.

[0158] According to one or more embodiments of the present disclosure, [Example 4] provides the method of Example 3, further comprising:

[0159] Optionally, the section traffic efficiency corresponding to each candidate passable section is determined separately according to the road layout information, including: determining the traffic layout parameters corresponding to the candidate passable section according to the road layout information in the warehouse, and determining the efficiency impact parameters corresponding to the traffic layout parameters, wherein the efficiency impact parameters are used to indicate the degree of influence of the traffic layout parameters on the traffic efficiency of the candidate passable section; determining the section traffic efficiency of the candidate passable section according to the traffic layout parameters and the efficiency impact parameters corresponding to the candidate passable section.

[0160] According to one or more embodiments of the present disclosure, [Example 5] provides the method of Example 2, further comprising:

[0161] Optionally, the section traffic efficiency corresponding to each candidate pass section is determined based on the traffic influencing factors corresponding to the candidate pass section, including: when the traffic influencing factors include collaboration influencing factors with pickers, the section traffic efficiency of the candidate pass section is determined based on the collaboration influencing factors, wherein the collaboration influencing factors at least include collaboration distance information, and the collaboration distance information is the shortest distance from the picker closest to the candidate pass section in the warehouse to the candidate pass section.

[0162] According to one or more embodiments of the present disclosure, [Example 6] provides the method of Example 5, further comprising:

[0163] Optionally, determining the section traffic efficiency of the candidate pass section based on the collaborative influencing factors includes: determining a collaborative influence parameter corresponding to the collaborative distance information, and determining the section traffic efficiency of the candidate pass section based on the collaborative influence parameter, wherein the collaborative influence parameter is used to indicate the degree of influence of the picker on the picking efficiency.

[0164] According to one or more embodiments of the present disclosure, [Example 7] provides the method of Example 2, further comprising:

[0165] Optionally, the section traffic efficiency corresponding to the candidate passable section is determined based on the traffic influencing factors corresponding to the candidate passable section, including: when the traffic influencing factors include the road traffic information of the candidate passable path, determining the section traffic information corresponding to the candidate passable section, and determining the section traffic efficiency corresponding to the candidate passable section based on the section traffic information, wherein the section traffic information at least includes the object type and / or the total number of road traffic objects included in the candidate passable section within a preset time period, and the road traffic objects include picking robots and / or item transport tools.

[0166] According to one or more embodiments of the present disclosure, [Example 8] provides the method of Example 1, further comprising:

[0167] Optionally, at least two candidate passage paths are determined for the picking robot to move between multiple shelves in the warehouse based on the multiple picking tasks, including: determining multiple target picking points corresponding to the multiple picking tasks, and multiple initial passage paths based on the multiple target picking points and road layout information in the warehouse; and determining at least two candidate passage paths from the multiple initial passage paths in order of length from short to long.

[0168] According to one or more embodiments of the present disclosure, [Example 9] provides the method of Example 1, further comprising:

[0169] Optionally, after controlling the picking robot to move along the picking path, it also includes: when there is an obstacle in the picking path that hinders the movement of the picking robot, controlling the picking robot according to a preset obstacle avoidance strategy, wherein the preset obstacle avoidance strategy includes waiting for the obstacle to be removed before passing, or updating the picking path and moving along the updated picking path.

[0170] According to one or more embodiments of the present disclosure, [Example 10] provides a control device for a picking robot, including:

[0171] a candidate path determination module, configured to determine, when a picking robot receives multiple picking tasks, at least two candidate paths for the picking robot to move between multiple shelves in a warehouse based on the multiple picking tasks;

[0172] a traffic efficiency determination module, configured to determine a path traffic efficiency corresponding to each candidate traffic path based on a traffic influencing factor corresponding to each candidate traffic path, wherein the traffic influencing factor includes at least one of road layout information of the warehouse, road traffic information, and a collaboration influencing factor with a picker;

[0173] The robot control module is used to determine a picking path from the multiple candidate paths according to the path efficiency, and control the picking robot to move along the picking path to transport the items to be picked.

[0174] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0175] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0176] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A control method for a picking robot, characterized in that: include: When the picking robot receives multiple picking tasks, determining at least two candidate passage paths for the picking robot to move between multiple shelves in the warehouse based on the multiple picking tasks; Determining the path traffic efficiency corresponding to each candidate traffic path according to the traffic influencing factors corresponding to each candidate traffic path, wherein the traffic influencing factors include at least one of the road layout information of the warehouse, road traffic information, and a collaboration influencing factor between the warehouse and the picker; A picking path is determined from the plurality of candidate paths according to the path efficiency, and the picking robot is controlled to move along the picking path to transport items to be picked.

2. The control method of the picking robot according to claim 1, characterized in that: The determining the path traffic efficiency corresponding to each candidate traffic path according to the traffic influencing factor corresponding to each candidate traffic path includes: For each candidate pass path, dividing the candidate pass path into at least two candidate pass sections, and determining the pass influencing factors corresponding to each candidate pass section respectively; For each candidate passable road section, determining the road section traffic efficiency corresponding to the candidate passable road section according to the traffic influencing factors corresponding to the candidate passable road section; The path traffic efficiency corresponding to the candidate pass path is determined according to the section traffic efficiencies corresponding to the plurality of candidate pass sections.

3. The control method of the picking robot according to claim 2, characterized in that: The determining the section traffic efficiency corresponding to the candidate passable road section according to the traffic influencing factors corresponding to the candidate passable road section includes: In a case where the traffic influencing factors include the road layout information of the warehouse, the traffic efficiency of the section corresponding to the candidate passable section is determined based on the road layout information, wherein the road layout information includes multiple passable sections and the traffic layout parameters of each of the passable sections, and the traffic layout parameters include at least one of section size information, preset traffic objects, preset traffic speeds, and preset traffic directions.

4. The control method of the picking robot according to claim 3, characterized in that: The determining, based on the road layout information, the road section traffic efficiency corresponding to the candidate traffic section includes: Determining, based on the road layout information in the warehouse, traffic layout parameters corresponding to the candidate traffic section, and determining an efficiency impact parameter corresponding to the traffic layout parameter, wherein the efficiency impact parameter is used to indicate the degree of influence of the traffic layout parameter on the traffic efficiency of the candidate traffic section; The section traffic efficiency of the candidate passable section is determined according to the traffic layout parameter and the efficiency impact parameter corresponding to the candidate passable section.

5. The control method of the picking robot according to claim 2, characterized in that: The determining the section traffic efficiency corresponding to the candidate passable road section according to the traffic influencing factors corresponding to the candidate passable road section includes: In the case where the traffic influencing factors include collaboration influencing factors with pickers, the traffic efficiency of the candidate traffic section is determined based on the collaboration influencing factors, wherein the collaboration influencing factors at least include collaboration distance information, and the collaboration distance information is the shortest distance from the picker closest to the candidate traffic section in the warehouse to the candidate traffic section.

6. The control method of the picking robot according to claim 5, characterized in that: The determining of the section traffic efficiency of the candidate traffic section according to the collaborative influencing factors includes: Determine a collaboration influence parameter corresponding to the collaboration distance information, and determine the section traffic efficiency of the candidate traffic section based on the collaboration influence parameter, wherein the collaboration influence parameter is used to indicate the degree of influence of the picker on the picking efficiency.

7. The control method of the picking robot according to claim 2, characterized in that The determining the section traffic efficiency corresponding to the candidate passable road section according to the traffic influencing factors corresponding to the candidate passable road section includes: In the case where the traffic influencing factors include road traffic information of the candidate pass path, the section traffic information corresponding to the candidate pass section is determined, and the section traffic efficiency corresponding to the candidate pass section is determined based on the section traffic information, wherein the section traffic information at least includes the object type and / or the total number of road traffic objects included in the candidate pass section within a preset time period, and the road traffic objects include picking robots and / or item transport tools.

8. The control method of the picking robot according to claim 1, characterized in that: The determining, based on the plurality of picking tasks, at least two candidate paths for the picking robot to move between the plurality of shelves in the warehouse comprises: Determining a plurality of target picking locations corresponding to the plurality of picking tasks, and a plurality of initial travel paths based on the plurality of target picking locations and road layout information in the warehouse; At least two candidate passing paths are determined from the plurality of initial passing paths in order of length from short to long.

9. The control method of the picking robot according to claim 1, characterized in that: After controlling the picking robot to move along the picking path, the method further includes: When there is an obstacle in the picking path that hinders the movement of the picking robot, the picking robot is controlled according to a preset obstacle avoidance strategy, wherein the preset obstacle avoidance strategy includes waiting for the obstacle to be removed before passing, or updating the picking path and moving along the updated picking path.

10. A control device for a picking robot, characterized in that: include: a candidate path determination module, configured to determine, when a picking robot receives multiple picking tasks, at least two candidate paths for the picking robot to move between multiple shelves in a warehouse based on the multiple picking tasks; a traffic efficiency determination module, configured to determine a path traffic efficiency corresponding to each candidate traffic path based on a traffic influencing factor corresponding to each candidate traffic path, wherein the traffic influencing factor includes at least one of road layout information of the warehouse, road traffic information, and a collaboration influencing factor with a picker; The robot control module is used to determine a picking path from the multiple candidate paths according to the path efficiency, and control the picking robot to move along the picking path to transport the items to be picked.

11. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the control method of the picking robot as described in any one of claims 1 to 9.

12. A storage medium containing computer-executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to execute the control method of the picking robot as described in any one of claims 1 to 9.

13. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the control method of the picking robot according to any one of claims 1 to 9.

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