Multi-robot Scheduling Method, Device, Equipment and Storage Medium in Warehouse Management

Abstract

The present invention relates to the field of robots, and discloses a multi-robot scheduling method, device, equipment and storage medium in warehouse management. This method is used to achieve efficient conflict-free scheduling of multiple robots in warehouse management through the integration of algorithmic path planning, conflict prediction and handling. The method includes: receiving job orders, sorting the job tasks in the job orders according to the priority and the principle of overall optimal path to obtain a job task sequence; assigning the job tasks in the job task sequence to the robots to be assigned in sequence to generate multiple robot-job pairs; using a fusion algorithm formed by the improved artificial potential field method and the spatio-temporal A* algorithm with a model prediction mechanism to perform path planning on the assigned robots in each robot-job pair, and using the CBS algorithm for conflict prediction. When there is a conflict, update the robot job paths and schedule the assigned robots.

Description

Technical Field

[0001] The present invention relates to the technical field of robots, and in particular, to a multi-robot scheduling method, device, equipment and storage medium in warehouse management. Background Art

[0002] An intelligent warehouse management system aims to achieve automated management of goods warehousing, outwarehousing and inventory allocation, and consists of a business system, a central scheduling system and robots. The typical process is that managers issue tasks through the business system, the central scheduling system arranges robots and issues tasks, and the robots execute them. In practice, the warehouse space is large, and multiple robots need to cooperate to work, which requires the overall coordination ability of the scheduling system.

[0003] The current mainstream rule-based technical solutions in the industry (such as single-robot shortest path planning + traffic rule constraints) have the core advantages of system stability and security, but there are significant limitations:

[0004] Insufficient intelligence level: Only coordinating multiple robots through static rules (such as one-way channels, priority avoidance) cannot dynamically respond to changes in the warehouse environment (such as sudden order surges, equipment failures);

[0005] Efficiency bottleneck: The global resource allocation is not optimized, which may lead to an increase in the robot idle rate (such as repeated path coverage, uneven task allocation);

[0006] High cost of scenario migration: The rules need to be customized for a specific warehouse layout, and when the shelf density, aisle width or operation process is adjusted, the rule base needs to be redesigned.

[0007] The centralized and distributed scheduling solutions proposed by academic research, although breaking through the traditional limitations in theory, face engineering problems:

[0008] The centralized solution (such as global path search with constraints) needs to handle NP-hard level computational complexity, and it is difficult to meet the millisecond-level response requirements of the warehouse scenario in real time;

[0009] The distributed solution (such as local rules + priority iteration) reduces the computational pressure, but may fall into local optimality, especially when the robot density exceeds the threshold, deadlocks are likely to occur;

[0010] Model simplification assumption: Ignoring the dynamic characteristics of robots (such as acceleration limit, turning radius) and physical collision volume, resulting in a deviation of more than 30% between the simulation results and the actual operation.

[0011] Therefore, the existing technologies still need to be improved and developed. Summary of the Invention

[0012] The present invention provides a multi-robot scheduling method, device, equipment and storage medium in warehouse management, which is used to achieve efficient conflict-free scheduling of multiple robots in warehouse management through the integration of algorithm path planning, conflict prediction and processing.

[0013] In the first aspect of the present invention, a multi-robot scheduling method in warehouse management is provided. The multi-robot scheduling method in warehouse management includes: receiving a job order, and sorting the job tasks in the job order according to the priority and the principle of the overall optimal path to obtain a job task sequence; obtaining the working states of all robots, and screening out the robots with the working state of being idle as the robots to be assigned; assigning the job tasks in the job task sequence to the robots to be assigned in sequence to generate multiple robot-job pairs, and each robot-job pair includes a job task and the assigned robot corresponding to the job task; using a fusion algorithm formed by an improved artificial potential field method and a spatio-temporal A* algorithm with a model prediction mechanism to perform path planning on the assigned robots in each robot-job pair to obtain robot job paths, and generating an initial scheduling path set according to all robot job paths; using the CBS algorithm to perform conflict prediction on the robot job paths in the initial scheduling path set to determine whether there are conflicts in the robot job paths; if so, determining the conflict type of the robot job paths, and regenerating conflict-free robot job paths based on the conflict type using a fusion algorithm formed by an improved artificial potential field method and a spatio-temporal A* algorithm, and adding the conflict-free robot job paths to the scheduling path set to obtain an updated scheduling path set; scheduling the assigned robots based on the robot job paths in the updated scheduling path set.

[0014] Preferably, receiving a job order and sorting the job tasks in the job order according to the priority and the principle of the overall optimal path to obtain a job task sequence includes: receiving a job order, performing a first sorting on the job tasks in the job order according to the priority of the job order to obtain a first-sorted job task; for the job tasks with the same priority in the first-sorted job tasks, calculating the total job moving distance of each job task; based on the total job moving distance of each job task, sorting the job tasks with the same priority according to the principle of the optimal path to obtain a second-sorted job task; integrating the first-sorted job task and the second-sorted job task to obtain a job task sequence.

[0015] Preferably, each job task includes at least one job assignment, the total job moving distance of the job task is the sum of the assignment moving distances of all job assignments corresponding to the job task, and the assignment moving distance of the job assignment is represented in any one of the Manhattan distance, Euclidean distance and real distance.

[0016] Preferably, the method of assigning the operation tasks in the operation task sequence to the to-be-assigned robots in sequence to generate a plurality of robot-operation pairs, where each robot-operation pair includes an operation task and the assigned robot corresponding to the operation task, includes: for operation tasks with different priorities, assigning the operation tasks according to the level of priority to obtain a plurality of operation teams; for each operation team, calculating the real distance between the to-be-assigned robot and the starting point of the first operation assignment of each operation task in the operation team, calculating the global distance according to the real distance and the total operation moving distance of the corresponding operation task, and based on the principle of minimizing the global distance, allocating the operation task to the corresponding to-be-assigned robot to generate a robot-operation pair, where each robot-operation pair includes an operation task and the assigned robot corresponding to the operation task.

[0017] Preferably, the fusion algorithm formed by the spatio-temporal A* algorithm using the improved artificial potential field method and the model prediction mechanism performs path planning for the selected robots in each robot operation pair to obtain the robot operation paths, and generates an initial scheduling path set according to all the robot operation paths, including: for each of the robot operation pairs, determining the starting position and the target operation position of the selected robot in each robot operation pair; creating an open set and a close set, where the open set is used to store the node states to be evaluated, and the close set is used to record the node states that have been evaluated. The close set is initially set to be empty, and a starting node state is constructed and placed into the open set. The starting node state is {(starting position coordinates, initial timestamp): heuristic value}; selecting the node state with the smallest heuristic value from the open set as best, placing best into the close set, and determining whether the node corresponding to best is the end point; if not, then performing an adjacent node processing step, where the adjacent node processing step includes: obtaining the adjacent node set subs of the node corresponding to best, and determining whether the adjacent node set subs is empty; if not, then selecting a node from the adjacent node set subs, calculating the movement time consumed from the node corresponding to best to the selected node, and generating the state of the selected node according to the calculated movement time consumed. The state of the selected node is {(coordinates of the selected node, movement time consumed): heuristic value}; determining whether the state of the selected node is in the close set; if not, then calculating the heuristic value of the state of the selected node according to the heuristic function of the fusion algorithm formed by the improved artificial potential field method and the spatio-temporal A* algorithm, and updating the state of the selected node according to the calculation result; placing the updated node state into the open set, and deleting the selected node from the adjacent node set subs; repeating the adjacent node processing step until the adjacent node set subs is empty; when the adjacent node set subs is empty, returning to perform selecting the node state with the smallest heuristic value from the open set as best, placing best into the close set, and determining whether the node corresponding to best is the end point, until the node corresponding to best is the end point, then searching backward for the previous states in the close set, and connecting the nodes corresponding to the states in the close set in sequence to obtain the robot operation path; generating an initial scheduling path set from all the robot operation paths.

[0018] Preferably, the heuristic function of the fusion algorithm formed by the improved artificial potential field method and the spatio-temporal A* algorithm is expressed as:

[0019] ; where, represents the node where the robot is located at moment, represents the target node of the robot. is the gravitational coefficient, is the gravitational force of the node where the robot target node pair is located at time represents the obstacles existing on the current path of the robot, is the repulsive force coefficient, is the robot is the repulsive force of all the obstacles existing on the path at time on the node where it is located at time is the robot is the heuristic function of the node where it is located at time represents that the robot is at the node where it is located at time and the target node of the robot the actual distance of represents that the robot is at the node where it is located at time and the obstacles existing on the current path of the robot the actual distance of

[0020] Preferably, calculate the motion time consumption from the node corresponding to best to the selected node, specifically including: when the previous search state is the stationary state, and the node to its adjacent node has not rotated, the motion time consumption is expressed as: ; when the previous search state is the stationary state, and the node to its adjacent node has rotated, the motion time consumption is expressed as: ; if the current node and the selected adjacent node coincide, then use to determine the motion time consumption; in the formula, is the translation distance from the node to its adjacent node , is the rotation angle from the node to its adjacent node , is the translational acceleration of the robot, is the rotational speed of the robot.

[0021] The second aspect of the present invention provides a multi-robot scheduling device in warehouse management, including: a sorting module, configured to receive job orders and sort the job tasks in the job orders according to the priority and the principle of the optimal overall path to obtain a job task sequence; a monitoring module, configured to obtain the working status of all robots and screen out the robots with a free working status as the robots to be assigned; a selection module, configured to assign the job tasks in the job task sequence to the robots to be assigned in sequence to generate a plurality of robot-job pairs, and each robot-job pair includes a job task and the assigned robot corresponding to the job task; a path planning module, configured to perform path planning on the assigned robots in each robot-job pair by using a fusion algorithm formed by an improved artificial potential field method and a spatio-temporal A* algorithm with a model prediction mechanism to obtain robot job paths, and generate an initial scheduling path set according to all the robot job paths; a conflict search module, configured to perform conflict prediction on the robot job paths in the initial scheduling path set by using the CBS algorithm to determine whether there are conflicts in the robot job paths; a path update module, configured to, when there are conflicts in the robot job paths, determine the conflict type of the robot job paths, and regenerate conflict-free robot job paths by using a fusion algorithm formed by an improved artificial potential field method and a spatio-temporal A* algorithm based on the conflict type, and add the conflict-free robot job paths to the scheduling path set to obtain an updated scheduling path set; a scheduling module, configured to schedule the assigned robots based on the robot job paths in the updated scheduling path set.

[0022] The third aspect of the present invention provides a multi-robot scheduling device in warehouse management, including: a memory and at least one processor, wherein computer-readable instructions are stored in the memory, and the memory and the at least one processor are interconnected by a line; the at least one processor calls the computer-readable instructions in the memory so that the multi-robot scheduling device in warehouse management executes each step of the multi-robot scheduling method in warehouse management as described above.

[0023] The fourth aspect of the present invention provides a computer-readable storage medium, in which computer-readable instructions are stored, and when it runs on a computer, it enables the computer to execute each step of the multi-robot scheduling method in warehouse management as described above.

[0024] In the technical solution provided by the present invention, the spatio-temporal A* algorithm that combines and improves the artificial potential field method and the model prediction mechanism is used to perform path planning for the selected robots in each pair of robot operations. Considering the dynamic changes in the robot's motion space, the actual motion model, and various conflict situations, it can plan a more reasonable and practical path for the robot in a complex warehouse environment, reduce the deviation between path planning and actual operation, and improve the safety and stability of the robot's operation. Moreover, the CBS algorithm is used to predict and judge conflicts in the robot operation paths in the initial scheduling path set, and a fusion algorithm is used to re-plan the paths for different conflict types, which can timely detect and resolve conflicts in the robot operation paths, ensure that no collisions or other problems occur when multiple robots cooperate in the warehouse space, and improve the reliability of the entire scheduling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart of a multi-robot scheduling method in warehouse management provided by an embodiment of the present invention;

[0026] Figure 2 It is a schematic structural diagram of a multi-robot scheduling device in warehouse management provided by an embodiment of the present invention;

[0027] Figure 3 It is a schematic structural diagram of a multi-robot scheduling device in warehouse management provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of the present invention are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the terms "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] For ease of understanding, the specific process of the embodiment of the present invention is described below. Please refer to Figure 1 , an embodiment of a multi-robot scheduling method in warehouse management in an embodiment of the present invention includes:

[0030] S101. Receive a job order, and sort the job tasks in the job order according to the priority and the principle of the overall optimal path to obtain a job task sequence;

[0031] S102. Obtain the working status of all robots, and filter out the robots with a free working status as the robots to be assigned;

[0032] S103. Assign the job tasks in the job task sequence to the robots to be assigned in sequence to generate multiple robot-job pairs. Each robot-job pair includes a job task and the assigned robot corresponding to the job task;

[0033] S104. Use a fusion algorithm formed by improving the artificial potential field method and the spatio-temporal A* algorithm with a model prediction mechanism to perform path planning on the assigned robots in each robot-job pair to obtain robot job paths, and generate an initial scheduling path set according to all robot job paths;

[0034] S105. Use the CBS algorithm to predict conflicts in the robot job paths in the initial scheduling path set to determine whether there are conflicts in the robot job paths;

[0035] S106. If so, determine the conflict type of the robot job paths, and based on the conflict type, use a fusion algorithm formed by improving the artificial potential field method and the spatio-temporal A* algorithm to regenerate conflict-free robot job paths, and add the conflict-free robot job paths to the scheduling path set to obtain an updated scheduling path set;

[0036] S107. Schedule the assigned robots based on the robot job paths in the updated scheduling path set.

[0037] It can be understood that the execution subject of the present invention can be a multi-robot scheduling device in warehouse management, or a terminal or a server. Specifically, it is not limited here. In this embodiment of the present invention, the server is used as the execution subject for illustration.

[0038] In this embodiment, in step S101, receive a job order, and sort the job tasks in the job order according to the priority and the principle of the overall path being optimal to obtain a job task sequence, including: receiving the job order, performing a first sorting on the job tasks in the job order according to the priority of the job order to obtain the first-sorted job tasks; for the job tasks with the same priority in the first-sorted job tasks, calculate the total job movement distance of each job task; based on the total job movement distance of each job task, sort the job tasks with the same priority according to the principle of the path being optimal to obtain the second-sorted job tasks; integrate the first-sorted job tasks and the second-sorted job tasks to obtain the job task sequence.

[0039] In this embodiment, each job task includes at least one job assignment, and the assignment movement distance of the job assignment can be represented in three ways: Manhattan distance, Euclidean distance, and true distance, that is, the assignment movement distance of the job assignment It can be expressed as:

[0040] or

[0041] or

[0042] .

[0043] In the formula, represents the th job assignment of the job task, represents the starting point of the th job assignment of the job task, represents the end point of the th job assignment of the th job task, represents the Manhattan distance, represents the Euclidean distance, represents the real distance, that is, the path distance from the starting point to the target point calculated by the A* algorithm.

[0044] In this embodiment, the total job moving distance of the job task is the sum of the assignment moving distances of all job assignments of the job task, and can be expressed as:

[0045] .

[0046] In the formula, D represents the total job moving distance of the job task.

[0047] In this embodiment, in step S102, the working states of the robot include idle, moving, working, waiting for planning, charging, and downtime, etc.

[0048] In this embodiment, real-time status data (such as position, power, task progress) is obtained through the robot communication module, and robots to be assigned are screened according to preset rules (such as sufficient power, no faults).

[0049] Example: Robot 1 is marked as "charging" because its power is lower than 20%, and Robot 2 with the status of "idle" is selected.

[0050] In this embodiment, in step S103, the operation tasks in the operation task sequence are assigned to the robots to be assigned in sequence to generate robot-operation pairs, including: for operation tasks with different priorities, the operation tasks are assigned according to the level of priority to obtain multiple operation teams; for each operation team, calculate the actual distance between the robot to be assigned and the starting point of the first operation assignment of each operation task in the operation team, calculate the global distance according to the actual distance and the total operation moving distance of the corresponding operation task, and based on the principle of minimizing the global distance, assign the operation task to the corresponding robot to be assigned to generate robot-operation pairs.

[0051] In this embodiment, a robot-operation pair refers to the pairing combination between a specific operation task and the robot responsible for executing the task in the warehouse management system. Each operation pair clearly includes an operation task and the selected robot responsible for executing the task, ensuring that each task has a corresponding execution entity.

[0052] In this embodiment, by preferentially allocating high-priority tasks (such as red-alert orders, VIP customer requirements), the on-time completion rate of key operations is ensured (the SLA compliance rate is increased by 15% - 25%1). For example: in a medical supplies warehouse, emergency medicine orders automatically obtain the highest priority. Another example is that in an e-commerce warehouse, orders with a promised 2-hour delivery have a higher priority than ordinary orders.

[0053] It can be understood that each operation task implies time window requirements (such as the receiving deadline, the promised outbound time limit), and the priority sorting is essentially a process of quantifying the urgency of the time window.

[0054] In this embodiment, in step S104, the situations of robot conflicts (or obstacles) include four types: local dynamic node conflicts, local dynamic path conflicts, local semi-dynamic node conflicts, and static node conflicts. A local dynamic node conflict means that at a moment, an obstacle appears at a certain node on the movement path of the robot, and the obstacle disappears after a limited time; a local dynamic path conflict means that at a moment, an obstacle appears on a certain section of the movement path of the robot, and the obstacle disappears after a limited time; a local semi-dynamic node conflict means that at a moment, an obstacle appears at a certain node on the movement path of the robot, and the obstacle does not disappear; a static node conflict means that an obstacle always exists at a certain node on the movement path of the robot.

[0055] The obstacles on the map when the spatio-temporal A* algorithm is used for search Introduce the time parameter, then the local dynamic node conflict can be described as , the local dynamic path conflict can be described as , the local semi-dynamic node conflict can be described as , the static node conflict can be described as . If the search is carried out in space and is also associated with time, the situation of different conflict types can be handled. Therefore, the design form of the heuristic function is extended by introducing the time parameter, that is

[0056] .

[0057] In the formula, is the estimated cost value from the starting node to the robot target node at time ; is the actual distance from the starting node to the node where the robot is currently located at time ; is the estimated distance from the node where the robot is currently located to the target node at time

[0058] In this way, the path planning problem when the motion space changes dynamically can be initially solved: the path search is carried out in the time-space dimension. If a conflict occurs at a certain moment during the search process, the search will not be carried out at this time stamp and will instead start from the previous time stamp. However, this design will fail to search or solve the wrong search path under extreme conditions (such as narrow road avoidance scenarios, etc.). The reason is that in extreme scenarios, there are fewer expandable adjacent nodes for a single node and adjacent nodes often all generate conflicts. Therefore, it is necessary to introduce an improved artificial potential field to improve the heuristic function.

[0059] The improved artificial potential field method intervenes in the motion of the robot in advance by designing a virtual force: the target area of the robot will generate an attractive force on it. On the contrary, the obstacle area will generate a repulsive force on the robot. Finally, the two forces are combined to help the robot complete the search for the optimal path.

[0059] In this embodiment, the gravitational and repulsive forces of the artificial potential field are directly embedded in the heuristic function of the spatio-temporal A*. Therefore, the heuristic function of the fusion algorithm formed by the improved artificial potential field method and the spatio-temporal A* algorithm is expressed as:

[0060] .

[0061] In the formula, represents the node where the robot is located at time , represents the target node of the robot, is the gravitational coefficient, is the gravitational force of the robot target node on the node where it is located at time ; Indicates the obstacles existing on the current path of the robot. is the repulsive force coefficient. is the robot represents the repulsive force of all the obstacles existing on the path at time on the node where the robot is located at time is the heuristic function of the node where the robot is located at time and represents the distance between the node where the robot is located at time and the target node of the robot in terms of the true distance. represents the distance between the node where the robot is located at time and the obstacle existing on the current path of the robot in terms of the true distance.

[0062] It can be understood that in the spatio-temporal A* algorithm, due to the static motion space assumption, the heuristic function is often designed in the form of Euclidean Distance or Manhattan Distance. However, in the composite spatio-temporal model, since the motion space will change dynamically, Euclidean Distance or Manhattan Distance cannot truly depict the distance estimation situation from the node where the robot is located to the target node at a certain moment of change, which may lead to the state search entering an infinite loop. Therefore, in this embodiment, the current true distance (True Distance), that is, the path distance calculated from the starting point to the target point through the spatio-temporal A* algorithm, is introduced as the distance estimation.

[0063] When there are conflicts on the path planned by the robot, according to different forms of conflicts, the heuristic function gives different response methods: for local dynamic node conflicts and local dynamic path conflicts, the heuristic function guides the robot to search for a path in the direction to avoid conflicts at the moment when the conflict appears and resumes when the conflict disappears; for local semi-dynamic node conflicts, the heuristic function guides the robot to search for a path that completely avoids the conflict after the conflict appears, forcing the robot to turn to the direction to avoid the conflict and search for a path without returning after the conflict appears; for static node conflicts, the heuristic function will guide the robot to search for a path that completely avoids the conflict and avoid colliding with it from beginning to end.

[0064] Furthermore, in order to improve applicability, the fusion algorithm formed by improving the artificial potential field method and the spatio-temporal A* algorithm of the model prediction mechanism introduces the model prediction mechanism to expand the discrete time into continuous time. Specifically, in the process of searching for adjacent nodes of each spatial node, motion prediction is performed according to the distance between adjacent nodes, whether rotation occurs between adjacent nodes, the current motion situation of the robot, and the motion model of the actual robot.

[0065] Motion prediction based on the distance between adjacent nodes, whether rotation occurs between adjacent nodes, the current motion of the robot, and the motion model of the actual robot includes the following situations:

[0066] When the previous search state is a uniform motion state, and the node to its adjacent node has not rotated, the motion time consumption is expressed as:

[0067] .

[0068] When the previous search state is a uniform motion state, and the node to its adjacent node has rotated, the motion time consumption is expressed as:

[0069] .

[0070] When the previous search state is a stationary state, and the node to its adjacent node has not rotated, the motion time consumption is expressed as:

[0071] .

[0072] When the previous search state is a stationary state, and the node to its adjacent node has rotated, the motion time consumption is expressed as:

[0073] .

[0074] When the previous search state is a stationary state, and the node to its adjacent node has not displaced, that is at this time, the motion time consumption is expressed as:

[0075] .

[0076] In the formula, is the translation distance from node to its adjacent node , is the rotation angle from node to its adjacent node , is the translation speed of the robot, is the translation acceleration of the robot, is the rotation speed of the robot.

[0077] In this embodiment, a fusion algorithm formed by the improved artificial potential field method and the spatio-temporal A* algorithm with a model prediction mechanism is used to perform path planning for the selected robots in each robot operation pair to obtain the robot operation paths, and an initial scheduling path set is generated according to all the robot operation paths, including:

[0078] For each robot operation pair, determine the starting position and the target operation position of the selected robot in each robot operation pair;

[0079] Create an open set and a close set. The open set is used to store the node states to be evaluated, and the close set is used to record the node states that have been evaluated. The close set is initially set to be empty, and construct the starting node state and put the starting node state into the open set. The starting node state is {(starting position coordinates, initial timestamp): heuristic value};

[0080] Select the node state with the smallest heuristic value from the open set as best, put best into the close set, and determine whether the node corresponding to best is the end point;

[0081] If not, execute the adjacent node processing step. The adjacent node processing step includes: obtain the adjacent node set subs of the node corresponding to best, and determine whether the adjacent node set subs is empty; if not, select a node from the adjacent node set subs, calculate the movement time consumption from the node corresponding to best to the selected node, and generate the state of the selected node according to the calculated movement time consumption. The state of the selected node is {(coordinates of the selected node, movement time consumption): heuristic value}; determine whether the state of the selected node is in the close set; if not, calculate the heuristic value of the state of the selected node according to the heuristic function of the fusion algorithm formed by the improved artificial potential field method and the spatio-temporal A* algorithm, and update the state of the selected node according to the calculation result; put the updated node state into the open set, and delete the selected node from the adjacent node set subs;

[0082] Repeat the execution of the adjacent node processing step until the adjacent node set subs is empty;

[0083] When the adjacent node set is empty, return to execute selecting the node state with the smallest heuristic value from the open set as best, put best into the close set, and determine whether the node corresponding to best is the end point, until the node corresponding to best is the end point, then perform a reverse search for the previous states in the close set, and connect the nodes corresponding to the states in the close set in sequence to obtain the robot operation path;

[0084] Generate an initial scheduling path set from all the robot operation paths.

[0085] In this embodiment, the heuristic value in the node state is calculated according to the heuristic function of the fusion algorithm formed by the improved artificial potential field method and the spatio-temporal A* algorithm.

[0086] In this embodiment, at the beginning of the path planning search, since it starts from the starting node, it can be considered that the previous search state is a static state. At this time, the prediction formula is selected according to whether rotation occurs between adjacent nodes. As described above:

[0087] When the previous search state is a static state, and the node to its adjacent node has no rotation, the motion time consumption is expressed as:

[0088] .

[0089] When the previous search state is a static state, and the node to its adjacent node has rotation, the motion time consumption is expressed as:

[0090] .

[0091] If the current node coincides with the selected adjacent node, that is, the case of, regardless of the previous search state, directly use to determine the motion time consumption.

[0092] In this embodiment, in step S105, in this embodiment, the search method of the conflict tree (ConflictTree, CT) in the CBS algorithm is adopted, that is, based on the best-first search strategy, a priority mechanism is introduced, and according to the urgency of the job task, the conflict branch where the robot corresponding to the job task with a higher priority is located is preferentially searched.

[0093] In this embodiment, when using the CBS algorithm to perform conflict prediction on the robot job paths in the initial scheduling path set, it is determined whether there are the same position nodes in the same time period for the robot job paths. If so, it is determined that there is a conflict in the robot job path. If not, it is determined that there is no conflict in the robot job path.

[0094] The CBS algorithm simulates the operation of each robot along its respective operation path. It divides time into discrete time steps and checks the positions of all robots within each time step. If at a certain moment, two or more robots are at the same position node, or their paths are about to cross, a conflict is detected. For example, if robot A reaches the coordinate (10, 10) at the 5th time step and robot B also plans to reach this coordinate at the 5th time step, this constitutes a conflict. By checking the positions and path directions of all robots step by step in this way, the CBS algorithm can comprehensively predict the operation paths of the robots, obtain the operation situation of the robot operation paths, and the operation situation of the robot operation paths details information such as the time, position where conflicts may occur, and the robot numbers involved.

[0095] In this embodiment, the conflict types include four types: local dynamic node conflict, local dynamic path conflict, local semi-dynamic node conflict, and static node conflict. When there is a conflict on the planned path of the robot, according to different forms of the conflict, the heuristic function gives different response methods: for local dynamic node conflict and local dynamic path conflict, the heuristic function guides the robot to search for a path in a direction to avoid the conflict at the moment the conflict appears and resumes when the conflict disappears; for local semi-dynamic node conflict, the heuristic function guides the robot to search for a path that completely avoids the conflict after the conflict appears, forcing the robot to turn to a direction to avoid the conflict after the conflict appears and not return; for static node conflict, the heuristic function will guide the robot to search for a path that completely avoids the conflict and avoid colliding with it from beginning to end.

[0096] Specifically, for different conflict types, a fusion algorithm formed by improving the artificial potential field method and the spatio-temporal A algorithm starts from the nodes near the position where the conflict occurs, and continuously explores new nodes according to the heuristic function and search strategy, calculates the motion time consumption, and evaluates the cost of the new path. During the search process, various factors such as obstacles and the motion trajectories of other robots will be comprehensively considered, and finally a new conflict-free path, that is, a conflict-free robot operation path, will be generated.

[0097] In this embodiment, in step S107, the robot operation paths in the updated scheduling path set are sent to the robots and the execution process is monitored.

[0098] Specifically, the path sequence (coordinate + timestamp) is transmitted to the robot through the communication module, and the position, speed, and task progress of the robot are tracked to dynamically handle emergencies (such as the addition of obstacles).

[0099] If the robot deviates from the path or fails, a replanning process is triggered. For example, if robot C suspends the task due to insufficient power, the system reassigns other robots to take over.

[0100] The method provided in this embodiment is a multi-robot scheduling method in warehouse management. It uses a spatio-temporal A* algorithm that combines and improves the artificial potential field method and the model prediction mechanism to perform path planning for the selected robots in each robot-job pair. Considering the dynamic changes in the robot's motion space, the actual motion model, and various conflict situations, it can plan a more reasonable and practical path for the robot in a complex warehouse environment, reduce the deviation between path planning and actual operation, and improve the safety and stability of the robot's operation. Moreover, the CBS algorithm is used to predict and judge conflicts in the robot-job paths in the initial scheduling path set, and a fusion algorithm is used to re-plan the path for different conflict types, which can timely detect and solve conflicts in the robot-job paths, ensure that no collisions occur when multiple robots cooperate in the warehouse space, and improve the reliability of the entire scheduling system.

[0101] The multi-robot scheduling method in warehouse management in the embodiments of the present invention has been described above. Next, the device in the embodiments of the present invention will be described. Please refer to Figure 2 The implementation manner of the multi-robot scheduling device in the embodiments of the present invention includes:

[0102] The sorting module 201 is configured to receive job orders and sort the job tasks in the job orders according to the priority and the principle of the overall optimal path to obtain a job task sequence;

[0103] The monitoring module 202 is configured to obtain the working status of all robots and screen out the robots with an idle working status as the robots to be assigned;

[0104] The selection module 203 is configured to assign the job tasks in the job task sequence to the robots to be assigned in sequence to generate multiple robot-job pairs, and each robot-job pair includes a job task and the selected robot corresponding to the job task;

[0105] The path planning module 204 is configured to use a fusion algorithm formed by the improved artificial potential field method and the spatio-temporal A* algorithm with a model prediction mechanism to perform path planning for the selected robots in each robot-job pair to obtain robot-job paths, and generate an initial scheduling path set according to all the robot-job paths;

[0106] The conflict search module 205 is configured to use the CBS algorithm to predict conflicts in the robot-job paths in the initial scheduling path set to determine whether there are conflicts in the robot-job paths;

[0107] A path update module 206, configured to determine the conflict type of a robot operation path when there is a conflict in the robot operation path, and regenerate a conflict-free robot operation path by using a fusion algorithm formed by an improved artificial potential field method and a spatio-temporal A* algorithm based on the conflict type, and add the conflict-free robot operation path to the scheduling path set to obtain an updated scheduling path set;

[0108] A scheduling module 207, configured to schedule the selected robots based on the robot operation paths in the updated scheduling path set.

[0109] In this embodiment, the spatio-temporal A* algorithm that fuses the improved artificial potential field method and the model prediction mechanism is used to perform path planning for the selected robots in each robot operation pair. Considering the dynamic changes in the robot motion space, the actual motion model, and various conflict situations, it can plan a more reasonable and practical path for the robot in a complex warehouse environment, reduce the deviation between path planning and actual operation, and improve the safety and stability of the robot operation. Moreover, the CBS algorithm is used to predict and judge the conflicts in the robot operation paths in the initial scheduling path set, and a fusion algorithm is used to re-plan the paths for different conflict types, which can timely detect and solve the conflicts in the robot operation paths, ensure that no collisions occur when multiple robots cooperate in the warehouse space, and improve the reliability of the entire scheduling system.

[0110] Figure 2 The structure of the multi-robot scheduling device in the warehouse management shown does not limit the multi-robot scheduling device in the warehouse management, and can implement the steps of the multi-robot scheduling method in the warehouse management provided in the above method embodiments.

[0111] Above Figure 2 The multi-robot scheduling device in the warehouse management in the embodiments of the present invention is described in detail from the perspective of modular functional entities. Next, the multi-robot scheduling device in the warehouse management in the embodiments of the present invention is described in detail from the perspective of hardware processing.

[0112] Figure 3FIG. 0 is a schematic structural diagram of a multi-robot scheduling device in warehouse management provided by an embodiment of the present invention. The device 300 may vary greatly due to configuration or performance differences, and may include one or more processors (central processing units, CPUs) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 for storing application programs 333 or data 332 (for example, one or more mass storage devices). Among them, the memory 320 and the storage media 330 may be transient storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the device 300. Further, the processor 310 may be configured to communicate with the storage media 330 and execute a series of instruction operations in the storage media on the device 300.

[0113] The device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on.

[0114] An embodiment of the present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is caused to execute the steps of the multi-robot scheduling method in warehouse management.

[0115] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, or units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0116] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0117] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. A multi-robot scheduling method in warehouse management, characterized in that: The multi-robot scheduling method in warehouse management includes: Receive job orders, and sort the job tasks in the job orders according to the priority and overall path optimal principle to obtain a job task sequence; Get the working status of all robots and select robots with idle working status as robots to be assigned; Assigning the work tasks in the work task sequence to the robots to be assigned in order to generate a plurality of robot work pairs, each of which includes a work task and a selected robot corresponding to the work task; The fusion algorithm formed by the improved artificial potential field method and the spatiotemporal A* algorithm of the model prediction mechanism is used to plan the path of the selected robot in each robot operation pair, obtain the robot operation path, and generate the initial scheduling path set based on all robot operation paths; The CBS algorithm is used to predict the conflicts of the robot operation paths in the initial scheduling path set to determine whether there are conflicts in the robot operation paths; If so, the conflict type of the robot operation path is determined, and based on the conflict type, a fusion algorithm formed by the improved artificial potential field method and the space-time A* algorithm is used to regenerate a conflict-free robot operation path, and the conflict-free robot operation path is added to the scheduling path set to obtain an updated scheduling path set; The selected robots are scheduled based on the robot operation paths in the updated scheduling path set.

2. The multi-robot scheduling method in warehouse management according to claim 1, characterized in that: Receive the job order and sort the job tasks in the job order according to the priority and overall path optimization principle to obtain the job task sequence, including: Receive the job order, sort the job tasks in the job order according to the priority of the job order, and obtain a sorted job task; For the tasks with the same priority in a sorted task, calculate the total moving distance of each task; Based on the total moving distance of each task, the tasks of the same priority are sorted according to the optimal path principle to obtain secondary sorted tasks; Integrate the first-order sorting job tasks and the second-order sorting job tasks to obtain a job task sequence.

3. The multi-robot scheduling method in warehouse management according to claim 2 is characterized in that: Each of the work tasks includes at least one work assignment, and the total work moving distance of the work task is the sum of the assigned moving distances of all work assignments corresponding to the work task. The assigned moving distance of the work assignment is represented by any one of Manhattan distance, Euclidean distance and real distance.

4. The multi-robot scheduling method in warehouse management according to claim 1, characterized in that: The process of assigning the work tasks in the work task sequence to the robots to be assigned in order to generate a plurality of robot work pairs, each of which includes a work task and a selected robot corresponding to the work task, comprises: For tasks with different priority levels, the tasks are assigned according to the priority levels to obtain multiple work teams; For each work team, the actual distance between the robot to be assigned and the starting point of the first work assignment of each work task in the work team is calculated, and the global distance is calculated according to the actual distance and the total work moving distance of the corresponding work task. Based on the principle of minimizing the global distance, the work task is assigned to the corresponding robot to be assigned to generate a robot work pair, each of which includes a work task and a selected robot corresponding to the work task.

5. The multi-robot scheduling method in warehouse management according to claim 1, characterized in that: The fusion algorithm formed by the improved artificial potential field method and the spatiotemporal A* algorithm of the model prediction mechanism performs path planning for the selected robots in each robot operation pair to obtain the robot operation path, and generates an initial scheduling path set according to all robot operation paths, including: For each of the robot operation pairs, determining a starting position and a target operation position of the selected robot in each robot operation pair; Create an open set and a close set. The open set is used to store the node states to be evaluated, and the close set is used to record the node states that have been evaluated. The close set is initially set to be empty, and a starting node state is constructed. The starting node state is placed in the open set. The starting node state is {(starting position coordinates, initial timestamp): heuristic value}; Select the node state with the smallest heuristic value from the open set as the best, put the best into the close set, and determine whether the node corresponding to the best is the end point; If not, then execute the adjacent node processing step, which includes: obtaining the adjacent node set subs of the node corresponding to best, and judging whether the adjacent node set subs is empty; if not, selecting a node from the adjacent node set subs, calculating the movement time from the node corresponding to best to the selected node, and generating the state of the selected node according to the calculated movement time, the state of the selected node is {(coordinates of the selected node, movement time): heuristic value}; judging whether the state of the selected node is in the close set; if not, calculating the heuristic value of the state of the selected node according to the heuristic function of the fusion algorithm formed by the improved artificial potential field method and the space-time A* algorithm, and updating the state of the selected node according to the calculation result; putting the updated node state into the open set, and deleting the selected node from the adjacent node set subs; Repeat the adjacent node processing steps until the adjacent node set subs is empty; When the adjacent node set subs is empty, return to execute to select the node state with the smallest heuristic value from the open set as the best, put the best into the close set, and determine whether the node corresponding to the best is the end point, until the node corresponding to the best is the end point, then reversely search the previous state in the close set, and connect the nodes corresponding to the states in the close set in order to obtain the robot operation path; All the robot operation paths are used to generate an initial scheduling path set.

6. The multi-robot scheduling method in warehouse management according to claim 5, characterized in that: The heuristic function of the fusion algorithm formed by the improved artificial potential field method and the space-time A* algorithm is expressed as: ; In the formula, Indicates that the robot is The node at which the moment is located, represents the robot's target node, is the gravitational coefficient, is the robot target node pair The gravitational force of the node at the moment; Indicates the obstacles on the robot's current path. is the repulsion coefficient, It's a robot All obstacles on the path at the moment The repulsion of the node at the moment; It's a robot The heuristic function of the node at the time, Indicates that the robot is The node at the moment and the robot's target node The real distance Indicates that the robot is The node at the moment and the obstacles on the robot's current path The real distance.

7. The multi-robot scheduling method in warehouse management according to claim 6, characterized in that: Calculate the movement time from the best corresponding node to the selected node, including: When the previous search state is static and the node To its neighboring nodes If no rotation occurs, the motion time is expressed as: ; When the previous search state is static and the node To its neighboring nodes If rotation occurs, the motion time is expressed as: ; If the current node and the selected adjacent node coincide, use To determine the duration of the movement; where For Node To its neighboring nodes The translation distance, For Node To its neighboring nodes The rotation angle of is the robot translation acceleration, is the robot rotation speed.

8. A multi-robot scheduling device in warehouse management, characterized in that: include: A sorting module is used to receive job orders and sort the job tasks in the job orders according to the priority and overall path optimization principle to obtain a job task sequence; The monitoring module is used to obtain the working status of all robots and select robots with idle working status as robots to be assigned; A selection module is used to select the work tasks in the work task sequence to the robots to be assigned in order, and generate a plurality of robot work pairs, each of which includes a work task and a selected robot corresponding to the work task; The path planning module is used to plan the path of the selected robot in each robot operation pair by using a fusion algorithm formed by the improved artificial potential field method and the spatiotemporal A* algorithm of the model prediction mechanism, obtain the robot operation path, and generate an initial scheduling path set based on all robot operation paths; The conflict search module is used to use the CBS algorithm to predict the conflicts of the robot operation paths in the initial scheduling path set to determine whether there are conflicts in the robot operation paths; The path updating module is used to determine the conflict type of the robot operation path when there is a conflict in the robot operation path, and regenerate a conflict-free robot operation path based on the conflict type by using a fusion algorithm formed by an improved artificial potential field method and a spatiotemporal A* algorithm, and add the conflict-free robot operation path to the scheduling path set to obtain an updated scheduling path set; The scheduling module is used to schedule the selected robots based on the robot operation paths in the updated scheduling path set.

9. A multi-robot scheduling device in warehouse management, characterized in that: comprising a memory and at least one processor, wherein the memory has computer-readable instructions stored therein; The at least one processor calls the computer-readable instructions in the memory to execute the various steps of the multi-robot scheduling method in warehouse management as described in any one of claims 1-7.

10. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the various steps of the multi-robot scheduling method in warehouse management as described in any one of claims 1-7 are implemented.

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