Carrying robot cluster scheduling method, electronic equipment and medium

Abstract

The invention discloses a transfer robot cluster scheduling method, electronic equipment and a medium. The method comprises the steps that an ERP issues an order group to an enterprise warehouse management system WMS to query goods allocation storage information; order group order picking information is formed and issued to a WCS, and AGV group scheduling is carried out in combination with available AGV resources; based on the cost optimization model of the multiple transfer robots, considering two optimization objectives of minimum total operation cost of the multiple transfer robots and balanced operation cost of the multiple transfer robots, and solving an AGV (Automatic Guided Vehicle) carrying path according to a total utility optimal principle; the AGV group executes path planning and executes two walking modes of European style and Manhattan distance for carrying; and finishing the carrying task and updating the carrying task. According to the invention, the linear four-direction constraint of Manhattan distance is relieved, the robot is allowed to pass through the preset oblique channel to carry out load-carrying oblique running, and the energy consumed in the steering process of the robot is reduced; in addition, a waiting area is omitted, the storage area is saved, and the warehouse construction cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing, and in particular, to a method for cluster scheduling of handling robots, an electronic device, and a medium. Background Art

[0002] Currently, the driving of handling robots on the market is all based on the Manhattan distance for scheduling. In this scheduling method, the robots can only drive in four directions: east, south, west, and north, which increases the driving path. In addition, it is necessary to set up waiting areas during the logistics process layout of the handling robots, which increases the warehousing area and construction costs. Summary of the Invention

[0003] To at least partly solve one of the technical problems existing in the prior art, an object of the present invention is to provide a method for cluster scheduling of handling robots that combines the Euclidean distance and the Manhattan distance, an electronic device, and a medium.

[0004] The first technical solution adopted by the present invention is as follows:

[0005] A method for cluster scheduling of handling robots includes the following steps:

[0006] The ERP issues an order group to the enterprise warehousing management system WMS to query the storage location information of the goods;

[0007] Form the picking information of the order group and issue it to the WCS, and perform AGV group scheduling in combination with the available AGV resources;

[0008] Based on the cost optimization model of multiple handling robots, considering two optimization objectives: the minimum total cost of multiple handling robots' operations and the balance of the operation costs of multiple handling robots, solve the path of the AGV handling path with the principle of optimal total utility;

[0009] The AGV group executes the planned path and performs handling by using two walking methods: the Euclidean distance and the Manhattan distance;

[0010] Complete the handling task and update the handling task.

[0011] Furthermore, the warehouse includes a storage area, a buffer area, and a picking area. All areas are arranged in a grid pattern. Multiple handling robots run in a straight line in the environment composed of grids, and only one handling robot can pass through each lane; a two-dimensional code is provided at the center of each grid. The stepping distance of the handling robot is the straight-line distance between two adjacent two-dimensional codes, and the driving and turning directions of the next step are judged through the two-dimensional code information.

[0012] Furthermore, the forming of the picking information of the order group and issuing it to the WCS, and performing AGV group scheduling in combination with the available AGV resources includes:

[0013] The WCS divides the tasks into m groups based on the number of customers and the number of schedulable AGVs. The number of shelves n to be carried during the picking operation is allocated to m idle handling robots and transported to p picking stations. Each order group is completed by a single picking station;

[0014] During the process of a single handling robot executing a task sequence, it is divided into two states: unloaded and loaded. Since the power consumption per unit distance traveled in the two states is different, and the power consumption is proportional to the traveling distance of the robot; during this process, the discharge characteristics of the lithium battery, the relationship between the battery consumption ratio and the remaining mileage, and the relationship between the remaining power and the task cost are considered.

[0015] Furthermore, the cost optimization model based on multiple handling robots considers two optimization objectives: minimizing the total cost of multiple handling robots' operations and balancing the operation costs of multiple handling robots. The AGV handling path is solved based on the principle of optimal total utility, including:

[0016] Based on the "goods-to-person" picking operation mode, a cost optimization model for multiple handling robots is constructed;

[0017] The cost optimization model considers two optimization objectives: minimizing the total cost of multiple mobile robots' operations and balancing the operation costs of multiple handling robots. Its objective function is:

[0018] F(π) = min(w 1 f(π) + w 2 h(π))

[0019] In the formula, f(π) represents the total task cost of all handling robots, and h(π) represents the task cost balance value; w 1 、w 2 represent the importance degrees of the two sub-objectives f(π) and h(π) on the total objective; the smaller the value of F(π), the better the scheduling scheme of multiple mobile robots

[0020] The expression of the total task cost f(π) is:

[0021]

[0022] In the formula, θ 1 represents the task correlation cost coefficient, and θ 2 represents the task self-cost coefficient; i, j represent the shelf numbers, r represents the robot number, p represents the picking station number; m represents the total number of robot numbers, n represents the total number of shelf numbers, p represents the total number of picking station numbers; x rij takes 1 to indicate that the handling robot r transports the shelves in the order of shelf i to shelf j, and vice versa takes 0; y rjp takes 1 to indicate that the handling robot r transports the shelves in the order of shelf i to the picking station p, and vice versa takes 0; C ijIndicates the task - associated distance from the robot's position to the arrival point, C j-st Indicates the distance from the arrival point to the starting point of the diagonal closest to the straight - line distance, C st-p Indicates the distance for the robot to turn and drive into the picking station.

[0023] Furthermore, the task cost of a single handling robot is:

[0024]

[0025] In the formula, q r Indicates the number of tasks assigned to robot r, Indicates that when robot r executes the q r task, the distance required to walk from point i of the shelf to point j of the shelf, Indicates that when robot r executes the q r task, the distance required to walk from point j of the shelf to the picking point p;

[0026] The relationship between the robot's battery consumption and the remaining mileage is as follows:

[0027] L(x)=ax 2 bx + c

[0028] In the formula, L(x) represents the function of the AGV's power consumption changing with time x, and a, b, c represent the constant quantities of the function analytic formula of the power consumption;

[0029] The walking mileage of each robot is within the power - consumption constraint range, and there is a reserved safety power to return to its charging area:

[0030] g(r)≤L(1 - g(r - 1))-L(1 - v)

[0031] In the formula, v is the percentage of safety power;

[0032] Because when the remaining power is the same, L 空载 >L 载货 , the power that can ensure the safe driving of the loaded robot can also ensure the safe driving of the unloaded robot for the same distance. Therefore, only the remaining mileage in the loaded state is considered to meet the safety - power requirement.

[0033] Furthermore, the handling - robot scheduling rules are divided into two cases: single - robot and multi - robot:

[0034] 1) When the number of robots affiliated with the picking station is a single handling robot, the handling robot immediately transports the next transportation task after completing the previous transportation task;

[0035] 2) When the number of subordinate robots is multiple handling robots, if the time node for each robot to complete the transportation task of the next process is greater than the time when the next process can start processing, select the robot that can complete the transportation task earliest; if only one robot has a time node for completing the handling task of the next process before the time when the process can start processing, select this robot; if multiple robots have time nodes for completing the transportation task of the next process before the time when the process can start picking, select the robot with the shortest running distance to the picking location when completing the transportation task;

[0036] The robot scheduling rule considers the earliest arrival and the shortest distance of the robot according to different situations during the picking process, so that the next task can obtain the earliest start time.

[0037] Further, the AGV group executes the planned path and performs handling in two walking modes of Euclidean distance and Manhattan distance, including:

[0038] In the storage area, the handling robot walks in a straight line four-way in the Manhattan distance mode. After entering the buffer area, the handling robot enters the starting point of the diagonal movement, that is, judges the running environment of the handling robot through the scheduling model, and the walking direction of the handling robot is changed to straight line eight-way walking, and reaches the target work station through a combination of diagonal and straight walking;

[0039] The handling robot can travel in 8 directions. Define the distance function as:

[0040] Divide the distance of the robot's load into four sections:

[0041] D 总距离 =C ij +2*C jp

[0042] In the formula, C ij represents the task-related distance from the robot position to the arrival point, C j-st represents the distance from the arrival point to the starting point of the diagonal movement with the shortest straight-line distance, C st-p represents the distance for the AGV to travel from the starting point of executing the Euclidean distance to the target picking point;

[0043] 1) The task-related distance from the robot position (X i , Y i ) to the arrival point (X j , Y j ):

[0044] C ij =d×(|X i -X j |+|Y i -Y j |)

[0045] In the formula, d is the straight-line distance between two adjacent position points on the sides;

[0046] 2) The distance from the goods location point (X j , Y j ) to the starting point of the diagonal line (X st , Y st ) with the closest straight-line distance:

[0047] C j-st = d × (|X j - X st | + |Y j - Y st |)

[0048] 3) The distance from the starting point of the diagonal line (X st , Y st ) to the picking point (X p , Y p ), passing through the end point of the diagonal line (X ed , Y ed ):

[0049] 3.1) When the picking station is located within the diagonal passage range (X p ≤ X ed ):

[0050] X ed - X p = Δd

[0051] Let represent that the robot turns from this position and drives into the picking station. The distance formula is as follows:

[0052]

[0053] 3.2) When the picking station is located outside the diagonal passage range (X p > X ed ):

[0054]

[0055] After the robot finishes picking, it will return to the goods location point along the same route.

[0056] Further, in the process of judging whether to move diagonally, whether there is an obstacle in the forward direction is taken into consideration, that is, the diagonal movement constraint of the robot must be that there are no other robots in the two grids adjacent to the next target position on the diagonal direction, so as to avoid collisions of the shelves caused by the robot's turning.

[0057] The second technical solution adopted by the present invention is:

[0058] An electronic device, the electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory. The at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a method for scheduling a cluster of handling robots as described above.

[0059] The third technical solution adopted by the present invention is:

[0060] A computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored. The at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement a method for scheduling a cluster of handling robots as described above.

[0061] The fourth technical solution adopted by the present invention is:

[0062] A computer program product or a computer program, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method.

[0063] The beneficial effects of the present invention are as follows: The present invention designs a slanting channel in the load operation buffer area, combines the Euclidean distance and the Manhattan distance for scheduling, releases the constraint of the straight four-way of the Manhattan distance, and allows it to travel obliquely with load through the preset slanting channel. This design shortens the distance for the handling machine to reach the destination picking position. At the same time, since the goods storage generally has a fixed picking direction, this scheduling method reduces the turning angle of the robot, which is beneficial to reducing the energy consumed during the turning process of the robot. This design cancels the waiting area generally set in the process of the logistics process layout of the handling robot, which will save the storage area and reduce the cost of warehouse construction. Description of the Drawings

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the drawings of the related technical solutions in the embodiments of the present invention or the prior art. It should be understood that the drawings introduced below are only for clearly expressing some embodiments of the technical solutions in the present invention for the convenience of those skilled in the art. Without creative efforts, other drawings can also be obtained based on these drawings.

[0065] Figure 1 It is a flowchart of multiple systems in the embodiment of the present invention collaborating to complete task allocation and multi-robot scheduling;

[0066] Figure 2It is the intelligent warehousing layout diagram in the embodiment of the present invention;

[0067] Figure 3 It is the relationship diagram between the battery consumption ratio and the remaining mileage in the embodiment of the present invention;

[0068] Figure 4 It is the flow chart of the collaborative Euclidean-Mannheim distance-based handling robot cluster scheduling in the embodiment of the present invention;

[0069] Figure 5 It is the schematic diagram of the robot scheduling rules in the embodiment of the present invention;

[0070] Figure 6 It is the schematic diagram of the walking directions of traditional and improved robots in the embodiment of the present invention; among them, Figure 6 in (a) is the walking direction of the traditional robot, Figure 6 in (b) is the walking direction of the improved robot;

[0071] Figure 7 It is the schematic diagram of the rules for the robot to diagonally walk to the picking station in the embodiment of the present invention. Detailed implementation manners

[0072] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation to the present invention. For the step numbers in the following embodiments, they are only set for the convenience of description and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0073] In the description of the present invention, it should be understood that for the orientation description, such as the orientation or positional relationship indicated by up, down, front, back, left, right, etc. is based on the orientation or positional relationship shown in the drawings, and it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0074] In the description of the present invention, the meaning of several is one or more, the meaning of multiple is two or more, greater than, less than, exceeding, etc. are understood as not including the present number, above, below, within, etc. are understood as including the present number. If there is a description of first and second, it is only for the purpose of distinguishing technical features and should not be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence of the indicated technical features.

[0075] In the description of the present invention, unless otherwise clearly defined, terms such as "set", "installed", "connected", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0076] Term Explanation:

[0077] AGV: Automated Guided Vehicle, an automatic guided vehicle, also known as an automatic guided handling vehicle, an automatic guided transport vehicle, or a handling robot.

[0078] ERP: Enterprise Resource Planning, an enterprise management system, an information system centered on management accounting that identifies and plans enterprise resources, thereby obtaining customer orders, completing processing and delivery, and finally receiving customer payments. ERP management software integrates all internal resources of an enterprise, plans procurement, production, cost, inventory, distribution, transportation, finance, and human resources, so as to achieve the best resource combination and obtain the best benefits.

[0079] WCS: Warehouse Control System, a warehouse control system. WCS is the English abbreviation of the warehouse control system, which plays a crucial role in logistics operations. As a link between the WMS system and the programmable logic controller (PLC) system, WCS can efficiently coordinate various logistics equipment, such as conveyors, palletizers, shuttle cars, robots, and guided vehicles, to achieve the integration of equipment system interfaces, unified scheduling, and real-time monitoring.

[0080] WMS: Warehouse Management System, a software system used to manage warehouse operations.

[0081] Aiming at the existing technical problems, the present invention provides an AGV group scheduling method that combines Euclidean distance and Manhattan distance, and designs a method of setting a diagonal channel in the load operation buffer area and scheduling by combining Euclidean distance and Manhattan distance. This method combines the general layout of intelligent warehousing, divides the process of dispatching handling robots into two distances: unloaded driving and loaded driving, and combines the requirements for setting buffer zones in the actual layout process of handling robots. After entering the buffer area during the loaded driving process, the constraints of the straight four-way Manhattan distance are removed, and it is allowed to drive diagonally under load through the preset diagonal channel. This design shortens the distance for the handling machine to reach the destination picking position. At the same time, since the storage location generally has a fixed picking direction, this scheduling method reduces the turning angle of the robot, which is beneficial to reducing the energy consumption during the turning process of the robot. This design cancels the waiting area that is generally set in the logistics process layout of handling robots, which will save warehouse area and reduce warehouse construction costs.

[0082] Embodiment 1

[0083] As shown Figure 1 in the figure, this embodiment provides a method for cluster scheduling of handling robots, including the following steps:

[0084] S1: The enterprise ERP issues an order group to the enterprise warehouse management system WMS to query the location information of goods stored in the storage location;

[0085] S2: Form the picking information of the order group and issue it to the WCS, and perform AGV group scheduling in combination with available AGV resources;

[0086] S3: Based on the cost optimization model of multiple handling robots, considering two optimization objectives of the least total cost of multiple mobile robot operations and the balance of the operation costs of multiple mobile robots, solve the path of the AGV handling path with the principle of optimal total utility;

[0087] S4: The AGV group executes the planned path and performs handling in two walking modes of Euclidean distance and Manhattan distance;

[0088] S5: Complete the handling task and update the handling task.

[0089] The picking operation of the intelligent warehousing system is in the "goods-to-person" mode. The mobile robot receives the task sequence issued by the upper control system (WCS), and transports the shelf to the picking platform according to the order of the task sequence instructions. After the picking personnel complete the picking of the goods, the shelf is transported back to the initial position. The navigation method of the mobile robot is QR code navigation. The mobile robot scans the QR code on the shelf to obtain the shelf coordinates, scans the QR code on the ground to identify its location and make a movement judgment, and uploads its own location information and task execution status to the WCS control center in real time.

[0090] The layout of the intelligent warehouse is as Figure 2 shown. From top to bottom, it is the warehouse shelf area, the high-speed operation area, and the picking platform area in turn. All areas adopt a grid arrangement mode. Multiple mobile robots run in a straight line in the environment composed of grids, and only one mobile robot can pass through each lane. The QR code is located in the center of each grid. The stepping distance of the mobile robot is the straight-line distance between adjacent two QR codes, and the next driving and turning directions are judged through the QR code information.

[0091] Assume that it is necessary to transport n shelves to complete the picking work of a batch of orders. The control center divides the tasks into m groups based on the scheduling algorithm and assigns them to m idle robots. The intelligent warehouse has p picking stations, and each order is completed by a single picking station. During the process of a single mobile robot executing the task sequence, it is divided into two states: no-load and load. The power consumption per unit distance traveled in the two states is different, and the power consumption is proportional to the traveling distance of the robot. At the same time, considering the discharge characteristics of the lithium battery, the relationship between the battery consumption ratio and the remaining mileage is as Figure 3As shown, it can be seen that as the battery usage increases, the remaining driving distance also decreases rapidly. For the scheduling problem of mobile robots with different remaining battery levels, the relationship between the remaining battery level and the task cost should be considered.

[0092] The following combines Figure 4 to elaborate in detail on the collaborative Euclidean-Manhattan distance-based scheduling of a fleet of handling robots.

[0093] (1) Parameter definition

[0094] Let i and j be the shelf numbers, where i, j ∈ (1, 2,..., n); x rij is a 0-1 decision variable. x rij takes 1 to indicate that the order of the mobile robot r carrying the shelf is shelf i → shelf j, and 0 otherwise, where r represents the robot number; p is the picking station number, p ∈ (1...p); y rip is a decision variable. y rip takes 1 to indicate that the robot r travels with the shelf i to the picking station p, and 0 otherwise; E(r) is the remaining battery level value of the mobile robot r; v is the safety battery percentage; (X i , Y i ) is the central node coordinates of shelf i. When i = 0, the mobile robot is at the original position; (X p , Y p ) is the position coordinates of the picking station; θ 1 is the task correlation cost coefficient, representing the power consumption cost per unit distance traveled by the mobile robot in the unloaded state; θ 2 is the task self-cost coefficient, representing the power consumption cost per unit distance traveled by the mobile robot in the loaded state; d is the straight-line distance between two QR code tags; is the task sequence of robot r. Among them, q r is the number of tasks assigned to robot r, that is, robot r needs to carry q r shelves in sequence. Specifically:

[0095] i and j represent the shelf numbers i, j ∈ (1, 2,..., n);

[0096] r represents the robot number, where r ∈ (1...m);

[0097] x rij takes 1 to indicate that the order of the mobile robot r carrying the shelf is from shelf i to shelf j, and 0 otherwise;

[0098] p is the picking station number, p ∈ (1...p);

[0099] y ripTake 1 to indicate that the mobile robot r transports the shelves in the order of shelf i to the picking station p, and vice versa take 0, where r ∈ (1...m);

[0100] E(r) is the current remaining battery percentage of the mobile robot r;

[0101] v represents the percentage of the safe battery level;

[0102] (X i ,Y i ) is the central node coordinate of shelf i. When i = 0, the mobile robot is at the original position;

[0103] (X p ,Y p ) is the set of location coordinates of the picking station;

[0104] (X st ,Y st ) is the set of coordinates representing the starting point of the diagonal;

[0105] (X ed ,Y ed ) is the set of coordinates representing the ending point of the diagonal;

[0106] θ 1 is the task-related cost coefficient, representing the cost generated per unit distance traveled in the unloaded state of the mobile robot;

[0107] θ 2 is the task self-cost coefficient, representing the cost generated per unit distance traveled in the loaded state of the mobile robot;

[0108] d is the straight-line distance between two adjacent position points and the QR code labels;

[0109] is the task sequence of robot r, where, q r is the number of tasks assigned to robot r.

[0110] During the process of the robot reaching the diagonal to the picking workstation, it may occur that the position of the picking station exceeds the range of the end point of the diagonal channel. At this time, the robot first travels to the end point of the diagonal channel, turns, and then travels along the boundary of the picking station according to the Manhattan distance to the picking station. See Figure 6 , the robot can travel in 8 directions: east, south, west, north, northeast, southeast, southwest, and northwest. Define the distance function as:

[0111] This embodiment divides the distance traveled by the robot into four segments:

[0112] D 总距离 = C ij + 2 * C jp

[0113] a) The distance from the robot's position (X i , Y i ) to the arrival point (X j , Y j ) associated with the task:

[0114] C ij = d × (|X i - X j | + |Y i - Y j |) (1)

[0115] b) The distance from the arrival point (X j , Y j ) to the starting point of the diagonal (X st , Y st ) that is closest to the straight-line distance:

[0116] C j-st = d × (|X j - X st | + |Y j - Y st |) (2)

[0117] In this embodiment, the starting point of the diagonal is set as the first position point of the exit of the roadway. Taking this starting point as the starting point of the diagonal, after the robot determines to drive diagonally, the robot turns to the diagonal angle and starts to drive diagonally to the same column as the picking station. After reaching the same column as the picking station, the robot turns to the angle facing the picking opening and drives straight into the picking workstation. During the process of the robot reaching the picking workstation diagonally, it may occur that the position of the picking station exceeds the range of the end point of the diagonal passage. At this time, the robot first drives to the end point of the diagonal passage, turns, and then drives along the boundary of the picking station according to the Manhattan distance to the picking station.

[0118] c) The distance C st , Y st ) from the starting point of the diagonal (X p , Y p ) to the picking point (X st-p :

[0119] See Figure 7 , when the robot travels from the starting point of the diagonal to the picking station, it is necessary to judge the position of the picking station to determine the end point of the diagonal of the robot and the way to reach the picking station. As shown in the figure, there are generally two situations. The judgment rules and distance calculation formulas are as follows:

[0120] c.1) When the position of the picking station is within the diagonal passage range (X p ≤ X ed ):

[0121] X ed - X p = Δd

[0122] Let represent that the robot turns from this position and drives into the picking station. The distance formula is as follows:

[0123]

[0124] c.2) The picking station is located outside the range of the diagonal channel (X p > X ed ):

[0125]

[0126] After the robot finishes picking, it will return to the goods location along the same route.

[0127] (2) Model establishment

[0128] Based on the "goods-to-person" picking operation mode, a cost optimization model for multiple handling robots is constructed. The model considers two optimization objectives: the minimum total cost of multiple mobile robot operations and the balance of the operation costs of multiple mobile robots. Its objective function is shown in Equation (5). The smaller the value of F(π), the better the scheduling scheme for multiple mobile robots, where π is a scheduling scheme:

[0129] F(π) = min (w 1 f(π) + w 2 h(π)) (5)

[0130] Among them, f(π) represents the total task cost of all handling robots, and h(π) represents the task cost balance value; w 1 , w 2 represent the importance degrees of the two sub-objectives f(π) and h(π) on the total objective.

[0131] The total task cost f(π) of all handling robots, including the task itself cost and the task correlation cost, is expressed as follows:

[0132]

[0133] The two distances for the handling robot to carry the goods from the picking goods location to the picking position are:

[0134] C jp = C j-st + C st-p (7)

[0135] The expression of the task cost balance value h(π) is:

[0136]

[0137] w 1 、w2 Indicates the importance of the influence of two sub-goals f(π) and h(π) on the total goal. When w 1 <w 2 it indicates that task balance is the main optimization goal. When w 1 >w 2 it indicates that minimizing the total cost is the main optimization goal:

[0138]

[0139] The expression for the task cost of a single robot is:

[0140]

[0141] The average value of the total task cost is:

[0142]

[0143] The cost of the robot running empty should be less than the cost of the robot running with a load. The objective constraint is:

[0144] θ 1 <θ 2 (12)

[0145] The relationship between the battery consumption of the robot and the remaining mileage is:

[0146] L(x) = ax 2 bx + c (13)

[0147] In the process of scheduling multiple handling robots, the accessibility of the path in the forward direction of the picking workstation relative to the diagonal direction of the handling robot needs to be considered. Therefore, when judging whether to move diagonally, whether there is an obstacle in the forward direction is taken into account. It means that the diagonal constraint of the robot must be that there are no other robots in the two grids adjacent to the next destination position in the diagonal direction, to avoid collisions of the shelves caused by the robot turning. The diagonal constraint of the robot must be that there are no other robots around the diagonal:

[0148]

[0149] The walking mileage of each robot is within the battery capacity constraint, and there is a reserved safety battery power to return to its charging area:

[0150]

[0151] Because when the remaining battery power is the same, L 空载 >L 载货 , the battery power that can ensure the safe driving of the robot with a load can also ensure the safe driving of the robot running empty for the same distance. Therefore, only the remaining mileage in the loaded state is considered to meet the safety battery power requirements.

[0152] Equation (16) indicates that a task is executed by only one robot, and Equation (17) indicates that all tasks are assigned to robots:

[0153]

[0154] (3) Solving algorithm design

[0155] 3.1) Order task assignment rules

[0156] The picking orders issued by ERP are initiated based on customer requirements. The purchase orders placed by customers often contain different commodities, and the picked commodities have different specifications and quantity requirements. The task assignment process includes both the assignment of order groups and the assignment of individual picking tasks. When distributing orders in this paper, the order groups placed by the same customer are preferentially assigned to the same picking workstation. To ensure the balance of the assigned tasks and the convenience of goods collection, when a picking workstation exceeds its picking load, it is preferentially assigned to an adjacent picking station. When assigning individual picking tasks, different picking subtasks within the same order group need to be assigned to the AGV formation belonging to the picking station. When the distances are the same, it is preferentially assigned to the AGV with a higher battery level. When the distances are different and the remaining battery levels of all can complete the task, it is preferentially assigned to the AGV with a shorter path distance according to the path distance.

[0157] 3.2) Robot scheduling rules

[0158] To reasonably allocate scheduling resources and improve system efficiency, new handling robot scheduling rules are proposed to determine the allocation relationship between handling tasks and handling robots. According to the characteristics of the FJSP model in this embodiment, the handling robot scheduling rules are divided into two cases: single robot and multiple robots as Figure 5 shown: (1) In the case of a single handling robot, the handling robot immediately transports the next transportation task after completing the previous transportation task; (2) In the case of multiple handling robots, if the time node for each robot to complete the transportation task of the next process is greater than the time when the next process can start processing, then select the robot that can complete the transportation task earliest; if only one robot's time node for completing the transportation task of the next process is before the time when the next process can start processing, then select this robot; if there are multiple robots whose time nodes for completing the transportation tasks of the next process are before the time when the next process can start processing, then select the robot with the shortest running distance when completing the transportation task among them. The new robot scheduling rules consider the earliest arrival and the shortest distance of the robot according to different situations during the workpiece processing, so that the next task can obtain the earliest start time.

[0159] 3.3) Robot walking rules

[0160] Traditional robots move according to the Manhattan distance rule. The robot advances towards the destination in a straight-line four-way manner along the scheduled path. This method is beneficial for the robot to move along the preset channels in the storage area, reducing collisions between the robot and storage locations and facilities. However, in the robot's operation buffer area, this restriction can be lifted to allow the robot to move diagonally. As follows Figure 6 As shown, the robot can move diagonally by turning. The rule for diagonal movement is that in the storage area, the robot moves in a straight-line four-way manner according to the Manhattan distance. After entering the buffer area, the robot enters the starting point of diagonal movement, i.e., judges the robot's operating environment through the scheduling model, and combines diagonal movement and straight-line movement to reach the target work station.

[0161] Embodiment 2

[0162] The embodiment of the present invention further provides an electronic device, which includes a processor and a memory. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement a Figure 1 material handling robot cluster scheduling method as shown.

[0163] It can be understood that the memory may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function, instructions for implementing the above various method embodiments, etc.; the data storage area can store data created according to the use of the server, etc.

[0164] The processor may include one or more processing cores. The processor uses various interfaces and circuits to connect various parts within the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by invoking data stored in the memory, it performs various functions of the server and processes data. Optionally, the processor may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor may integrate a combination of one or more of a central processing unit (CPU) and a modem, etc. Among them, the CPU mainly processes the operating system, application programs, etc.; the modem is used for processing wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor and may be implemented separately by a single chip.

[0165] Since this electronic device is the electronic device corresponding to a method for scheduling a handling robot cluster in an embodiment of the present invention, and the principle of the electronic device for solving problems is similar to that of this method, the implementation of this electronic device can refer to the implementation process of the above method embodiment, and the repeated parts will not be elaborated.

[0166] Embodiment 3

[0167] The embodiment of the present invention further provides a computer-readable storage medium, in which at least one instruction, at least one segment of program, code set, or instruction set is stored, and the at least one instruction, the at least one segment of program, the code set, or the instruction set is loaded and executed by the processor to implement Figure 1 a method for scheduling a handling robot cluster as shown.

[0168] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable storage medium, which includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disc memories, magnetic tape memories, or any other computer-readable medium capable of carrying or storing data.

[0169] Since this storage medium is the storage medium corresponding to a method for scheduling a cluster of handling robots according to an embodiment of the present invention, and the principle of solving problems by this storage medium is similar to that of this method, the implementation of this storage medium can refer to the implementation process of the above method embodiment, and the repeated parts will not be described again.

[0170] Embodiment 4

[0171] In some possible implementation manners, each aspect of the method according to an embodiment of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a computer device, the program code is used to cause the computer device to execute the steps of a method for scheduling a cluster of handling robots according to various exemplary embodiments described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in a high-level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0172] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0173] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0174] The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, and it cannot be used to limit the protection scope of the present invention. All equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for dispatching a transport robot cluster, characterized in that: The following steps are involved: ERP sends the order group to the enterprise warehouse management system WMS to query the storage location information; The order group picking information is generated and sent to WCS, and the AGV group is dispatched based on the available AGV resources; Based on the cost optimization model of multiple handling robots, the two optimization objectives of minimizing the total cost of multiple handling robots and balancing the cost of multiple handling robots are considered, and the AGV handling path is solved based on the principle of optimal total utility. The AGV group executes the planned path and performs the transportation in two walking modes: Euclidean and Manhattan distance. Complete the transport task and update the transport task.

2. A method for dispatching a transport robot cluster according to claim 1, characterized in that: The warehouse includes storage area, buffer area and picking area. All areas are arranged in a grid. Multiple handling robots run in a straight line in the grid environment. A QR code is set at the center of each grid. The stepping distance of the handling robot is the straight-line distance between two adjacent QR codes, and the next driving and turning direction is determined by the QR code information.

3. A method for dispatching a transport robot cluster according to claim 1, characterized in that: The order group picking information is sent to WCS, and the AGV group is dispatched in combination with the available AGV resources, including: WCS divides the tasks into m groups based on the number of customers and the number of dispatchable AGVs. The number of shelves that need to be transported for picking work is n, which are assigned to m idle handling robots and transported to p picking stations. Each order group is completed by a picking station alone. When a single handling robot executes a task sequence, it is divided into two states: empty and loaded. The power consumption per unit distance traveled in the two states is different, and the power consumption is proportional to the robot's travel distance.

4. A method for dispatching a transport robot cluster according to claim 1, characterized in that: The cost optimization model based on multiple handling robots considers two optimization objectives: minimizing the total cost of multiple handling robots and balancing the cost of multiple handling robots. The AGV handling path is solved based on the total utility optimal principle, including: Construct a cost optimization model for multiple handling robots; The cost optimization model considers two optimization goals: minimizing the total cost of multiple mobile robots and balancing the cost of multiple handling robots. Its objective function is: F(π)=min(w1f(π)+w2h(π)) In the formula, f(π) represents the total task cost of all handling robots, h(π) represents the equilibrium value of the task cost; w1 and w2 represent the importance of the two sub-goals f(π) and h(π) on the total goal; the smaller the F(π) value, the better the multi-mobile robot scheduling scheme. The expression of the total task cost f(π) is: Where θ1 represents the task-associated cost coefficient, θ2 represents the task cost coefficient; i, j represents the shelf number, r represents the robot number, and p represents the picking station number; m represents the total number of robot numbers, n represents the total number of shelf numbers, and p represents the total number of picking stations; x rij The value 1 indicates that the mobile robot r moves the shelves from shelf i to shelf j, otherwise it is 0; y rjp The value 1 indicates that the mobile robot r moves the shelves from shelf i to picking station p, otherwise it is 0; C ij represents the task-associated distance from the robot position to the cargo point, C j-st Indicates the distance from the cargo point to the starting point of the closest oblique line, C st-p Indicates the distance the robot turns to drive into the picking station.

5. A method for dispatching a transport robot cluster according to claim 4, characterized in that: The task cost of a single transport robot is: In the formula, q r represents the number of tasks assigned to robot r, Indicates that the robot executes the qth r The distance required for the task to walk from shelf i to shelf j, Indicates that the robot executes the qth r The distance required for the task to walk from point j on the shelf to point p for picking; The relationship between the robot's battery consumption and remaining mileage is as follows: L(x)=ax 2 bx+c Where L(x) represents the function of AGV power consumption changing with time x, a, b, c represent the constants of the analytical expression of the power consumption function; Each robot's mileage is within the power constraints, and there is a safe amount of power reserved to return to its charging area: g(r)≤L(1-g(r-1))-L(1-v) In the formula, v is the percentage of safe power; When the remaining power is the same, L 空载 >L 载货 , to ensure that the cargo-carrying robot can travel safely, and to ensure that the unloaded robot can travel the same distance safely, so only the remaining mileage in the cargo state is considered to meet the safety power requirements.

6. A method for dispatching a transport robot cluster according to claim 1, characterized in that: The dispatching rules of handling robots are divided into two cases: single robot and multi-robot: 1) When the number of robots is a single transport robot, the transport robot will immediately carry out the next transport task after completing the previous transport task; 2) When there are multiple transport robots, if the time node for each robot to complete the transport task of the next process is greater than the time when the next process can start processing, the robot that can complete the transport task the earliest is selected; if only one robot completes the transport task of the next process before the time when the process can start processing, this robot is selected; if multiple robots complete the transport task of the next process before the time when the process can start picking, the robot with the shortest running distance to the picking location when completing the transport task is selected; The robot scheduling rules consider the earliest arrival and shortest distance of the robot according to different situations during the picking process, so that the next task can get the earliest start time.

7. A method for dispatching a transport robot cluster according to claim 1, characterized in that: The AGV group executes the planned path and performs the transportation in two walking modes: Euclidean and Manhattan distance, including: In the storage area, the handling robot walks in a straight-line four-way manner according to the Mann distance. After entering the buffer area, the handling robot enters the starting point of the oblique movement. The scheduling model is used to determine the operating environment of the handling robot. The handling robot changes its walking direction to a straight-line eight-way walking direction, and reaches the destination station by combining oblique and straight walking. The transport robot can travel in 8 directions and the distance function is defined as: D 总距离 =C ij +2*(C j-st +U st-p ) In the formula, C ij represents the task-associated distance from the robot position to the cargo point, C j-st Indicates the distance from the cargo point to the starting point of the closest oblique line, U st-p It indicates the distance that the AGV travels from the starting point of executing the Euclidean distance to the destination picking point; 1) By the robot position (X i ,Y i ) Arrival point (X j ,Y j )Task association distance: C ij =d×(|X i -X j |+|And i -AND j |) Where d is the straight-line distance between the adjacent points on two sides; 2) By cargo point (X j ,Y j ) to the nearest oblique starting point (X st ,Y st ) distance: C j-st =d×(|X j -X st |+|And j -AND st |) 3) From the starting point of the oblique line (X st ,Y st ) to the picking point (X p ,Y p ) distance: 3.1) When the picking station is located within the inclined channel (X p ≤X ed ): X ed -X p =Δd make It means that the robot turns from this position to the picking station. The distance formula is as follows: 3.2) The picking station is located outside the inclined channel (X p >X ed ): After the robot completes the picking, it will return to the cargo location along the same route.

8. A method for dispatching a transport robot cluster according to claim 1, characterized in that: In the process of judging whether to move diagonally, whether there are obstacles in the forward direction is taken into consideration. That is, the robot's diagonal movement constraint must be that there are no other robots in the two grids adjacent to the next destination position in the diagonal direction to avoid collision with shelves due to robot turning.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 8.

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