CN115186878A - Multi-AGV online task allocation method and system - Google Patents

Multi-AGV online task allocation method and system Download PDF

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CN115186878A
CN115186878A CN202210716146.0A CN202210716146A CN115186878A CN 115186878 A CN115186878 A CN 115186878A CN 202210716146 A CN202210716146 A CN 202210716146A CN 115186878 A CN115186878 A CN 115186878A
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CN115186878B (en
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王晓伟
吴嘉璇
吴松屿
秦兆博
谢国涛
秦洪懋
边有钢
胡满江
秦晓辉
徐彪
丁荣军
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Wuxi Institute Of Intelligent Control Hunan University
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
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Abstract

The invention discloses a method and a system for distributing multiple AGV online tasks, wherein the method comprises the following steps: step 1, sequencing the priority of each task according to the arrival time of each task in a task pool; step 2, selecting the first N tasks with the priority level in the task pool as the tasks to be allocated according to a preset auction period, conducting auction in sequence, and sending related information of the tasks to be allocated to each AGV; step 3, optimizing the task execution sequence by the AGV; the tasks comprise tasks which are not completed currently and received new tasks; step 4, the AGV judges whether to participate in bidding according to the self state, and if the judgment result is yes, a bidding value is calculated; step 5, distributing the tasks to the AGV with the maximum bidding value and returning to the step 3; and 6, repeatedly executing the steps 2-5 until the number of the tasks in the task pool is 0.

Description

Multi-AGV online task allocation method and system
Technical Field
The invention relates to the technical field of AGV (automatic Guided Vehicle) task scheduling, in particular to a multi-AGV online task allocation method and a multi-AGV online task allocation system.
Background
In warehouse logistics, automatic Guided Vehicles (AGVs) are often used for transporting goods, the number of transporting tasks is usually not fixed, the transporting tasks are dynamically generated, randomness is provided, the task density is high, and the requirement on the timeliness of the tasks is high. The reasonable task allocation strategy is set, so that the time for the AGV to complete the total task can be effectively shortened, the response speed of the AGV to the task is increased, and the overall efficiency of the system is improved.
An existing scheduling system usually adopts a centralized task allocation scheme, the number of tasks is usually fixed, a scene where the tasks arrive dynamically cannot be responded quickly, and the calculation complexity is higher as the number of the tasks increases. Secondly, the optimization objective is considered to be single, multi-objective optimization is rarely studied, uneven task load occurs, the number of individual AGV tasks is too large or too small, and time consumption for all AGV tasks to complete is increased. Moreover, the scheduling system does not consider the influence of the execution sequence of the tasks on the task efficiency of the AGV in the task scheduling process.
Disclosure of Invention
It is an object of the present invention to provide a multiple AGV online task allocation method and system that overcomes or at least alleviates at least one of the above-mentioned deficiencies of the prior art.
In order to achieve the above object, the present invention provides an online task allocation method for multiple AGVs, which includes:
step 1, sorting the priority of each task according to the arrival time of each task in a task pool;
step 2, selecting the tasks with the priority levels in the first N in the task pool as tasks to be allocated according to a preset auction period, carrying out auction in sequence, and sending related information of the tasks to be allocated to each AGV;
step 3, optimizing the task execution sequence by the AGV; the tasks comprise tasks which are not completed currently and received new tasks;
step 4, the AGV judges whether to participate in bidding according to the state of the AGV, and if the judgment result is yes, a bidding value is calculated;
step 5, distributing the tasks to the AGV with the maximum bidding value and returning to the step 3;
and 6, repeating the execution steps 2-5 until the number of the tasks in the task pool is 0.
Further, the method for sequencing the unallocated tasks in the task pool in step 1 includes:
calculating the priorities of the unallocated tasks in the task pool according to the following formula, and then sequentially sorting the tasks from small to large according to the sizes of the priorities:
H i ＝(t cur -t i )*P i
in the formula, H i Indicating the priority of the task numbered i, t cur Representing the current time, t i Indicates the time of occurrence of the task with number i, P i Indicating the importance of the task numbered i.
Further, the step 2 further comprises: and if the number of the tasks which are not distributed in the task pool is less than N, selecting all the tasks which are not distributed in the task pool to carry out auction in sequence.
Further, the method for optimizing the task execution sequence by the AGV in step 3 specifically includes:
step 31, acquiring position information and current electric quantity information of the AGV and an unfinished task set of the AGV per se;
and step 32, optimizing the task execution sequence of each AGV by adopting a particle swarm algorithm, so that the path cost and the energy consumption cost of the AGV completing the tasks according to the new task sequence are the lowest.
Further, the particle swarm algorithm specifically includes:
step 321, generating an initial particle swarm by a greedy algorithm;
322, generating a new particle swarm through cross variation;
step 323, calculating the fitness of all individuals in the new particle swarm in step 322 by using the following formula, judging whether the fitness of the individual with the minimum fitness in the particle swarm is smaller than the fitness of the optimal individual in all the particle swarm, if so, updating the optimal individual in all the particle swarm to be the individual, adding 1 to the iteration time T of one cycle, and in the next cycle, using the optimal individual to generate a new individual by crossing with other individuals, and when the cycle time T is greater than the maximum allowable iteration time T max If so, ending the iteration, wherein the optimal individuals in all the particle swarm currently are the optimal task execution sequence in the current task set, and obtaining the optimal fitness corresponding to the optimal task execution sequence;
Figure BDA0003708840140000021
in the formula, the fitness represents the fitness of the AGV with the number k to complete all tasks,
Figure BDA0003708840140000022
indicating an incomplete task set T at AGV number k k Middle task k j Distance to be traveled, R (k) j ，k j+1 ) Representing a task k j And task k j+1 To correlate the distance therebetween.
Further, the method for calculating the bidding value of the AGVs participating in bidding in step 4 specifically includes:
step 41, calculating the path cost of the AGV for receiving the new task by subtracting the distance to be traveled by all the uncompleted tasks completed by the AGV according to the distance to be traveled by the AGV for completing all the uncompleted tasks and the new task;
step 42, calculating the energy consumption cost of the AGV for receiving the new tasks according to the fact that the residual electric quantity after the AGV completes all the uncompleted tasks and the new tasks is subtracted from the residual electric quantity after the completion of all the uncompleted tasks;
and 43, calculating the bidding value Q of the AGV with the number k according to the following formula according to the reciprocal of the weighted sum of the path cost and the energy consumption cost k β is a weighted value of two costs, 0 < β < 1:
Figure BDA0003708840140000031
in the formula, L k ' indicating that the AGV with the number of k completes the estimated total path of all tasks according to the optimal task execution sequence after adding a new task, L k Representing that the AGV with the number of k finishes the estimated total path, SOC, of all the tasks according to the optimal task execution sequence ave Indicating the amount of power consumed by the AGV per unit length of travel.
Further, in step 4, whether the AGV participates in the bidding is determined according to one of the following two methods:
the method comprises the following steps: judging whether the number of tasks in the unfinished task set of the AGV with the number k is larger than the average number of unfinished tasks multiplied by a proportionality coefficient alpha or not, if so, not participating in bidding; otherwise, participating in bidding;
the second method comprises the following steps: estimation residual capacity of AGV with judgment number of k
Figure BDA0003708840140000032
Whether or not it is lower than threshold SOC 0 If yes, the AGV with the number k does not participate in bidding; otherwise, participating in bidding.
The invention also provides a multi-AGV online task allocation system, which comprises:
the task pool unit is used for sequencing the priority of each task according to the arrival time of each task in the task pool and marking the distributed tasks as distributed tasks;
the scheduling unit is used for selecting the tasks with the priority levels positioned in the first N in the task pool as tasks to be allocated according to a preset auction period, carrying out sequential auctions and sending related information of the tasks to be allocated to each AGV;
the scheduling unit is used for allocating tasks to the AGV with the maximum corresponding bidding value and informing the AGV equipment units of the tasks, and the selected AGV equipment units update the task sets of the AGV equipment units; the tasks comprise tasks which are not completed currently and received new tasks.
Further, the method for optimizing the task execution sequence by the AGV equipment unit specifically includes:
step 31, acquiring position information and current electric quantity information of the AGV and an unfinished task set of the AGV per se;
step 32, optimizing the task execution sequence of each AGV by using a particle swarm algorithm, so that the AGVs complete the path cost and the energy consumption cost of the tasks according to the new task sequence are the lowest, wherein the particle swarm algorithm specifically comprises:
step 321, generating an initial particle swarm by a greedy algorithm;
322, generating a new particle swarm through cross variation;
step 323, calculating the fitness of all the individuals in the new particle swarm of step 322 by using the following formula, judging whether the fitness of the individual with the minimum fitness in the particle swarm is smaller than the fitness of the optimal individual in all the particle swarm, if so, updating the optimal individual in all the particle swarm to be the individual, and circulating onceAdding 1 to the iteration times T, and generating a new individual by using the optimal individual and other individuals in a next cycle in a crossed manner, wherein when the cycle times T are greater than the maximum allowable iteration times T max If so, ending the iteration, wherein the optimal individuals in all the particle swarm currently are the optimal task execution sequence in the current task set, and obtaining the optimal fitness corresponding to the optimal task execution sequence;
Figure BDA0003708840140000041
in the formula, the fitness represents the fitness of the AGV with the number k to complete all tasks,
Figure BDA0003708840140000042
indicating an incomplete task set T at AGV number k k Middle task k j Distance to be traveled, R (k) j ，k j + 1 ) Representing a task k j And task k j+1 Associated with a distance therebetween.
Further, the method for calculating a bidding value of an AGV participating in bidding in step 4 specifically includes:
step 41, calculating the path cost of the AGV for receiving the new task by subtracting the distance to be traveled by all the uncompleted tasks completed by the AGV according to the distance to be traveled by the AGV for completing all the uncompleted tasks and the new task;
step 42, calculating the energy consumption cost of the AGV for receiving the new tasks according to the fact that the residual electric quantity after the AGV completes all the uncompleted tasks and the new tasks is subtracted from the residual electric quantity after the completion of all the uncompleted tasks;
and 43, calculating the bidding value Q of the AGV with the number k according to the following formula according to the reciprocal of the weighted sum of the path cost and the energy consumption cost k β is a weighted value of two costs, 0 < β < 1:
Figure BDA0003708840140000043
in the formula, L k ' indicates that AGV with number k is joining a New renPost-transaction predicted total path L for completing all tasks according to optimal task execution sequence k Representing that the AGV with the number of k finishes the estimated total path, SOC, of all the tasks according to the optimal task execution sequence ave Indicating the amount of power consumed by the AGV per unit length of travel.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the method is suitable for various scenes such as storage and logistics, the scheduling system can quickly respond to the tasks, the tasks are distributed to the proper AGVs, the total path cost of the AGVs for executing the tasks is reduced, and the overall benefit of the system is improved.
2. The invention constructs a task pool to buffer tasks aiming at the scenes that the tasks are dynamically generated and the task quantity is unpredictable, and sequences according to the time and the importance degree of the task generation, so that the tasks with high comprehensive priority are distributed and executed firstly.
3. According to the multi-AGV task allocation strategy provided by the invention, the task load balance of each AGV is considered during task allocation, the situations that the number of individual AGV tasks is too large and the idle time of the individual AGV is too long are avoided, all AGV equipment resources are fully utilized, and the task number of each AGV is averaged.
4. The distributed task allocation algorithm is different from a centralized task allocation algorithm, the calculation complexity is low, each AGV only needs to calculate the bidding value of the AGV and send the bidding value to the scheduling system during task allocation, and the scheduling system only needs to select the AGV with the optimal bidding value and allocate the task to the optimal AGV for processing, so that the task completing efficiency is improved, and the overall efficiency of the system is improved.
5. The AGV task execution sequence optimization algorithm provided by the invention can dynamically adjust the AGV task execution sequence and reduce the total path cost for the AGV to complete all tasks.
Drawings
Fig. 1 is a schematic diagram of a main flow of task allocation according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating a task sequencing optimization process according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a multiple AGV dispatching system according to an embodiment of the present invention.
Detailed Description
In the drawings, the same or similar reference numerals are used to designate the same or similar elements or elements having the same or similar functions. Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
In the description of the present invention, the terms "central", "longitudinal", "lateral", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore, should not be construed as limiting the scope of the present invention.
In the present invention, the technical features of the embodiments and implementations may be combined with each other without conflict, and the present invention is not limited to the embodiments or implementations in which the technical features are located.
The present invention will be further described with reference to the accompanying drawings and specific embodiments, it should be noted that the technical solutions and design principles of the present invention are described in detail in the following only by way of an optimized technical solution, but the scope of the present invention is not limited thereto.
The following terms are referred to herein, and their meanings are explained below for ease of understanding. It will be understood by those skilled in the art that the following terms may have other names, but any other names should be considered consistent with the terms set forth herein without departing from their meaning.
As shown in fig. 1, the method for allocating multiple AGVs on-line tasks according to the embodiment of the present invention includes:
step 1, sequencing the priority of each task according to the arrival time of each task in a task pool. The task pool is constructed in advance and used for dynamically storing task information which arrives in real time, recording a starting point, an end point, generation time and an importance degree of a task, and carrying out priority sequencing on the task.
In one embodiment, the prioritization of tasks is generally based on task arrival time and priority, which includes:
step 1.1, the task pool records the dynamically generated task information. The related information of the task mainly comprises a task starting point, a task ending point, task generation time, task importance degree and S i Indicating the task related information numbered i.
S i ＝(x i ，y i ，x i ′，y i ′，t i ，P i ) (1)
Wherein (x) i 、y i ) Two-dimensional coordinates (x) representing the start of the task with number i i ′、y i ') denotes the two-dimensional coordinates of the end point of the task numbered i, t i Indicates the time of occurrence of the task numbered i, P i Indicating the degree of importance of the preset task numbered i, P i The value range of (A) is set between 1 and 2, the higher the importance degree is, the higher P i The larger the value.
And 1.2, sequencing the unallocated tasks in the task pool. Obtaining the current time t cur And calculating the priority of all tasks.
H i ＝(t cur -t i )*P i (2)
In the formula, H i Indicating the priority of the task numbered i. And then sorting according to the priority of the tasks, wherein the tasks with large priority values are arranged in the front.
The scheduling method and the scheduling system are suitable for various scenes such as logistics and storage, the scheduling system can quickly respond to the tasks, the tasks are distributed to the proper AGVs, the total path cost and the total energy consumption cost of the AGVs for executing the tasks are reduced, and the total benefit of the system is improved. In the logistics and warehousing scenes, tasks to be allocated are dynamically generated and have unpredictability, and the number of the tasks is not fixed, so that the system constructs a task pool for dynamically storing the tasks, prevents the number of the tasks from being too large or too small, and performs sequencing according to the task generation time and the importance degree, so that the tasks with high priority are allocated and executed firstly. Of course, the prioritization may be performed in other manners as desired.
And 2, selecting the first N tasks with the priority levels in the task pool as the tasks to be distributed according to a preset auction period, carrying out auction in sequence, and sending the relevant information of the tasks to be distributed to each AGV.
Wherein, the auction period T is preset fix This may be, but is not limited to, based on historical data settings, such as: if M transport tasks are generated in the historical data on average per hour, the auction period can be set to be less than N/M hours, and T is set fix It can be set to N/2M hours. The value of N is a positive integer. The task related information comprises a task starting point, a task ending point, task generation time and task importance degree.
Step 2 also includes a special case: and if the number of the tasks which are not distributed in the task pool is less than N, selecting all the tasks which are not distributed in the task pool to carry out auction in sequence.
And 3, optimizing the task execution sequence by the AGV. The tasks comprise tasks which are not completed currently and received new tasks.
In an embodiment, the method for optimizing the task execution sequence by the AGV in step 3 specifically includes:
and step 31, acquiring position information and current electric quantity information of the AGV and an unfinished task set of the AGV.
Figure BDA0003708840140000071
And expressing the position two-dimensional coordinates of the AGV with the number of k, wherein the value range of k is 1-M, and M expresses the total number of the AGV.
Figure BDA0003708840140000072
Indicating the current power of AGV numbered k. T is k Indicating that the AGV with number k does not itself complete the set of tasks,
Figure BDA0003708840140000073
for task set T k Task of (1), k j The task is numbered in the task pool. The order of arrangement of the tasks in the task set is the execution order of the tasks. J. the design is a square k Indicating the number of tasks in the task set that the AGV with number k itself has not completed.
Figure BDA0003708840140000074
And step 32, optimizing the task execution sequence of each AGV by adopting a particle swarm algorithm, so that the path cost and the energy consumption cost of the AGV completing the tasks according to the new task sequence are the lowest. The AGV completes tasks according to the sequence of the tasks in the task set, and the sequence of the tasks in the task set is assumed to be as shown in a formula (3). The AGV completes the estimated total path L of all tasks k Equal to the distance travelled by a single task
Figure BDA0003708840140000075
Adding the associated distance R (k) from one task end point to the next task start point j ，k j+1 ). And (4) estimating the total distance, namely subsequently evaluating the individual fitness of the particle swarm. Distance that AGV needs to travel to complete a single task
Figure BDA0003708840140000076
And the distance R (k) associated with the AGV transitioning from the last task end to the next task start j ，k j+1 ). Manhattan distance estimation is adopted. The starting point of the task is (x) j ，y j ) End point is (x) j ′，y j ′)。(x j+1 ，y j+1 ) Is the starting point of the next task.
In another embodiment, the euclidean distance may be used or the path planning algorithm may be used to find the distance instead of the manhattan distance in the above embodiment.
In one embodiment, the particle swarm algorithm specifically includes:
step 321, generating an initial particle swarm by a greedy algorithm: assuming that the task set of the current AGV has N tasks, firstly selecting a task i as a starting point, then selecting the task with the shortest Manhattan distance from the end point of the task i in the rest tasks, and so on, and selecting the task with the shortest distance from the end point of the last task each time. And traversing the process for N times, selecting different tasks as starting points each time, and recording the path cost and the task execution sequence which are calculated by taking the different tasks as the starting points. The number of cycles T is set to 1. And the individual fitness function fitne s of the particle swarm is the path cost for completing all tasks. And comparing the N task execution sequence schemes, and selecting the individual with the minimum fitness as the optimal individual of the particle swarm and the optimal individual in all the particle swarms.
Step 322, generating a new particle population by cross mutation: crossover is the selection of different individuals in a population of individuals for different sequences, and mutation refers to the random adjustment of the sequences of the individuals in the population. Firstly, selecting the optimal individual in all the particle swarms, and randomly exchanging different sequences with other individuals to generate a new individual. And (3) carrying out mutation operation on part of individuals, and exchanging sequences, wherein the sequence of individual exchange means the execution sequence of exchange tasks.
Step 323, calculating the fitness of all individuals in the new particle swarm in step 322 by using the following formula, judging whether the fitness of the individual with the minimum fitness in the particle swarm is smaller than the fitness of the optimal individual in all the particle swarm, if so, updating the optimal individual in all the particle swarm to be the individual, adding 1 to the iteration time T of one cycle, and in the next cycle, using the optimal individual to generate a new individual by crossing with other individuals, and when the cycle time T is greater than the maximum allowable iteration time T max If so, ending the iteration, wherein the optimal individuals in all the particle swarm currently are the optimal task execution sequence in the current task set, and obtaining the optimal fitness corresponding to the optimal task execution sequence;
Figure BDA0003708840140000081
Figure BDA0003708840140000082
R(k j ，k j+1 )＝|x j ′-x j+1 |+|y j ′-y j+1 | (6)
in the formula, the fitness represents the fitness of the AGV with the number k to complete all tasks,
Figure BDA0003708840140000083
indicating an incomplete task set T at AGV number k k Middle task k j Distance to be traveled, R (k) j ，k j+1 ) Representing a task k j And task k j+1 To correlate the distance therebetween. Maximum number of allowed iterations T max Typically greater than 500.
Let T be the set of original uncompleted tasks for the AGV with number k k The task set after adding the new task in the original task set is T k ′。
Calculating all AGV original task sets T by using the particle swarm task optimization sequencing algorithm k The optimal task execution sequence and the optimal fitness are the estimated total path L for completing all tasks according to the optimal task execution sequence k Calculating the task set T after adding the new task by using the method k ' the optimal task execution sequence and the estimated total path L for completing all tasks according to the sequence k ′。
According to the method for optimizing the task execution sequence of the AGV task set in the embodiment, the execution sequence of the tasks distributed by the AGV is optimized by adopting the improved particle swarm algorithm, so that the total path cost and the total energy consumption cost for the AGV to finish all the tasks can be effectively reduced.
And 4, judging whether to participate in bidding or not by the AGV according to the state of the AGV, and if so, calculating a bidding value.
In one embodiment, step 4 may determine whether the AGV is participating in a bid as follows:
the method comprises the following steps: judging whether the number of tasks in the unfinished task set of the AGV with the number k is larger than the average number of unfinished tasks multiplied by a proportionality coefficient alpha or not, if so, not participating in bidding; otherwise, participating in bidding.
In this embodiment, the AGV rootAccording to the state of the AGV, the residual power is estimated after the AGV finishes the existing unfinished tasks and the new tasks, whether the AGV participates in bidding is further evaluated, if the number of the unfinished tasks of the current AGV is larger than the average number of the unfinished tasks multiplied by a proportionality coefficient alpha, the alpha value range is set to be 1-1.5, the alpha value is smaller, the numerical value that the number of the tasks in the unfinished task set of the AGV per se is allowed to exceed the average number of the unfinished tasks is smaller, if the formula (8) is met, the AGV does not participate in bidding, and the bidding value is set to be 0. That is, determining whether an AGV is participating in a bidding requires calculating the average number J of tasks in the set of tasks that all AGVs have not completed ave As the basis:
Figure BDA0003708840140000091
J k ＞J ave *α (8)
in another embodiment, step 4 may determine whether the AGV is participating in a bid as follows:
the second method comprises the following steps: estimation residual capacity of AGV with judgment number of k
Figure BDA0003708840140000092
Whether or not it is lower than threshold SOC 0 If yes, the AGV with the number k does not participate in bidding as shown in the following formula (9); otherwise, participating in bidding.
Figure BDA0003708840140000093
In the formula (I), the compound is shown in the specification,
Figure BDA0003708840140000094
the AGV with the number k completes the task set T of the incomplete task and the new task k The estimated remaining capacity thereafter. Estimate the remaining capacity
Figure BDA0003708840140000095
Is equal to the current electric quantity
Figure BDA0003708840140000096
Subtract the complete task set T k ' predicted total path L of all tasks in k ' multiplying the amount of power consumed by AGV travel per unit length SOC ave . That is, the threshold value SOC is set 0 If the number is k, the estimated residual capacity of the AGV
Figure BDA0003708840140000097
Below threshold SOC 0 Then the AGV does not participate in bidding, and the bidding value is set to 0.
And each AGV calculates a bidding value according to the current unfinished task information, the new task information and the current electricity quantity, and sends the bidding value to the scheduling system. The new task is an auctioned task. Wherein, the electric quantity threshold value is generally set to be 20% -40% of the total electric quantity.
The method for calculating the bidding value of the AGVs participating in bidding in step 4 specifically includes:
step 41, calculating the path cost of the AGV for receiving the new task by subtracting the distance to be traveled by all the uncompleted tasks completed by the AGV according to the distance to be traveled by the AGV for completing all the uncompleted tasks and the new task;
step 42, calculating the energy consumption cost of the AGV for receiving the new task according to the fact that the residual electric quantity after the AGV completes all the unfinished tasks and the new task is subtracted from the residual electric quantity after the AGV completes all the unfinished tasks;
and 43, calculating the bidding value Q of the AGV with the number k according to the following formula according to the reciprocal of the weighted sum of the path cost and the energy consumption cost k Beta is weighted value of two costs, beta is more than 0 and less than 1, after each AGV calculates the bidding value, the bidding information is sent to the dispatching center:
Figure BDA0003708840140000101
in the formula, L k ' indicating that the AGV with the number k completes the estimated total path of all the tasks according to the optimal task execution sequence after adding a new task, L k Number of the displayK AGV completes the estimated total path, SOC, of all tasks according to the optimal task execution sequence ave Indicating the amount of power consumed by the AGV per unit length of travel.
And 5, distributing the task to the AGV with the maximum bidding value, returning to the step 3, and marking the task as distributed in a task pool. The multi-AGV task allocation strategy provided by the invention considers the task load balance of each AGV during task allocation, avoids the occurrence of the condition that the number of individual AGV tasks is too large and the idle time of the individual AGV is too long, fully utilizes all AGV equipment resources, and averages the number of tasks of each AGV. And secondly, the bidding values of all the AGVs are comprehensively considered during task allocation, so that the tasks can be allocated to the optimal AGVs for processing, the execution efficiency of the completed tasks is improved, and the overall efficiency of the system is improved.
And 6, repeating the execution steps 2-5 until the number of the tasks in the task pool is 0.
As shown in fig. 3, an embodiment of the present invention further provides a multi-AGV online task allocation system, which includes a task pool unit, a scheduling unit, and an AGV equipment unit, wherein:
on the other hand, the invention also provides a dispatching system for distributing the tasks of the multiple AGVs. The system comprises a task pool unit, a scheduling unit and an AGV equipment unit.
And the task pool unit is used for sequencing the priority of each task according to the arrival time of each task in the task pool. Further, the task pool unit is responsible for dynamically storing the task information which arrives in real time, recording the starting point, the end point, the generation time and the importance degree of the task, and carrying out priority sequencing on the task.
And the scheduling unit is used for selecting the tasks with the priority levels positioned in the first N in the task pool as the tasks to be allocated according to a preset auction period and carrying out auction in sequence. The scheduling unit is also used for issuing task information and sending the related information of the tasks to be distributed to each AGV equipment unit through the communication unit. The communication unit can be in wifi, 4G, 5G or other wireless data transmission communication modes. Of course, the communication unit may be built in the scheduling unit, and the AGV equipment units in the scheduling unit transmit the tasks to be allocated.
And the AGV equipment unit is used for receiving the task information transmitted by the scheduling unit, judging whether to participate in bidding according to the self state, if so, calculating a bidding value, and feeding the bidding value back to the scheduling unit through the communication unit.
The scheduling unit distributes the tasks to the AGV with the maximum bidding value, the selected AGV is informed of the equipment through the communication unit, and the selected AGV updates the task set of the AGV. The task pool element marks the task as allocated.
In one embodiment, the method for optimizing the task execution sequence by AGV equipment units specifically includes:
step 31, acquiring position information and current electric quantity information of the AGV and an unfinished task set of the AGV per se;
step 32, optimizing the task execution sequence of each AGV by using a particle swarm algorithm, so that the AGVs complete the path cost and the energy consumption cost of the tasks according to the new task sequence are the lowest, as shown in fig. 2, the particle swarm algorithm specifically includes:
at step 321, an initial particle swarm is generated by a greedy algorithm.
Step 322, generating new particle populations by cross mutation.
Step 323, calculating the fitness of all the individuals in the new particle swarm of step 322 by using the following formula, judging whether the fitness of the individual with the minimum fitness in the particle swarm is smaller than the fitness of the optimal individual in all the particle swarm, if so, updating the optimal individual in all the particle swarm to be the individual, adding 1 to the iteration time T of one cycle, and generating a new individual by using the optimal individual and other individuals in a crossed manner when the next cycle is carried out, wherein the cycle time T is greater than the maximum allowable iteration time T max If so, ending the iteration, wherein the optimal individuals in all the current particle swarms are the optimal task execution sequence in the current task set, and obtaining the optimal fitness corresponding to the optimal task execution sequence;
Figure BDA0003708840140000111
in the formula, the fitness represents the fitness of the AGV with the number k to complete all tasks,
Figure BDA0003708840140000112
indicating an incomplete task set T at AGV number k k Middle task k j Distance to be traveled, R (k) j ，k j+1 ) Representing a task k j And task k j+1 Associated with a distance therebetween.
In one embodiment, the method for calculating a bidding value of an AGV participating in bidding by an AGV equipment unit specifically includes:
step 41, calculating the path cost of the AGV for receiving the new task by subtracting the distance to be traveled by all the uncompleted tasks completed by the AGV according to the distance to be traveled by the AGV for completing all the uncompleted tasks and the new task;
step 42, calculating the energy consumption cost of the AGV for receiving the new tasks according to the fact that the residual electric quantity after the AGV completes all the uncompleted tasks and the new tasks is subtracted from the residual electric quantity after the completion of all the uncompleted tasks;
and 43, calculating the bidding value Q of the AGV with the number k according to the following formula according to the reciprocal of the weighted sum of the path cost and the energy consumption cost k β is a weighted value of two costs, 0 < β < 1:
Figure BDA0003708840140000121
in the formula, L k ' indicating that the AGV with the number k completes the estimated total path of all the tasks according to the optimal task execution sequence after adding a new task, L k Representing that the AGV with the number k finishes the estimated total path, SOC, of all the tasks according to the optimal task execution sequence ave Indicating the amount of power consumed by the AGV per unit length of travel.
Finally, it should be pointed out that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it. Those of ordinary skill in the art will understand that: modifications can be made to the technical solutions described in the foregoing embodiments, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A multi-AGV online task allocation method is characterized by comprising the following steps:
step 1, sorting the priority of each task according to the arrival time of each task in a task pool;
step 2, selecting the tasks with the priority levels in the first N in the task pool as tasks to be allocated according to a preset auction period, carrying out auction in sequence, and sending related information of the tasks to be allocated to each AGV;
step 3, optimizing the task execution sequence by the AGV; the tasks comprise tasks which are not completed currently and received new tasks;
step 4, the AGV judges whether to participate in bidding according to the state of the AGV, and if the judgment result is yes, a bidding value is calculated;
step 5, distributing the tasks to the AGV with the maximum bidding value and returning to the step 3;
and 6, repeating the execution steps 2-5 until the number of the tasks in the task pool is 0.
2. The method for multiple AGVs on-line task allocation according to claim 1, wherein the step 1 of sorting the unallocated tasks in the task pool includes:
calculating the priority of the unallocated tasks in the task pool according to the following formula, and then sequentially sorting the unallocated tasks from small to large according to the priority:
H i ＝(t cur -t i )*P i
in the formula, H i Indicating the priority of the task numbered i, t cur Representing the current time, t i Indicating the time at which the task numbered i was generated，P i Indicating the importance of the task numbered i.
3. The method for on-line task allocation for multiple AGVs according to claim 1, wherein said step 2 further comprises: and if the number of the tasks which are not distributed in the task pool is less than N, selecting all the tasks which are not distributed in the task pool to carry out auction in sequence.
4. The method according to claim 1, wherein said step 3, in which the AGVs optimize the task execution sequence, specifically includes:
step 31, acquiring position information and current electric quantity information of the AGV and unfinished task sets of the AGV per se;
and step 32, optimizing the task execution sequence of each AGV by adopting a particle swarm algorithm, so that the path cost and the energy consumption cost of the AGV completing the tasks according to the new task sequence are the lowest.
5. The method for online task allocation for multiple AGVs according to claim 4, wherein the particle swarm algorithm specifically comprises:
step 321, generating an initial particle swarm by a greedy algorithm;
322, generating a new particle swarm through cross variation;
step 323, calculating the fitness of all the individuals in the new particle swarm of step 322 by using the following formula, judging whether the fitness of the individual with the minimum fitness in the particle swarm is smaller than the fitness of the optimal individual in all the particle swarm, if so, updating the optimal individual in all the particle swarm to be the individual, adding 1 to the iteration time T of one cycle, and generating a new individual by using the optimal individual and other individuals in a crossed manner when the next cycle is carried out, wherein the cycle time T is greater than the maximum allowable iteration time T max If so, ending the iteration, wherein the optimal individuals in all the particle swarm currently are the optimal task execution sequence in the current task set, and obtaining the optimal fitness corresponding to the optimal task execution sequence;
Figure FDA0003708840130000021
in the formula, the fitness represents the fitness of the AGV with the number k to complete all tasks,
Figure FDA0003708840130000022
indicating an incomplete task set T at AGV number k k Middle task k j Distance to be traveled, R (k) j ，k j+1 ) Representing a task k j And task k j+1 Associated with a distance therebetween.
6. The method for allocating on-line tasks of multiple AGVs according to any of claims 1-5, wherein said method for calculating bid values of AGVs participating in bidding in step 4 specifically comprises:
step 41, calculating the path cost of the AGV for receiving the new task by subtracting the distance to be traveled by all the uncompleted tasks completed by the AGV according to the distance to be traveled by the AGV for completing all the uncompleted tasks and the new task;
step 42, calculating the energy consumption cost of the AGV for receiving the new tasks according to the fact that the residual electric quantity after the AGV completes all the uncompleted tasks and the new tasks is subtracted from the residual electric quantity after the completion of all the uncompleted tasks;
and 43, calculating a bidding value Q of the AGV with the number of k according to the following formula according to the reciprocal of the weighted sum of the path cost and the energy consumption cost k β is a weighted value of two costs, 0 < β < 1:
Figure FDA0003708840130000023
in the formula, L k ' indicating that the AGV with the number k completes the estimated total path of all the tasks according to the optimal task execution sequence after adding a new task, L k Representing that the AGV with the number of k finishes the estimated total path, SOC, of all the tasks according to the optimal task execution sequence ave Indicating the amount of power consumed by the AGV per unit length of travel.
7. The method of claim 6, wherein in step 4, whether AGVs participate in bidding is determined according to one of the following two methods:
the method comprises the following steps: judging whether the number of tasks in the unfinished task set of the AGV with the number k is larger than the average number of unfinished tasks multiplied by a proportionality coefficient alpha or not, if so, not participating in bidding; otherwise, participating in bidding;
the second method comprises the following steps: estimation residual capacity of AGV with judgment number of k
Figure FDA0003708840130000031
Whether the value is lower than the threshold value SOCo, if so, the AGV with the number k does not participate in bidding; otherwise, participating in bidding.
8. A multiple AGV online task distribution system, comprising:
the task pool unit is used for sequencing the priority of each task according to the arrival time of each task in the task pool and marking the distributed tasks as distributed;
the scheduling unit is used for selecting the tasks with the priority levels positioned in the first N in the task pool as tasks to be allocated according to a preset auction period, carrying out sequential auctions and sending related information of the tasks to be allocated to each AGV;
the dispatching unit is used for distributing tasks to the AGVs corresponding to the bidding values to the maximum extent and informing the AGV equipment units of the AGVs, and the selected AGV equipment units update task sets of the AGV; the tasks comprise tasks which are not completed currently and received new tasks.
9. The system of claim 8, wherein said method for AGV equipment unit to optimize task execution sequence includes:
step 31, acquiring position information and current electric quantity information of the AGV and an unfinished task set of the AGV per se;
step 32, optimizing the task execution sequence of each AGV by using a particle swarm algorithm, so that the AGVs complete the path cost and the energy consumption cost of the tasks according to the new task sequence are the lowest, wherein the particle swarm algorithm specifically comprises:
step 321, generating an initial particle swarm by a greedy algorithm;
step 322, generating a new particle swarm through cross mutation;
step 323, calculating the fitness of all the individuals in the new particle swarm of step 322 by using the following formula, judging whether the fitness of the individual with the minimum fitness in the particle swarm is smaller than the fitness of the optimal individual in all the particle swarm, if so, updating the optimal individual in all the particle swarm to be the individual, adding 1 to the iteration time T of one cycle, and generating a new individual by using the optimal individual and other individuals in a crossed manner when the next cycle is carried out, wherein the cycle time T is greater than the maximum allowable iteration time T max If so, ending the iteration, wherein the optimal individuals in all the current particle swarms are the optimal task execution sequence in the current task set, and obtaining the optimal fitness corresponding to the optimal task execution sequence;
Figure FDA0003708840130000041
in the formula, the fitness represents the fitness of the AGV with the number k to complete all tasks,
Figure FDA0003708840130000042
indicating an incomplete task set T at AGV number k k Middle task k j Distance to be traveled, R (k) j ，k j+1 ) Representing a task k j And task k j+1 Associated with a distance therebetween.
10. The system according to any of claims 8-9, wherein said method for calculating bidding values of AGVs participating in bidding in step 4 specifically comprises:
step 41, calculating the path cost of the AGV for receiving the new task by subtracting the distance to be traveled by all the uncompleted tasks completed by the AGV according to the distance to be traveled by the AGV for completing all the uncompleted tasks and the new task;
step 42, calculating the energy consumption cost of the AGV for receiving the new task according to the fact that the residual electric quantity after the AGV completes all the unfinished tasks and the new task is subtracted from the residual electric quantity after the AGV completes all the unfinished tasks;
and 43, calculating a bidding value Q of the AGV with the number of k according to the following formula according to the reciprocal of the weighted sum of the path cost and the energy consumption cost k β is a weighted value of two costs, 0 < β < 1:
Figure FDA0003708840130000043
in the formula, L k ' indicating that the AGV with the number k completes the estimated total path of all the tasks according to the optimal task execution sequence after adding a new task, L k Representing that the AGV with the number k finishes the estimated total path, SOC, of all the tasks according to the optimal task execution sequence ave Indicating the amount of power consumed by the AGV per unit length of travel.
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