CN-122022375-A - AGV cooperative scheduling method and system for multi-station joint tasks

CN122022375ACN 122022375 ACN122022375 ACN 122022375ACN-122022375-A

Abstract

The invention relates to the technical field of automatic guided vehicle dispatching, in particular to an AGV collaborative dispatching method and system for multi-station joint tasks. The method comprises the following steps of constructing a task relaxation time assessment model, assessing the urgency degree of a task by using the task relaxation time assessment model, estimating the energy consumption of executing the task, constructing a comprehensive optimization objective function according to the energy consumption, obtaining a task-AGV distribution result according to a basic constraint condition and the comprehensive optimization objective function, screening an optimal path by a cost function based on the task-AGV distribution result, and completing AGV cooperative scheduling by combining the urgency degree and the optimal path and through multi-AGV path conflict detection and time window-game coordination. The invention solves the problem of cooperative scheduling in the process of multi-station yarn breakage joint task automation in a textile workshop.

Inventors

XI XINFU
SUN DAWEI
YAN MENG
JIANG CHENG
ZHANG XIAOWEI

Assignees

江苏唯睿芯路科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260303

Claims (8)

1. An AGV cooperative scheduling method for multi-station joint tasks is characterized by comprising the following steps: constructing a task relaxation time assessment model, and assessing the urgency degree of a task by using the task relaxation time assessment model; estimating energy consumption of executing tasks, and constructing a comprehensive optimization objective function according to the energy consumption; Obtaining a task-AGV distribution result according to the basic constraint condition and the comprehensive optimization objective function; screening an optimal path through a cost function based on a task-AGV distribution result; Combining the urgency degree and the optimal path, and completing AGV cooperative scheduling through multi-AGV path conflict detection and time window-game type coordination; Combining the urgency degree and the optimal path, and completing AGV cooperative scheduling through multi-AGV path conflict detection and time window-game type coordination, wherein the method comprises the following steps: The AGV conflict state is identified through time window conflict detection; Setting an urgency threshold, and completing AGV cooperative scheduling through game type coordination based on the conflict state, the local priority value and the urgency threshold; The local priority value satisfies: Wherein, the Indicating the local priority value of the AGV-i, The local weight coefficient is represented by a set of coefficients, Indicating the urgency of the AGV-i, Indicating the expected remaining time of the AGV-i from the conflicting node.
2. The multi-station joint task oriented AGV collaborative scheduling method according to claim 1, wherein the task relaxation time assessment model satisfies: Wherein, the The time of relaxation is indicated as such, Representing the latest allowable completion time of task t, The current time is indicated as such, The estimated time required for the AGV to reach the workstation and the dual arm robot to complete the splice operation is shown.
3. The AGV collaborative scheduling method for a multi-station joint task according to claim 1, wherein the estimating the energy consumption of executing the task satisfies: Wherein, the Representing an estimate of the energy consumption to perform a task, The energy consumption coefficient is represented by a coefficient of energy consumption, Represents the cumulative travel distance on path i when task t is performed, Represents the accumulated waiting time generated by queuing and avoiding in the process of executing the task t, Indicating the current load quality parameters of the dual arm joint robot that the AGV is carrying.
4. The AGV collaborative scheduling method for a multi-station joint task according to claim 1, wherein the constructing a comprehensive optimization objective function according to the energy consumption satisfies: Wherein, the The overall optimization objective function is represented as such, The decision variables are represented by the values of the decision variables, Indicating that the task t is being performed by the AGV-i, Indicating that task t is not being performed by AGV-i, The weight coefficient is represented by a number of weight coefficients, Indicating the expected delay penalty for task t being performed by AGV-i, Representing an estimate of the energy consumption to perform a task, And (5) representing path conflict and congestion risk penalty indexes.
5. The multi-station joint task oriented AGV collaborative scheduling method according to claim 1, wherein the basic constraint conditions include: The task is uniquely allocated with constraint that each joint task is executed by only one AGV; AGV availability and time feasibility constraint that if AGV-i is currently executing other tasks, the latest completion time of the assigned task must not be destroyed after a new task t is inserted into the task sequence; and (3) energy and electric quantity safety constraint, namely considering the residual electric quantity of the AGV, and when the task t is executed, the residual electric quantity must not be lower than a safety threshold value.
6. The multi-station joint task oriented AGV collaborative scheduling method according to claim 1, wherein the optimal path is screened by a cost function based on a task-AGV distribution result, and the method is characterized in that: Wherein, the The cost function of the path P is represented, The path layer weight coefficient is represented as such, A single road segment in the path P is represented, The length of the road segment is represented, The normalized constant representing the maximum segment in each candidate path, Representing the occupancy density of the current road segment, Indicating the maximum congestion density that may occur in the scheduling context, Represents the historical average delay time for the road segment, Representing a historically counted maximum average delay, A task urgency indicator indicating the destination workstation, A normalized upper limit representing the urgency of all tasks in the current task set.
7. The multi-station joint task oriented AGV collaborative scheduling method according to claim 1, further comprising the steps of: Calculating the real-time cost of the current path; and comparing the cost of the bypass path with the real-time cost of the current path to obtain a final obstacle avoidance strategy.
8. The AGV cooperative scheduling system for the multi-station joint tasks is characterized by comprising input equipment, output equipment, a processor and a memory, wherein the input equipment, the output equipment, the processor and the memory are mutually connected, and the memory comprises program instructions for executing the AGV cooperative scheduling method for the multi-station joint tasks according to any one of claims 1-7.

Description

AGV cooperative scheduling method and system for multi-station joint tasks Technical Field The invention relates to the technical field of automatic guided vehicle dispatching, in particular to an AGV collaborative dispatching method and system for multi-station joint tasks. Background In the existing automatic joint of the spinning workshop, an AGV is adopted to carry a simple mechanical arm to carry out the tour joint, and the scheduling mode is mainly 1) fixed route circulation, and 2) the latest AGV allocation based on a simple task queue. The method has the obvious problems that a fixed route cannot respond to broken yarns at random, so that response is slow, a system lacks dynamic evaluation of task emergency degree (yarn breaking time length) to influence joint efficiency, and the root of the method is that a scheduling system does not consider joint optimization of task time sequence constraint, path dynamic occupation and multi-AGV cooperative avoidance. The method comprises the steps of carrying out automatic operation on a plurality of AGVs, carrying out overall task allocation effectively when the AGVs work cooperatively, avoiding conflict caused by the fact that the AGVs request the same AGV or the same station simultaneously, planning and dynamically adjusting the AGV driving path to prevent traffic jam and deadlock from occurring in narrow channels and intersections, improving the real-time response capability of the system to random broken yarn faults, reducing waiting time after broken yarns of a spinning machine, guaranteeing that the joint tasks are finished efficiently and orderly, and solving the problem of cooperative scheduling in the automatic process of the multi-station broken yarn joint tasks of the spinning workshop at present. Disclosure of Invention Aiming at the inadequacy of the existing method and the requirement of practical application, the method aims at solving the problems. In one aspect, the invention provides an AGV cooperative scheduling method for multi-station joint tasks, which comprises the following steps: The method comprises the steps of constructing a task relaxation time assessment model, assessing the urgency degree of a task by using the task relaxation time assessment model, estimating the energy consumption of executing the task, constructing a comprehensive optimization objective function according to the energy consumption, obtaining a task-AGV distribution result according to a basic constraint condition and the comprehensive optimization objective function, screening an optimal path through a cost function based on the task-AGV distribution result, combining the urgency degree and the optimal path, and completing AGV cooperative scheduling through multi-AGV path conflict detection and time window-game coordination. By evaluating the task urgency degree and optimizing the energy consumption, intelligent collaborative scheduling of the multi-AGV system is realized. The method comprises the steps of dynamically identifying task priorities based on a task relaxation time model, guaranteeing timely processing of emergency tasks and improving scheduling responsiveness, fusing energy consumption estimation to construct a comprehensive optimization target, reducing energy consumption and promoting green manufacturing while guaranteeing efficiency, optimizing resource utilization and path planning through task-AGV distribution and cost function path screening, and reducing idle and congestion, and finally, combining collision detection and time window-game coordination to effectively avoid AGV collision and waiting and enhance system reliability and overall throughput. The invention improves the dispatching efficiency, economy and adaptability under the multi-station joint task scene, and provides a reliable solution for the AGV cooperative operation in the complex industrial environment. Optionally, the task relaxation time assessment model satisfies: Wherein, the The time of relaxation is indicated as such,Representing the latest allowable completion time of task t,The current time is indicated as such,The estimated time required for the AGV to reach the workstation and the dual arm robot to complete the splice operation is shown. Optionally, the estimating the energy consumption of the executing task satisfies: Wherein, the Representing an estimate of the energy consumption to perform a task,The energy consumption coefficient is represented by a coefficient of energy consumption,Represents the cumulative travel distance on path i when task t is performed,Represents the accumulated waiting time generated by queuing and avoiding in the process of executing the task t,Indicating the current load quality parameters of the dual arm joint robot that the AGV is carrying. Optionally, the constructing a comprehensive optimization objective function according to the energy consumption meets the following conditions: Wherein, the The overall optimization objective