AGV task allocation method, logistics sorting method and logistics sorting system

Abstract

The invention relates to an AGV task allocation method, a logistics sorting method and a logistics sorting system. The AGV task allocation method comprises the following steps that: a sorting task isinitiated, a parcel is placed on an AGV located on a sorting table through manpower or a mechanical arm, i.e., the AGV obtains the task on the sorting table; and after the task is obtained, the processes of planning a path and delivering the parcel are completed; after the AGV completes the jth task, the time required by the AGV to go from the current task position to the next sorting table and obtains a task is calculated;a sorting table enabling the minimum time required is selected as the next sorting table to which the AGV returns after delivers the task. The method is applied to the scenethat the number of tasks is far larger than that of AGVs, and the utilization efficiency of the system is improved.

Description

technical field [0001] The invention relates to an automated logistics technology, in particular to an AGV task assignment method, a logistics sorting method and a system. Background technique [0002] Today, with the rapid development of automation and intelligent logistics, the use of belt transmission systems or automatic guided vehicles (Automated Guided Vehicle, AGV) with a small number of manpower to complete the picking operation has been favored by more and more practitioners. Multi-robot task assignment ( Multi-robot Task Allocation, MRTA) is an unavoidable problem in the AGV sorting and scheduling system, especially the sorting scenarios involving large-scale cluster scheduling. For various types of AGV picking systems, how to coordinate task allocation is one of the main factors affecting the efficiency of the picking system. [0003] Such as figure 1 As shown, the task list ti and the idle robot list rj are two groups of nodes that do not have self-connection. ...

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Problems solved by technology

Moreover, the Hungarian algorithm does not take into account the flow control problem, resulting in the possibility of systematic congestion, making the matching results obtained by the algorithm lose the optimal nature

[0008] 2. The minimum cost maximum flow algorithm is more suitable for path allocation network flow control, and path search and adjustment in the task allocation stage will take an unbearable extra time cost

[0009] 3. When most systems perform AGV scheduling, they usually schedule idle AGVs to complete the current task to reduce the completion time of the task, but this will also increase the overall waiting time and driving distance of the AGV.

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