Deadlock-solving Traffic Control Methods for Automated Guided Vehicle Systems
Maoning Chen, Yuangen Lu, Canrong Zhang · 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) · 2021
To cope with the challenges arising in the logistics industry, intelligent warehouse systems equipped with Automated Guided Vehicle (AGV) are becoming an attractive choice for enterprises. Such intelligent systems are often deployed in complex working environments where collision and deadlock problems are often inevitable. This paper focuses on designing effective traffic control strategies and algorithms to eliminate deadlocks faced in the system. More specifically, under the Resource Authorization policy, the deadlock is defined based on the graph theory; in order to minimize the occurrence of deadlocks, this paper proposes a Future Path-oriented Planning (FPP) algorithm which considers the future routes that will be traversed by AGVs by simulation; and, moreover, Deadlock Detection and Recovery (DR) strategy is introduced to detect and eliminate deadlocks. Numerical experiments conducted on two typical types of maps demonstrate the effectiveness of the proposed algorithms.