TY - GEN
T1 - Multi-AGV Scheduling based on Hierarchical Intrinsically Rewarded Multi-Agent Reinforcement Learning
AU - Zhang, Jiangshan
AU - Guo, Bin
AU - Sun, Zhuo
AU - Li, Mengyuan
AU - Liu, Jiaqi
AU - Yu, Zhiwen
AU - Fan, Xiaopeng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Automated Guided Vehicle (AGV) has been widely used in automated warehouses and flexible manufacture systems for material delivery. As a flexible robot, AGV can finish automatic transportation of raw materials in different locations. The proper AGV scheduling strategy can effectively reduce the overall delivery time. To eliminate the large scheduling overhead from the centralized methods, we propose a multi-AGV distributed scheduling scheme in this paper. In particular, we design a Hierarchical Intrinsic Reward Mechanism (HIRM) for the multi-agent reinforcement learning to improve the convergence speed and the final policy level. Based on it, we propose the HIRM Bidirectionally-Coordinated Network (HIRM-BiCNet) based multi-AGV distributed scheduling scheme, to improve the scheduling success rate. The proposed scheme avoids the dependence on the global information and explicit communication. Experiment results demonstrate that our approach achieves impressive results at increase in scheduling success rate (30.75%) and decrease in scheduling time (16 time steps) compared to existing schemes.
AB - Automated Guided Vehicle (AGV) has been widely used in automated warehouses and flexible manufacture systems for material delivery. As a flexible robot, AGV can finish automatic transportation of raw materials in different locations. The proper AGV scheduling strategy can effectively reduce the overall delivery time. To eliminate the large scheduling overhead from the centralized methods, we propose a multi-AGV distributed scheduling scheme in this paper. In particular, we design a Hierarchical Intrinsic Reward Mechanism (HIRM) for the multi-agent reinforcement learning to improve the convergence speed and the final policy level. Based on it, we propose the HIRM Bidirectionally-Coordinated Network (HIRM-BiCNet) based multi-AGV distributed scheduling scheme, to improve the scheduling success rate. The proposed scheme avoids the dependence on the global information and explicit communication. Experiment results demonstrate that our approach achieves impressive results at increase in scheduling success rate (30.75%) and decrease in scheduling time (16 time steps) compared to existing schemes.
KW - AGVs
KW - Distributed Scheduling
KW - Intrinsic Motivation
KW - Multi-agent Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85146110679&partnerID=8YFLogxK
U2 - 10.1109/MASS56207.2022.00028
DO - 10.1109/MASS56207.2022.00028
M3 - 会议稿件
AN - SCOPUS:85146110679
T3 - Proceedings - 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
SP - 155
EP - 161
BT - Proceedings - 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022
Y2 - 20 October 2022 through 22 October 2022
ER -