[1]ZHANG Wenxu,MA Lei,HE Huilin,et al.Air-ground heterogeneous coordination for multi-agent coverage based on reinforced learning[J].CAAI Transactions on Intelligent Systems,2018,13(2):202-207.[doi:10.11992/tis.201609017]
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Air-ground heterogeneous coordination for multi-agent coverage based on reinforced learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 2 Page number: 202-207 Column: 学术论文—机器学习 Public date: 2018-04-15
Title:
Air-ground heterogeneous coordination for multi-agent coverage based on reinforced learning
Author(s):
ZHANG Wenxu;  MA Lei;  HE Huilin;  WANG Xiaodong
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Keywords:
heterogeneous multi-agent system; coverage; air-ground; UAV/UGV; DEC-POMDPs; reinforced learning
CLC:
TP181
DOI:
10.11992/tis.201609017
Abstract:
With the heterogeneous coordinate task of unmanned aerial vehicles (UAVs) and unmanned ground vehicle (UGVs) as the background to this study, a novel air-ground heterogeneous coverage model for a coordinated multi-agent is proposed by the complementation between UAV and UGV heterogeneity, in order to extend and improve the dynamic coverage of a heterogeneous multi-agent system. During the coverage process, the advantages of mobility and the observation scope of the UAV were used in order to guide the actions of the UGV. Moreover, in view of the partial agent observability and uncertainty, decentralized and partially observable Markov decision processes (DEC-POMDPs) were applied as the model in order to establish the coverage environment. Additionally, the reinforced learning algorithm of multi-agents was utilized in order to complete the coverage of the environment. The simulation results revealed that the coverage process was accelerated by the cooperation of the UAV and UGV. Additionally, the reinforced learning algorithm also improved the effectiveness of the coverage model.
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