Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed

doi:10.1109/TII.2022.3160629

	Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed
	Wu, Shiguang1,2 ; Pu, Zhiqiang1,2 ; Qiu, Tenghai1 ; Yi, Jianqiang1,2 ; Zhang, Tianle1,2
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN	1551-3203
	2023
卷号	19 期号:1 页码:121-132
通讯作者	Pu, Zhiqiang(zhiqiang.pu@ia.ac.cn)
摘要	Deriving a distributed, time-efficient, and connectivity-guaranteed coverage policy in multitarget environment poses huge challenges for a multirobot team with limited coverage and limited communication. In particular, the robot team needs to cover multiple targets while preserving connectivity. In this article, a novel deep-reinforcement-learning-based approach is proposed to take both multitarget coverage and connectivity preservation into account simultaneously, which consists of four parts: a hierarchical observation attention representation, an interaction attention representation, a two-stage policy learning, and a connectivity-guaranteed policy filtering. The hierarchical observation attention representation is designed for each robot to extract the latent features of the relations from its neighboring robots and the targets. To promote the cooperation behavior among the robots, the interaction attention representation is designed for each robot to aggregate information from its neighboring robots. Moreover, to speed up the training process and improve the performance of the learned policy, the two-stage policy learning is presented using two reward functions based on algebraic connectivity and coverage rate. Furthermore, the learned policy is filtered to strictly guarantee the connectivity based on a model of connectivity maintenance. Finally, the effectiveness of the proposed method is validated by numerous simulations. Besides, our method is further deployed to an experimental platform based on quadrotor unmanned aerial vehicles and omnidirectional vehicles. The experiments illustrate the practicability of the proposed method.
关键词	Robots Optimization Maintenance engineering Task analysis Informatics Topology Reinforcement learning Connectivity maintenance deep reinforcement learning (DRL) multirobot system multitarget coverage
DOI	10.1109/TII.2022.3160629
关键词[WOS]	DEPLOYMENT ; NETWORK
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0102404] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27030000] ; National Natural Science Foundation of China[62073323] ; External Cooperation Key Project of the Chinese Academy of Sciences[173211KYSB20200002] ; External Cooperation Key Project of the Chinese Academy of Sciences[TII-21-3816]
项目资助者	National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; External Cooperation Key Project of the Chinese Academy of Sciences
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:000880654600015
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51240
专题	复杂系统认知与决策实验室
通讯作者	Pu, Zhiqiang
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wu, Shiguang,Pu, Zhiqiang,Qiu, Tenghai,et al. Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2023,19(1):121-132.
APA	Wu, Shiguang,Pu, Zhiqiang,Qiu, Tenghai,Yi, Jianqiang,&Zhang, Tianle.(2023).Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,19(1),121-132.
MLA	Wu, Shiguang,et al."Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 19.1(2023):121-132.