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Deep reinforcement learning based multi-target coverage with connectivity guaranteed
Shiguang Wu1,2; Zhiqiang Pu1,2; Tenghai Qiu1; Jianqiang Yi1,2; Tianle Zhang1,2
Source PublicationIEEE Transactions on Industrial Informatics
2022
Issue2022Pages:1-12
Abstract

Deriving a distributed, time-efficient, and connectivity guaranteed coverage policy in multi-target environment poses huge challenges for a multi-robot team with limited coverage and limited communication. In particular, the robot team needs to cover multiple targets while preserving connectivity. In this paper, a novel deep reinforcement learning based approach is proposed to take both multi-target 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 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 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 quad-rotor unmanned aerial vehicles (UAVs) and omnidirectional vehicles. The experiments illustrate the practicability of the proposed method.

KeywordMulti-target coverage multi-robot system connectivity maintenance deep reinforcement learning
DOI10.1109/TII.2022.3160629
URL查看原文
Indexed BySCI
Language英语
Sub direction classification多智能体系统
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47426
Collection综合信息系统研究中心_飞行器智能技术
Corresponding AuthorZhiqiang Pu
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shiguang Wu,Zhiqiang Pu,Tenghai Qiu,et al. Deep reinforcement learning based multi-target coverage with connectivity guaranteed[J]. IEEE Transactions on Industrial Informatics,2022(2022):1-12.
APA Shiguang Wu,Zhiqiang Pu,Tenghai Qiu,Jianqiang Yi,&Tianle Zhang.(2022).Deep reinforcement learning based multi-target coverage with connectivity guaranteed.IEEE Transactions on Industrial Informatics(2022),1-12.
MLA Shiguang Wu,et al."Deep reinforcement learning based multi-target coverage with connectivity guaranteed".IEEE Transactions on Industrial Informatics .2022(2022):1-12.
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