Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed
文献类型：期刊论文
| 作者 | Wu, Shiguang1,2 ; Pu, Zhiqiang1,2; Qiu, Tenghai2; Yi, Jianqiang1,2; Zhang, Tianle1,2
|
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
 |
| 出版日期 | 2023 |
| 卷号 | 19期号:1页码:121-132 |
| 关键词 | Robots Optimization Maintenance engineering Task analysis Informatics Topology Reinforcement learning Connectivity maintenance deep reinforcement learning (DRL) multirobot system multitarget coverage |
| ISSN号 | 1551-3203 |
| DOI | 10.1109/TII.2022.3160629 |
| 通讯作者 | Pu, Zhiqiang(zhiqiang.pu@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | DEPLOYMENT ; NETWORK |
| 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] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000880654600015 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51239]  |
| 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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。