Residual Reinforcement Learning for Motion Control of a Bionic Exploration Robot-RoboDact | |
Zhang, Tiandong4,5; Wang, Rui5; Wang, Shuo1,2,4,5; Wang, Yu5; Zheng, Gang3; Tan, Min | |
刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT |
2023 | |
卷号 | 72页码:13 |
关键词 | Active disturbance rejection control (ADRC) bionic exploration robot motion control residual reinforcement learning (RRL) soft actor-critic (SAC) |
ISSN号 | 0018-9456 |
DOI | 10.1109/TIM.2023.3282297 |
通讯作者 | Wang, Rui(rwang5212@ia.ac.cn) |
英文摘要 | This article aims to investigate the motion control method of a bionic underwater exploration robot (RoboDact). The robot is equipped with a double-joint tail fin and two undulating pectoral fins to obtain good mobility and stability. The hybrid propulsion mode helps perform stable and effective underwater exploration and measurement. To coordinate these two kinds of bionic propulsion fins and address the challenge of measurement noises and external disturbances during underwater exploration, a novel residual reinforcement learning method with parameter randomization (PR-RRL) is proposed. The control strategy is a weighted superposition of a feedback controller and a residual controller. The observation feedback controller based on active disturbance rejection control (ADRC) is adapted to improve stability and convergence. And the residual controller based on the soft actor-critic (SAC) algorithm is adapted to improve adaptability to uncertainties and disturbances. Moreover, the parameter randomization training strategy is proposed for adapting natural complicated scenarios by randomizing the partial dynamics of the underwater exploration robot during the training phase. Finally, the feasibility and efficacy of the presented motion control method are validated by comprehensive simulation tests and RoboDact prototype physical experiments. |
资助项目 | STI 2030-Major Projects[2021ZD0114504] ; National Natural Science Foundation of China[62276253] ; National Natural Science Foundation of China[62203435] ; National Natural Science Foundation of China[62122087] ; Beijing Natural Science Foundation[4222055] ; Beijing Natural Science Foundation[4222056] ; Youth Innovation Promotion Association CAS[2020137] ; Scientific Research Program of Beijing Municipal Commission of Education-Natural Science Foundation of Beijing[KZ202210017024] |
WOS关键词 | FISH ; IMPLEMENTATION ; MANEUVERS |
WOS研究方向 | Engineering ; Instruments & Instrumentation |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:001012772500001 |
资助机构 | STI 2030-Major Projects ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS ; Scientific Research Program of Beijing Municipal Commission of Education-Natural Science Foundation of Beijing |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/53762] |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Wang, Rui |
作者单位 | 1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, F-59000 Lille, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Shanghai 200031, Peoples R China 3.Univ Lille, CRIStAL Ctr Rech Informat Signal & Automat Lille, Cent Lille, Lille 100190, France 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Tiandong,Wang, Rui,Wang, Shuo,et al. Residual Reinforcement Learning for Motion Control of a Bionic Exploration Robot-RoboDact[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2023,72:13. |
APA | Zhang, Tiandong,Wang, Rui,Wang, Shuo,Wang, Yu,Zheng, Gang,&Tan, Min.(2023).Residual Reinforcement Learning for Motion Control of a Bionic Exploration Robot-RoboDact.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,72,13. |
MLA | Zhang, Tiandong,et al."Residual Reinforcement Learning for Motion Control of a Bionic Exploration Robot-RoboDact".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 72(2023):13. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
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。
修改评论