Energy Efficient Swimming: Exploring an Intermittent Swimming Gait for Robotic Fish via Deep Reinforcement Learning
文献类型：期刊论文
| 作者 | Cao, Qiyuan5,6; Wang, Rui6; Huang S(黄顺)3,4; Zhang, Tiandong6; Yin B(银波)4; Tan, Min2; Wang, Shuo1,5,6 |
| 刊名 | IEEE-ASME TRANSACTIONS ON MECHATRONICS
 |
| 出版日期 | 2025-07-02 |
| 页码 | 12 |
| 关键词 | Fish Sports Robots Energy efficiency Biological system modeling Robot kinematics Force Deep reinforcement learning Robot sensing systems Computational fluid dynamics Biomimetic robot computational fluid dynamics (CFD) deep reinforcement learning (DRL) |
| ISSN号 | 1083-4435 |
| DOI | 10.1109/TMECH.2025.3579529 |
| 通讯作者 | Wang, Rui(rwang5212@ia.ac.cn) |
| 英文摘要 | Energy conservation is a major challenge for robotic fish due to difficulties in replenishing energy underwater. In response, we explore the optimal energy efficient swimming gait for robotic fish using our proposed deep reinforcement learning (DRL) framework. Surprisingly, our work reveals that traditional continuous swimming gaits are not the most energy efficient option for robotic fish. Instead, "smart swimmers" utilize intermittent swimming gaits (ISG) to enhance efficiency, resembling the natural "burst and coast" behavior observed in real fish. During the burst phase, the robotic fish utilizes high-frequency undulations to accelerate, followed by a coast phase where it remains stationary, analogous to surfing. The key difference in intermittent swimming at different speeds is the coast phase duration. Furthermore, our computational fluid dynamics analysis reveals the energy-saving mechanism behind this ISG for robotic fish. Experimental results confirm the superiority of the ISG learned from DRL over the commonly used continuous swimming gait in robotic fish applications, resulting in a notable average energy savings of 24.2% . |
| 分类号 | 一类 |
| WOS关键词 | ADVANTAGES ; BODY |
| 资助项目 | STI 2030-Major Projects[2022ZD0209600] ; National Natural Science Foundation of China[62276253] ; National Natural Science Foundation of China[62403463] ; National Natural Science Foundation of China[U23B2038] ; Postdoctoral Fellowship Program of CPSF[GZC20241917] ; China Postdoctoral Science Foundation[2024M763532] |
| WOS研究方向 | Automation & Control Systems ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001522946700001 |
| 资助机构 | STI 2030-Major Projects ; National Natural Science Foundation of China ; Postdoctoral Fellowship Program of CPSF ; China Postdoctoral Science Foundation |
| 其他责任者 | Wang, Rui |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/102247]  |
| 专题 | 力学研究所_流固耦合系统力学重点实验室(2012-) |
| 作者单位 | 1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China 2.Chinese Acad Sci, Inst Automat, Lab Cognit & Decis Intelligence Complex Syst, Beijing 100190, Peoples R China; 3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China; 4.Chinese Acad Sci, Key Lab Mech Fluid Solid Coupling Syst, Inst Mech, Beijing 100190, Peoples R China; 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China; 6.Chinese Acad Sci, Inst Automation, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Cao, Qiyuan,Wang, Rui,Huang S,et al. Energy Efficient Swimming: Exploring an Intermittent Swimming Gait for Robotic Fish via Deep Reinforcement Learning[J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS,2025:12. |
| APA | Cao, Qiyuan.,Wang, Rui.,黄顺.,Zhang, Tiandong.,银波.,...&Wang, Shuo.(2025).Energy Efficient Swimming: Exploring an Intermittent Swimming Gait for Robotic Fish via Deep Reinforcement Learning.IEEE-ASME TRANSACTIONS ON MECHATRONICS,12. |
| MLA | Cao, Qiyuan,et al."Energy Efficient Swimming: Exploring an Intermittent Swimming Gait for Robotic Fish via Deep Reinforcement Learning".IEEE-ASME TRANSACTIONS ON MECHATRONICS (2025):12. |
入库方式： OAI收割
来源：力学研究所
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