Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish
文献类型：期刊论文
| 作者 | Dong, Huijie2,3 ; Wu, Zhengxing2,3; Meng, Yan2,3; Tan, Min2,3; Yu, Junzhi1,3
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| 刊名 | IEEE-ASME TRANSACTIONS ON MECHATRONICS
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| 出版日期 | 2022-06-01 |
| 卷号 | 27期号:3页码:1629-1639 |
| 关键词 | Robots Optimization Buoyancy Transient analysis Robot kinematics Hydrodynamics Energy consumption Biomimetic robot deep reinforcement learning (DRL) gliding efficiency gliding robotic fish underwater robotics |
| ISSN号 | 1083-4435 |
| DOI | 10.1109/TMECH.2021.3096848 |
| 通讯作者 | Yu, Junzhi(junzhi.yu@ia.ac.cn) |
| 英文摘要 | In this article, we present a gliding efficiency optimization strategy based on deep reinforcement learning for a gliding robotic fish. For the gliding motion in shallow waters, the nonsteady motion strongly impacts the gliding range and also reduces efficiency. This article presents a concept of transient gliding motion and illustrates its importance for the gliding robotic fish. For better gliding performance of active fins, several pectoral fins with different sizes are designed and their hydrodynamics and optimizing capabilities are analyzed by computational fluid dynamics simulation. Then, a double deep Q network-based optimization strategy is proposed to improve gliding efficiency by active pectoral fins, in which an adversarial model and a two-stage reward function are presented for the adequate calculation of gliding range. Simulations are conducted to validate the convergence and effectiveness of the proposed strategy. The aquatic experiments are carried out to further verify the proposed strategy. The results reveal that the optimization strategy can save about 4.88% of energy and 19.45% of travel time. This article provides clues to the design of active control surfaces and improvement of gliding efficiency for underwater vehicles. Remarkably, the proposed strategy can significantly improve the duration and endow the robot with the potential to perform complex tasks. |
| WOS关键词 | AUTONOMOUS UNDERWATER GLIDER ; SHAPE OPTIMIZATION ; PERFORMANCE |
| 资助项目 | National Natural Science Foundation of China[61725305] ; National Natural Science Foundation of China[61633020] ; National Natural Science Foundation of China[61633004] ; National Natural Science Foundation of China[62033013] ; National Natural Science Foundation of China[62073196] ; S&T Program of Hebei[F2020203037] |
| WOS研究方向 | Automation & Control Systems ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000811604100041 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; S&T Program of Hebei |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49179]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 复杂系统管理与控制国家重点实验室_水下机器人 |
| 通讯作者 | Yu, Junzhi |
| 作者单位 | 1.Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, BIC ESAT,Dept Adv Mfg & Robot, Beijing 100871, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Dong, Huijie,Wu, Zhengxing,Meng, Yan,et al. Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish[J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS,2022,27(3):1629-1639. |
| APA | Dong, Huijie,Wu, Zhengxing,Meng, Yan,Tan, Min,&Yu, Junzhi.(2022).Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish.IEEE-ASME TRANSACTIONS ON MECHATRONICS,27(3),1629-1639. |
| MLA | Dong, Huijie,et al."Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish".IEEE-ASME TRANSACTIONS ON MECHATRONICS 27.3(2022):1629-1639. |
入库方式： OAI收割
来源：自动化研究所
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