Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish

doi:10.1109/TMECH.2021.3096848

	Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish
	Dong, Huijie1,2 ; Wu, Zhengxing1,2 ; Meng, Yan1,2 ; Tan, Min1,2 ; Yu, Junzhi1,3
发表期刊	IEEE-ASME TRANSACTIONS ON MECHATRONICS
ISSN	1083-4435
	2022-06-01
卷号	27 期号:3 页码:1629-1639
通讯作者	Yu, Junzhi(junzhi.yu@ia.ac.cn)
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.
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
DOI	10.1109/TMECH.2021.3096848
关键词[WOS]	AUTONOMOUS UNDERWATER GLIDER ; SHAPE OPTIMIZATION ; PERFORMANCE
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; S&T Program of Hebei
WOS研究方向	Automation & Control Systems ; Engineering
WOS类目	Automation & Control Systems ; Engineering, Manufacturing ; Engineering, Electrical & Electronic ; Engineering, Mechanical
WOS记录号	WOS:000811604100041
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49180
专题	复杂系统认知与决策实验室_先进机器人复杂系统认知与决策实验室_水下机器人
通讯作者	Yu, Junzhi
作者单位	1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, BIC ESAT,Dept Adv Mfg & Robot, Beijing 100871, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.