CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish
Dong, Huijie1,2; Wu, Zhengxing1,2; Meng, Yan1,2; Tan, Min1,2; Yu, Junzhi1,3
Source PublicationIEEE-ASME TRANSACTIONS ON MECHATRONICS
ISSN1083-4435
2022-06-01
Volume27Issue:3Pages:1629-1639
Corresponding AuthorYu, Junzhi(junzhi.yu@ia.ac.cn)
AbstractIn 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.
KeywordRobots Optimization Buoyancy Transient analysis Robot kinematics Hydrodynamics Energy consumption Biomimetic robot deep reinforcement learning (DRL) gliding efficiency gliding robotic fish underwater robotics
DOI10.1109/TMECH.2021.3096848
WOS KeywordAUTONOMOUS UNDERWATER GLIDER ; SHAPE OPTIMIZATION ; PERFORMANCE
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; S&T Program of Hebei
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Manufacturing ; Engineering, Electrical & Electronic ; Engineering, Mechanical
WOS IDWOS:000811604100041
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49180
Collection复杂系统管理与控制国家重点实验室_先进机器人
复杂系统管理与控制国家重点实验室_水下机器人
Corresponding AuthorYu, Junzhi
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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