Exploring Learning-Based Control Policy for Fish-Like Robots in Altered Background Flows

doi:10.1109/IROS55552.2023.10342394

	Exploring Learning-Based Control Policy for Fish-Like Robots in Altered Background Flows
	Lin, Xiaozhu; Song, Wenbin; Liu, Xiaopei; He, Xuming; Wang, Yang
	2023-10-05
会议录名称	2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
ISSN	2153-0858
页码	2338-2345
发表状态	已发表
DOI	10.1109/IROS55552.2023.10342394
摘要	The study of motion control for the fish-like robots in complex fluid fields is of great importance in improving the performance of underwater vehicles, due to its strong maneuverability, propulsion efficiency, and deceptive visual appearance. In this article, a novel learning-based control framework is first proposed to autonomously explore efficient control policies that are capable of performing motion control tasks in non-quiescent and unknown background flows. First, we utilize a high-fidelity simulation system, named FishGym, to generate various uniform flows. Next, a DRL-based algorithm is incorporated with the FishGym to train the fish-like robot to control its motion to optimally complete a delicately designed task (Approaching Target and Stay) in both quiescent and uniform flow. Then, the obtained control policy together with an online estimator is directly applied to a Path-Following Task. The proposed framework well balances the simulation accuracy and the computational efficiency, which is of crucial importance for effective coupling with the learning algorithm. The simulation results indicate that, via the proposed learning framework, the robot successfully acquired a swimming strategy that can be used to adapt to different background flows and tasks. Furthermore, we also observe some adaptation behavior of the robot, such as rheotaxis, that is similar to the fish in nature, which gains us more insight into the mechanism underlying the adaptation behavior of fish in a complex environment. © 2023 IEEE.
关键词	Visualization Simulation Propulsion Fish Behavioral sciences Task analysis Motion control
会议名称	2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
会议地点	Detroit, MI, USA
会议日期	1-5 Oct. 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20240315412071
EI主题词	Motion control
EISSN	2153-0866
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 723.4.2 Machine Learning ; 731.3 Specific Variables Control ; 731.5 Robotics ; 971 Social Sciences
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/349504
专题	信息科学与技术学院信息科学与技术学院_PI研究组_何旭明组信息科学与技术学院_PI研究组_刘晓培组信息科学与技术学院_博士生信息科学与技术学院_PI研究组_汪阳组
作者单位	School of Information Science and Technology, ShanghaiTech University, Shanghai, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Lin, Xiaozhu,Song, Wenbin,Liu, Xiaopei,et al. Exploring Learning-Based Control Policy for Fish-Like Robots in Altered Background Flows[C]:Institute of Electrical and Electronics Engineers Inc.,2023:2338-2345.