Real-world learning control for autonomous exploration of a biomimetic robotic shark | |
Yan Shuaizheng2; Wu Zhengxing2; Wang Jian2; Huang Yupei2; Tan Min2; Yu Junzhi1,2 | |
刊名 | IEEE Transactions on Industrial Electronics |
2022-05 | |
卷号 | 70期号:4页码:3966-3974 |
英文摘要 | With the development of learning-based autonomous underwater exploration method of robotic fish, how to improve data quality and sampling efficiency, so as to achieve better control performance, becomes a challenging research subject. To address this issue, in this article, a feasible real-world deep reinforcement learning framework for autonomous underwater exploration of robotic fish is proposed, which ably avoids model discrepancy generated in virtual training. The designed framework consists of three phases: 1) teaching initialization; 2) regular update of reinforcement learning; and 3) phased consolidation training. Especially, reasonable teaching initialization improves the data sampling efficiency and stabilizes the real-world early training. The consolidation training ensures the reproducibility of good controllers by interim imitation learning in the middle training phase. Extensive underwater experiments on a novel self-developed biomimetic robotic shark show that the proposed real-world learning method significantly improves the safety and efficiency of autonomous exploration based on local sensor information, providing a promising solution for exploring in uncharted wild waters. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/51842] |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Wu Zhengxing |
作者单位 | 1.State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, BIC-ESAT, College of Engineering, Peking University 2.Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Yan Shuaizheng,Wu Zhengxing,Wang Jian,et al. Real-world learning control for autonomous exploration of a biomimetic robotic shark[J]. IEEE Transactions on Industrial Electronics,2022,70(4):3966-3974. |
APA | Yan Shuaizheng,Wu Zhengxing,Wang Jian,Huang Yupei,Tan Min,&Yu Junzhi.(2022).Real-world learning control for autonomous exploration of a biomimetic robotic shark.IEEE Transactions on Industrial Electronics,70(4),3966-3974. |
MLA | Yan Shuaizheng,et al."Real-world learning control for autonomous exploration of a biomimetic robotic shark".IEEE Transactions on Industrial Electronics 70.4(2022):3966-3974. |
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