Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

doi:10.1109/JAS.2021.1004228

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	Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks
	Tianhao Zhang; Jiuhong Xiao; Liang Li; Chen Wang; Guangming Xie
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2021
卷号	8 期号:12 页码:1964-1976
摘要	Controlling multiple multi-joint fish-like robots has long captivated the attention of engineers and biologists, for which a fundamental but challenging topic is to robustly track the postures of the individuals in real time. This requires detecting multiple robots, estimating multi-joint postures, and tracking identities, as well as processing fast in real time. To the best of our knowledge, this challenge has not been tackled in the previous studies. In this paper, to precisely track the planar postures of multiple swimming multi-joint fish-like robots in real time, we propose a novel deep neural network-based method, named TAB-IOL. Its TAB part fuses the top-down and bottom-up approaches for vision-based pose estimation, while the IOL part with long short-term memory considers the motion constraints among joints for precise pose tracking. The satisfying performance of our TAB-IOL is verified by testing on a group of freely swimming fish-like robots in various scenarios with strong disturbances and by a deed comparison of accuracy, speed, and robustness with most state-of-the-art algorithms. Further, based on the precise pose estimation and tracking realized by our TAB-IOL, several formation control experiments are conducted for the group of fish-like robots. The results clearly demonstrate that our TAB-IOL lays a solid foundation for the coordination control of multiple fish-like robots in a real working environment. We believe our proposed method will facilitate the growth and development of related fields.
关键词	Deep neural networks formation control multiple fish-like robots pose estimation pose tracking
DOI	10.1109/JAS.2021.1004228
引用统计	被引频次：7[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/173211/45407
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Tianhao Zhang,Jiuhong Xiao,Liang Li,et al. Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(12):1964-1976.
APA	Tianhao Zhang,Jiuhong Xiao,Liang Li,Chen Wang,&Guangming Xie.(2021).Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks.IEEE/CAA Journal of Automatica Sinica,8(12),1964-1976.
MLA	Tianhao Zhang,et al."Toward Coordination Control of Multiple Fish-Like Robots: Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks".IEEE/CAA Journal of Automatica Sinica 8.12(2021):1964-1976.

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