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In this book, the authors first introduce two fish-like underwater robots, including a multiple fins-actuated robotic fish and a caudal fin-actuated robotic fish with a barycenter regulating mechanism. They study how a robotic fish uses its onboard pressure sensor arrays based-ALLS to estimate its trajectory in multiple locomotions, including rectilinear motion, turning motion, ascending motion, and spiral motion. In addition, they also explore the ALLS-based relative position and attitude perception between two robotic fish in a leader-follower formation. Four regression methods—multiple linear regression methods, support vector regressions, back propagation neural networks, and random forest methods—are used to evaluate the relative positions or attitudes using the ALLS data. The research on ALLS-based local sensing between two adjacent fish robots extends current research from one individual underwater robot to two robots in formation, and will attract increasing attention from scholars of robotics, underwater technology, biomechanics and systems, and control engineering.

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9781032316185

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Guangming Xie received his B.S. degrees in both Applied Mathematics and Electronic and Computer Technology, his M.E. degree in Control Theory and Control Engineering, and his Ph.D. degree in Control Theory and Control Engineering from Tsinghua University, Beijing, China in 1996, 1998, and 2001, respectively. Then he worked as a postdoctoral research fellow in the Center for Systems and Control, Department of Mechanics and Engineering Science, Peking University, Beijing, China from July 2001 to June 2003. In July 2003, he joined the Center as a lecturer. Now he is a Full Professor of Dynamics and Control in the College of Engineering, Peking University. Xingwen Zheng received his Ph.D. degree in General Mechanics and Foundation of Mechanics from Peking University in 2020 and his B.E. degree in Mechanical Engineering and Automation from Northeastern University in 2015. Now he is a JSPS post-doctoral fellow at the University of Tokyo.

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