With the aid of visual perception technologies, two up-to-date underwater robot systems are demonstrated. The first system focuses on underwater robotic operation for the task of object collection in the sea. The second is an untethered biomimetic robotic fish with a camera stabilizer, its control methods based on visual tracking.
The authors provide a self-contained and comprehensive guide to understand underwater visual perception and control. Bridging the gap between theory and practice in underwater vision, the book features implementable algorithms, numerical examples, and tests, where codes are publicly available. Additionally, the mainstream technologies covered in the book include deep learning, adversarial learning, evolutionary computation, robust control, and underwater bionics. Researchers, senior undergraduate and graduate students, and engineers dealing with underwater visual perception and control will benefit from this work.
Junzhi Yu is a professor of Peking University, whose research interests incude biomimetic robots, intelligent control, and intelligent mechatonic systems. In these areas, he has (co-)authored 3 monographs, and published over 100 SCI papers in the prestigious robotics and automation related journals.
Xingyu Chen, PhD in University of Chinese Academy of Sciences.
Shihan Kong, PhD student in University of Chinese Academy of Sciences.