Publisher Description

Visual Perception and Control of Underwater Robots covers theories and applications from aquatic visual perception and underwater robotics. Within the framework of visual perception for underwater operations, image restoration, binocular measurement, and object detection are addressed. More specifically, the book includes adversarial critic learning for visual restoration, NSGA-II-based calibration for binocular measurement, prior knowledge refinement for object detection, analysis of temporal detection performance, as well as the effect of the aquatic data domain on object detection. 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.

Author Biography

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.

Table of Contents

1. Introduction 2. Adaptive Real-Time Underwater Visual Restoration with Adversarial Critical Learning 3. A NSGA-II-Based Calibration for Underwater Binocular Vision Measurement 4. Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos 5. Rethinking Temporal Object Detection from Robotic Perspectives 6. Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes 7. IWSCR: An Intelligent Water Surface Cleaner Robot for Collecting Floating Garbage 8. Underwater Target Tracking Control of an Untethered Robotic Fish with a Camera Stabilizer 9. Summary and Outlook

Long Description

Visual Perception and Control of Underwater Robots covers theories and applications from aquatic visual perception and underwater robotics. Within the framework of visual perception for underwater operations, image restoration, binocular measurement, and object detection are addressed. More specifically, the book includes adversarial critic learning for visual restoration, NSGA-II-based calibration for binocular measurement, prior knowledge refinement for object detection, analysis of temporal detection performance, as well as the effect of the aquatic data domain on object detection. 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.

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