A Fish-like Binocular Vision System for Underwater Perception of Robotic Fish.

field of view stitching fish-like vision system robotic fish underwater perception underwater robot

Journal

Biomimetics (Basel, Switzerland)
ISSN: 2313-7673
Titre abrégé: Biomimetics (Basel)
Pays: Switzerland
ID NLM: 101719189

Informations de publication

Date de publication:
12 Mar 2024
Historique:
received: 17 01 2024
revised: 28 02 2024
accepted: 10 03 2024
medline: 27 3 2024
pubmed: 27 3 2024
entrez: 27 3 2024
Statut: epublish

Résumé

Biological fish exhibit a remarkably broad-spectrum visual perception capability. Inspired by the eye arrangement of biological fish, we design a fish-like binocular vision system, thereby endowing underwater bionic robots with an exceptionally broad visual perception capacity. Firstly, based on the design principles of binocular visual field overlap and tangency to streamlined shapes, a fish-like vision system is developed for underwater robots, enabling wide-field underwater perception without a waterproof cover. Secondly, addressing the significant distortion and parallax of the vision system, a visual field stitching algorithm is proposed to merge the binocular fields of view and obtain a complete perception image. Thirdly, an orientation alignment method is proposed that draws scales for yaw and pitch angles in the stitched images to provide a reference for the orientation of objects of interest within the field of view. Finally, underwater experiments evaluate the perception capabilities of the fish-like vision system, confirming the effectiveness of the visual field stitching algorithm and the orientation alignment method. The results show that the constructed vision system, when used underwater, achieves a horizontal field of view of 306.56°. The conducted work advances the visual perception capabilities of underwater robots and presents a novel approach to and insight for fish-inspired visual systems.

Identifiants

pubmed: 38534856
pii: biomimetics9030171
doi: 10.3390/biomimetics9030171
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : National Natural Science Foundation of China
ID : 62233001, T2121002, 62073196

Auteurs

Ru Tong (R)

Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.

Zhengxing Wu (Z)

Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.

Jinge Wang (J)

Human-Oriented Methodology and Equipment Laboratory, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China.

Yupei Huang (Y)

Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.

Di Chen (D)

State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China.

Junzhi Yu (J)

Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China.

Classifications MeSH