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Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Wang, Wei and Gu, Dongbing and Xie, Guangming (2019) 'Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors.' IEEE Transactions on Systems Man and Cybernetics: Systems, 49 (5). pp. 891-903. ISSN 2168-2216

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Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds.

Item Type: Article
Uncontrolled Keywords: Autonomous optimization; central pattern generators (CPGs); gait evaluation; robotic fish; underwater navigation; underwater robots
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 17 Sep 2018 14:00
Last Modified: 13 Jan 2022 22:01

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