PSO-based leg-loss identification method for legged robots

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Published: Aug 7, 2018

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S. Chattunyakit
Division of Human Mechanical Systems and Design, Graduate School of Engineering, Hokkaido University, N13W8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan
Y. Kobayashi
Division of Human Mechanical Systems and Design, Faculty of Engineering, Hokkaido University, N13W8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan
T. Emaru
Division of Human Mechanical Systems and Design, Faculty of Engineering, Hokkaido University, N13W8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan

Abstract

Legged robots have been widely developed and utilized in various applications since they are more flexible than the conventional wheel-based robots which cannot perform effectively in bumpy areas. Although the legged robot has no difficulty operating in uneven terrain, the broken parts of legged robot can lead to the task failure. In case of damages, the legged robots cannot operate properly with prior control strategies due to transformed models. This paper proposes the new method to detect the broken legs by employing only internal sensors. The lengths of robot legs will be estimated using the comparison between damaged robot and candidate models constructed in the simulation. Particle Swarm Optimization (PSO) is operated to discover the best candidate model that provides the highest fitness value. The similarity of orientation of robot body between actual damaged robot and candidate models is set as the fitness function calculated using normalized cross-correlation algorithm. The efficiency of this method is verified using numerical simulations and experiments, which shows that the proposed method can detect the lengths of robot legs more accurate than the existing method.

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How to Cite
Chattunyakit, S., Kobayashi, Y., & Emaru, T. (2018). PSO-based leg-loss identification method for legged robots. Journal of Research and Applications in Mechanical Engineering, 4(1), 46–57. Retrieved from https://ph01.tci-thaijo.org/index.php/jrame/article/view/138793
Issue
Vol. 4 No. 1 (2016)
Section
RESEARCH ARTICLES

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