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Applied Bionics and Biomechanics
Volume 1, Issue 1, Pages 57-66

Learning Algorithm for a Brachiating Robot

Hideki Kajima,1 Yasuhisa Hasegawa,2 and Toshio Fukuda1

1Department of Micro System Engineering, Nagoya University, Nagoya, Japan
2Department of Mechanical and Systems Engineering, Gifu University, Gifu, Japan

Copyright © 2003 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper introduces a new concept of multi-locomotion robot inspired by an animal. The robot, ‘Gorilla Robot II’, can select the appropriate locomotion (from biped locomotion, quadruped locomotion and brachiation) according to an environment or task. We consider ‘brachiation’ to be one of the most dynamic of animal motions. To develop a brachiation controller, architecture of the hierarchical behaviour-based controller, which consists of behaviour controllers and behaviour coordinators, was used. To achieve better brachiation, an enhanced learning method for motion control, adjusting the timing of the behaviour coordination, is proposed. Finally, it is shown that the developed robot successfully performs two types of brachiation and continuous locomotion.