Neural control for gait generation and adaptation of a gecko robot
Conference proceedings article
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Author list: Srisuchinnawong A., Shao D., Ngamkajornwiwat P., Teerakittikul P., Dai Z., Ji A., Manoonpong P.
Publisher: Hindawi
Publication year: 2019
Start page: 468
End page: 473
Number of pages: 6
ISBN: 9781728124674
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
Geckos are highly adaptable creatures, able to scale a variety of slopes, including walls, and can change their gait depending on their environment. Roboticists have tried to implement this behaviour in gecko robots. So far, an open-loop controlled robot without a tail that uses only one specific gait can climb to a 50ฐ slope. In this paper, we propose neural control that allows a gecko robot to climb to a 70ฐ slope by generating different gaits for various slope angles. The control consists of three main components: a central pattern generator (CPG) for generating various rhythmic patterns, CPG post-processing for shaping the CPG signals, and a delay line for transmitting the shaped CPG signals to drive the legs of the gecko robot. The robot uses a body inclination sensor to provide sensory feedback for gait adaptation. When the incline is below 35ฐ, the robot walks with a predefined fast trot gait. If the incline is increased, it will change its gait from the trot gait to an intermediate gait, followed by a slow wave gait, which is both the most stable and the slowest gait, for climbing the steepest slopes. Using this walking strategy, the robot can efficiently climb a variety of slopes using different gaits and can automatically adapt its gait to maximise speed while ensuring stability. ฉ 2019 IEEE.
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