Applied Bionics and Biomechanics

Applied Bionics and Biomechanics / 2012 / Article
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Human-Robot Interaction/Interface

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Volume 9 |Article ID 257069 | https://doi.org/10.3233/ABB-2011-0021

J. A. Gallego, A. Forner-Cordero, J. C. Moreno, E. A. Turowska, J. L. Pons, "Detection of Gait Perturbations Based on Proprioceptive Information. Application to Limit Cycle Walkers", Applied Bionics and Biomechanics, vol. 9, Article ID 257069, 16 pages, 2012. https://doi.org/10.3233/ABB-2011-0021

Detection of Gait Perturbations Based on Proprioceptive Information. Application to Limit Cycle Walkers

Abstract

Walking on irregular surfaces and in the presence of unexpected events is a challenging problem for bipedal machines. Up to date, their ability to cope with gait disturbances is far less successful than humans’: Neither trajectory controlled robots, nor dynamic walking machines (Limit Cycle Walkers) are able to handle them satisfactorily. On the contrary, humans reject gait perturbations naturally and efficiently relying on their sensory organs that, if needed, elicit a recovery action. A similar approach may be envisioned for bipedal robots and exoskeletons: An algorithm continuously observes the state of the walker and, if an unexpected event happens, triggers an adequate reaction. This paper presents a monitoring algorithm that provides immediate detection of any type of perturbation based solely on a phase representation of the normal walking of the robot. The proposed method was evaluated in a Limit Cycle Walker prototype that suffered push and trip perturbations at different moments of the gait cycle, providing 100% successful detections for the current experimental apparatus and adequately tuned parameters, with no false positives when the robot is walking unperturbed.

Copyright © 2012 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.


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