Adaptive dynamic walking of a quadruped robot using a neural system
model
(2001)Abstract
We are trying to induce a quadruped robot to walk dynamically on irregular
terrain by using a neural system model. In this paper, we integrate
several reflexes, such as a stretch reflex, a vestibulospinal reflex
and extensor/flexor reflexes, into a central pattern generator (CPG).
We try to realize adaptive walking up and down a slope of 12°, walking
over an obstacle 3 cm in height, and walking on terrain undulation
consisting of bumps 3 cm in height with fixed parameters of CPGs
and reflexes. The success in walking on such irregular terrain in
spite of stumbling and landing on obstacles shows that the control
method using a neural system model proposed in this study has the
ability for autonomous adaptation to unknown irregular terrain. In
order to clarify the role of a CPG, we investigate the relation between
parameters of a CPG and the mechanical system by simulations and
experiments. CPGs can generate stable walking suitable for the mechanical
system by receiving inhibitory input as sensory feedback and generate
adaptive walking on irregular terrain by receiving excitatory input
as sensory feedback. MPEG footage of these experiments can be seen
at: http://www.kimura.is.uec.ac.jp.
