Head motion stabilization during quadruped robot locomotion: combining dynamical systems and a genetic algorithm

Abstract

The head shaking that results from robot locomotion is important because it difficults stable image acquisition and the possibility to rely on that information to act accordingly, for instance, to achieve visually-guided locomotion. In this article, we focus on the development of a head controller able to minimize the head motion induced by locomotion itself. Specifically, we propose a combined approach to generate head movement stabilization on a quadruped robot, using Central Pattern Generators (CPGs) and a genetic algorithm. Head movement is generated by CPGs which are modelled as autonomous differential equations. This approach allows to explicitly specify parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated trajectories according to changes in these parameters. It is therefore easy to combine the CPG with an optimization method. A genetic algorithm determines the best set of parameters that generates the head movement that reduces the head shaking caused by locomotion. Experimental results on a simulated AIBO robot demonstrate that the proposed approach generates head movement that does not eliminate but reduces the one induced by locomotion.