| Summary: | Images captured by cameras mounted on the head of walking robots show oscillations due to the locomotion itself. These isturbances difficult the achievement of robotic tasks that rely on visual information. In this work, we tackle this problematic and propose a combined approach based on a controller architecture that is able to generate locomotion for a quadruped robot and a global optimization algorithm to generate head movement stabilization. The movement controllers are biologically inspired in the concept of Central Pattern Generators that are modeled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows to explicitly specify parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. An elitist Electromagnetism-like algorithm searches for the best set of parameters that generates the head movement in order to reduce the head shaking caused by locomotion. Optimization is done off-line according to the head movement induced by the locomotion when no stabilization procedure was performed. Experiments in a walking AIBO robot demonstrate that the proposed approach generates head movement that reduces significantly the one induced by locomotion.
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