Gait Optimization on a Humanoid Robot using Particle Swarm Optimization

Abstract

This paper describes the application of Particle Swarm Optimization (PSO) for gait optimization on a humanoid robot. The biped gait is modeled by a number of parameterizable trajectories. To achieve omni-directional walking, different sets of gait parameters are optimized for specific walk directions and interpolated later. By using a fitness test based on an acceleration walk, the optimized sets of parameters are suitable for a wide range of walk speeds. We tested the applicability of the approach by performing gait optimization for several walk directions on a modified Kondo KHR-1 robot.