Adaptation of quadruped gaits using surface classification and gait optimization

An evolutionary computational approach for a gait generation of a quadruped robot autonomously generates a gait that adapts in an environment. In this approach, a fitness function that measures a performance of the gait is defined and parameters are optimized by maximizing or minimizing the function with evolutionary computation algorithms. However the previous research only has considered the optimization on an environment. In this paper, we suggest a gait adaptation method for a quadruped robot using a terrain classification and a gait optimization for an adaptation on various surfaces. The surfaces for the adaptation are learnt with a classification algorithm and a gait parameter on each surface is optimized with Particle Swarm Optimization (PSO). After the learning and the optimization, the classifier is used for classifying a surface that a robot is located and an optimized gait parameter is selected based on the classification result for the adaptation. The adaptation framework, a feature design and a filtering method for a classifier and a gait design for a quadruped robot are proposed in this paper. The proposed method was verified in a realistic 3D simulator and it successfully classified surfaces and selected optimized gaits for adaptations.