Using Cyclic Genetic Algorithms to learn gaits for an actual quadruped robot

Abstract

It is a difficult task to generate optimal walking gaits for mobile legged robots. Generating and coordinating an optimal gait involves continually repeating a series of actions in order to create a sustained movement. In this work, we present the use of a Cyclic Genetic Algorithm (CGA) to learn near optimal gaits for an actual quadruped servo-robot with three degrees of movement per leg. This robot was used to create a simulation model of the movement and states of the robot which included the robot's unique features and capabilities. The CGA used this model to learn gaits that were optimized for this particular robot. Tests done in simulation show the success of the CGA in evolving gait control programs and tests on robot show that these control programs produce reasonable gaits.