Quadruped gait learning using cyclic genetic algorithms

Abstract

Abstract-Generating walking gaits for legged robots is a challenging task. Gait generation with proper leg coordination involves a series of actions that are continually repeated to create sustained movement. In this paper we present the use of a Cyclic Genetic Algorithm (CGA) to learn gaits for a quadruped servorobot with three degrees of movement per leg. An actual robot was used to generate a simulation model of the movement and states of the robot. The CGA used the robot's unique features and capabilities to develop gaits specific for that particular robot. Tests done in simulation show the success of the CGA in evolving a reasonable control program and preliminary tests on the robot show that the resultant control program produces a suitable gait.

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

“…In previous work [6] we tested the effectiveness of a similar CGA in evolving walking gaits for a robot with more complex legs. The servo-robot used has four legs, each of which has three degrees of freedom, in/out, up/down, and forward/back.…”