Synchronization and Gait Adaptation in Evolving Hexapod Robots

Abstract

In this paper we present a distributed control architecture for a simulated hexapod robot with twelve degrees of freedom consisting of six homogeneous neural modules controlling the six corresponding legs that only have access to local sensory information and that coordinate by exchanging signals that diffuse in space like gaseous neuro-trasmitters. The free parameters of the neural modules are evolved and are selected on the basis of the distance travelled by the robot. Obtained results indicate how the six neural controllers are able to coordinate so to produce an effective walking behaviour and to adapt on the fly by selecting the gait that is most appropriate to the current robot/environmental circumstances. The analysis of the evolved neural controllers indicates that the six neural controllers synchronize and converge on an appropriate gait on the basis of extremely simple control mechanisms and that the effects of the physical interaction with the environment are exploited to coordinate and to converge on a tripod or tetrapod gait on the basis of the current circumstances.