Evolving Feasible Gaits for a Hexapod Robot by Reducing the Space of Possible Solutions

The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot mechanics and drives on a flat terrain. We show in a series of evolutionary simulations how a gradual reduction of the permissible state space of the movements of the robot legs leads to the proper leg trajectories for a hexapod robot. This strategy enables the learning system to discover feasible gaits, using only simple dependencies between the control signals of the legs and a simple fitness function. Finally, a stable and fast tripod gait evolved in simulation is shown in an experiment on the real walking robot Ragno.