Learning Area Coverage for a Self-Sufficient Hexapod Robot Using a Cyclic Genetic Algorithm

Abstract

Self-sufficient autonomous robots are able to perform independent tasks while maintaining enough energy to function. We develop a self-sufficient robot control system where a cyclic genetic algorithm (GA) is used to learn the control program for a hexapod robot equipped with a quick charge power supply. This robot uses high capacitance capacitors for its onboard power storage and a sensor system to detect power need related information. A detailed simulation is developed, to be used by a cyclic GA to learn control programs for the robot. These programs enable it to perform area coverage and periodically return to a recharging station to maintain power. In this paper, we expound on previous research and report the transfer of the complete simulated self-sufficient behavior to the physical robot and colony power supply system, where tests have been conducted to confirm the viability of our approach.