Evolutionary locomotion control of a hexapod robot using particle swarm optimized fuzzy controller

This paper proposes hexapod robot locomotion control using a fuzzy controller (FC) learned through particle swarm optimization (PSO). The gait of each leg in the hexapod robot is controlled using a finite state machine so that the robot moves straight forward when the swing amplitude of each leg is set to be identical. This paper proposes locomotion control of the hexapod robot using an FC for applications in different environments. Given the robot state, the FC controls the robot orientation by changing the swing amplitude of the middle leg on each side of the robot. As to the design of the FC, all of the free parameters in which are learned through PSO, which avoids the time-consuming manual design task. The proposed PSO-based FC approach is applied to two hexapod robot locomotion control problems: obstacle boundary-following control and circle-following control. Simulations are conducted to verify the effectiveness of the locomotion control approach.