A Truncated Fourier Series with genetic algorithm for the control of biped locomotion

Humanoid research has made notable progress during the past 25 years. However, currently most humanoids use the ZMP (zero moment point) for control of bipedal locomotion, which requires precise modeling and actuation with high control gains. On the contrary, researchers do not rely on such precise modeling and actuation. In this paper we have tried to introduce a novel method for the evolution of walking behavior in a simulated humanoid robot with up to 22 degrees of freedom. In this method a modified truncated Fourier series (TFS) generates walking angular trajectories and it is optimized by genetic algorithm. By studying human walking TFS parameters have also been reduced. As a test-bed, we chose Robocup 3D soccer simulation environment (SPARK) and implemented our method in MRL 3D team´s agents. Experimental results show that training of the robot can be successfully performed by our method, thus allowing the biped robot to walk fast, stably and straightly.