Application of Genetic Algorithms for biped robot gait synthesis optimization during walking and going up-stairs

Abstract

Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru I'humanoid robot, which is under development in our laboratory. The evaluation by simulations shows that the proposed method has a good performance.