Running trajectory generation for humanoid robot

Abstract

Using minimum energy function as an optimization tool, a stable trajectory for a humanoid robot to run is achieved with the genetic algorithm. Compared with other methods, it can reduce consumed energy during the whole process. The proposed method is adaptive to various step lengths and step times for running and could be applied in other missions, such as walking, overcoming obstacles. The system stability is improved by controlling shoulders and elbows to compensate the errors. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.