An improved ZMP-based CPG model of bipedal robot walking searched by SaDE

This paper proposed amethod to improve thewalking behavior of bipedal robot with adjustable step length.Objectives of this paper are threefold. (1) Genetic Algorithm Optimized Fourier Series Formulation (GAOFSF) is modified to improve its performance. (2) Self-adaptive Differential Evolutionary Algorithm (SaDE) is applied to search feasible walking gait. (3) An efficient method is proposed for adjusting step length based on themodified central pattern generator (CPG)model.TheGAOFSF ismodified to ensure that trajectories generated are continuous in angular position, velocity, and acceleration. After formulation of the modified CPG model, SaDE is chosen to optimize walking gait (CPG model) due to its superior performance.Through simulation results, dynamic balance of the robot with modified CPG model is better than the original one. In this paper, four adjustable factors (��hs,support, ��hs,swing,��ks,support, and��ks,swing) are added to the joint trajectories.Through adjusting these four factors, joint trajectories are changed and hence the step length achieved by the robot. Finally, the relationship between (1) the desired step length and (2) an appropriate set of ��hs,support, ��hs,swing, ��ks,support, and ��ks,swing searched by SaDE is learnt by Fuzzy Inference System(FIS). Desired joint angles can be found without the aid of inverse kinematic model.