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