This paper proposed a method to improve the walking 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 the modified central pattern generator (CPG) model. The GAOFSF is modified 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 ( , , , and ) 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 , , , and searched by SaDE is learnt by Fuzzy Inference System (FIS). Desired joint angles can be found without the aid of inverse kinematic model. 1. Introduction Recently, many approaches have been adopted for generation of bipedal walking gait. Some researches [1–3] adopted a simplified dynamic model to generate walking gait calculated through inverse kinematic model which is complex and hence the computation load is high. Inspired by neural science, some researchers investigated central pattern generator (CPG). The prime reason for arousing their interest is that CPG models provide several parameters for modulation of locomotion, such as step stride and rhythm, and are suitable to integrate feedback sensors. Hence, a good interaction between the robot and the environment can be achieved [4]. According to Ijspeert [4], CPG becomes more and more popular in robot community. Taga et al. [5] integrated feedbacks with neural oscillators for unpredicted environment. Yang et al. [6], Shafii et al. [7], and Yazdi et al. [8] utilized TFS to formulate ZMP-based CPG model as the basic walking pattern of bipedal robot. Or [9] presented a hybrid CPG-ZMP control system for flexible spine humanoid robot. Aoi and Tsuchiya [10] proposed a locomotion control system based on CPG model for straight and curved walking. Farzenah et al. [11] noted that many researches on CPG model are designed for specific motion only and thus cannot generate
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