Abstract
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 (Rhs,support, Rhs,swing, Rks,support, and Rks,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 Rhs,support, Rhs,swing, Rks,support, and Rks,swing searched by SaDE is learnt by Fuzzy Inference System (FIS). Desired joint angles can be found without the aid of inverse kinematic model.
Highlights
Many approaches have been adopted for generation of bipedal walking gait
The results show that the modified central pattern generator (CPG) model can achieve a satisfactory performance
Safety factor is set as 0 m since it becomes difficult to search a feasible walking gait for original CPG model if safety factor is set as 0.01 m
Summary
Many approaches have been adopted for generation of bipedal walking gait. Some researches [1,2,3] adopted a simplified dynamic model to generate walking gait calculated through inverse kinematic model which is complex and the computation load is high. Based on the above-mentioned findings, this paper focuses on (1) CPG model for trajectory generation and (2) providing an efficient method to adjust step length. SaDE is chosen as the method for optimizing the walking gait of robot in this paper because (1) its performance is superior and (2) appropriate strategies and parameters are not chosen manually. Look-up table proposed by Yang et al [6] is not adopted in this paper because (1) a lot of memory is occupied if tremendous data is stored and (2) arbitrary step length within specific range cannot be commanded to the robot To deal with this problem, four parameters (Rhs,support, Rhs,swing, Rks,support, and Rks,swing) are added to the modified CPG model and searched by SaD. Desired joint angles can be found without the aid of inverse kinematics
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