Gait Optimization of a Quadruped Robot Using Evolutionary Computation

Abstract

Evolutionary Computation (EC) has strengths in terms of computation for gait optimization. However, conventional evolutionary algorithms use typical gait parameters such as step length and swing height, which limit the trajectory deformation for optimization of the foot trajectory. Furthermore, the quantitative index of fitness convergence is insufficient. In this paper, we perform gait optimization of a quadruped robot using foot placement perturbation based on EC. The proposed algorithm has an atypical solution search range, which is generated by independent manipulation of each placement that forms the foot trajectory. A convergence index is also introduced to prevent premature cessation of learning. The conventional algorithm and the proposed algorithm are applied to a quadruped robot; walking performances are then compared by gait simulation. Although the two algorithms exhibit similar computation rates, the proposed algorithm shows better fitness and a wider search range. The evolutionary tendency of the walking trajectory is analyzed using the optimized results, and the findings provide insight into reliable leg trajectory design.