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.

进化计算（EC）在步态优化的计算方面具有优势。然而，传统的进化算法使用典型的步态参数，例如步长和挥杆高度，这限制了轨迹变形以优化脚部轨迹。此外，适应度收敛的定量指标不足。在本文中，我们使用基于EC的脚部位置扰动对四足机器人进行了步态优化。所提出的算法具有非典型解搜索范围，该范围是通过独立控制形成脚部轨迹的每个位置生成的。还引入了收敛指数以防止过早停止学习。将传统算法和提出的算法应用于四足机器人。然后通过步态模拟比较步行表现。尽管两种算法具有相似的计算速率，但所提出的算法具有更好的适应性和更宽的搜索范围。使用优化结果分析了步行轨迹的演变趋势，这些发现为可靠的腿部轨迹设计提供了见识。