Optimizing NAO humanoid walking using ABC algorithm

F. Halataei, A. Kayhani
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引用次数: 2

Abstract

Humanoid locomotion of biped robots is an interesting research area due to its similarity to human walking. Furthermore, wide application of humanoid locomotion especially in robocup competitions made it more interesting for several researchers in the fields of electronics, mechanics and computer science, although it has its own complexity. This paper proposes a novel approach that optimizes the walking speed and stability for NAO biped robots. This method has been implemented on simulated NAO robot in Robocup 3D simulation environment (rcsssever3d). We have used Artificially Bee Colony (ABC) algorithm in order to achieve full stability and considerable fast walking speed without any external disturbances. Experimental results show that using this approach also, significantly decreased optimization time in order to get optimal values for parameters in our fitness function. At end a comparison has been made between the described optimization solution and GA optimization, while same walking method and foot trajectory formula has been used.
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基于ABC算法的NAO类人步行优化
由于双足机器人的类人运动与人类行走的相似性,它是一个有趣的研究领域。此外,类人运动的广泛应用,特别是在机器人世界杯比赛中,使得电子、力学和计算机科学领域的一些研究人员对它更感兴趣,尽管它有其自身的复杂性。提出了一种优化NAO双足机器人行走速度和稳定性的新方法。该方法已在Robocup三维仿真环境(rsssever3d)中对仿真的NAO机器人进行了实现。我们采用人工蜂群(artificial Bee Colony, ABC)算法,在不受外界干扰的情况下实现了完全稳定和相当快的行走速度。实验结果表明,使用该方法可以显著减少优化时间,以获得适应度函数中参数的最优值。最后将所述优化方案与采用相同行走方法和足部轨迹公式的遗传算法优化方案进行了比较。
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