Abstract

This paper presents a trajectory generation algorithm for robots which can walk like human with movable foot and active toe. The proposed algorithm allows smooth transition between walking phases namely, single and double support phases. A neural network approach is used for solving inverse kinematics so that the biped robot follows the ankle and hip trajectories to walk. Zero moment point (ZMP) stability is ensured by taking into account the upper body movements along with the planned motion trajectories. Here, we analyze the effect of lateral upper body motion on ZMP stability. Different types of trajectories for upper body are generated, and the one which ensured the most stable locomotion is identified.

中文翻译：

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使用人工神经网络进行轨迹跟踪以稳定上肢运动的类人步态

摘要

本文提出了一种机器人的轨迹生成算法，该机器人可以像人一样行走，具有活动脚和活动脚趾。所提出的算法允许在步行阶段即单和双支持阶段之间平滑过渡。使用神经网络方法来解决逆运动学问题，从而使两足动物机器人跟随脚踝和臀部的轨迹行走。通过考虑上半身的运动以及计划的运动轨迹，确保零力矩点（ZMP）的稳定性。在这里，我们分析了上半身横向运动对ZMP稳定性的影响。生成了上身的不同类型的轨迹，并确定了确保最稳定的运动的轨迹。