基于改进差分进化算法优化的仿人机器人自适应进化神经网络步态生成

T. T. Huan, Cao Van Kien, H. Anh
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引用次数: 3

摘要

本文介绍了一种新的双足机器人步态生成方法,其目的是控制类人机器人在平面平台上更自然、更稳定地行走。基于自适应进化神经模型(AENM)的动态双足步态生成器,采用改进的差分进化(MDE)优化算法对其进行最优识别。通过与遗传算法(GA)和粒子群算法(PSO)的比较,验证了该方法的有效性。以小型人形机器人为原型,对所提出的MDE算法和其他算法的性能进行了测试。对比结果表明,所提出的神经AENM模型是一种鲁棒、精确的两足步态生成方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Evolutionary Neural Network Gait Generation for Humanoid Robot Optimized with Modified Differential Evolution Algorithm
This paper introduces a novel approach for the biped robot gait generation which aims to control humanoid robot to walk more naturally and stably on a flat platform. The dynamic biped gait generator created by the novel adaptive evolutionary neural model (AENM) that is optimally identified with the proposed modified differential evolution (MDE) optimization algorithm. The comparison results with genetic algorithm (GA) and particle swarm optimisation (PSO) demonstrated the effectiveness of proposed MDE method. The prototype small sized humanoid robot is used to test the performance of the proposed MDE algorithm and other algorithms. The comparison results demonstrate that the new proposed neural AENM model proves an effective approach for a robust and precise biped gait generation.
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