Neural Gait Synthesis for Autonomous Biped Robots

自主双足机器人的神经步态合成

• 批准号: 9024499
• 负责人: Yuan Zheng
• 金额: $ 10.14万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-12-15 至 1993-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9024499&HistoricalAwards=false
• 关键词: Neural; Gait; Synthesis; Autonomous; Biped

This award funds initial research on integration of methods for reflexive gait synthesis and adaptation to difficult terrains, for a biped walking robot. The research will explore neural- network-based mechanisms for training a central pattern generator to generate a new gait that is learned by a terrain-monitoring learning and adaptation unit. It will also study the convergence properties of backpropagation and reinforcement learning mechanisms for use in the learning and adaptation unit. The long-term goal of this research in biped robotics is to achieve more agility and mobility than is possible in multilegged walking machines or other forms of machine locomotion, and ultimately to provide capability for mobile robots to augment or replace humans in hazardous operations or in rough terrain.

该奖项资助双足步行机器人的反射步态合成和适应困难地形的方法整合的初步研究。 该研究将探索基于神经网络的机制，用于训练中央模式生成器以生成由地形监测学习和适应单元学习的新步态。 它还将研究用于学习和适应单元的反向传播和强化学习机制的收敛特性。 这项双足机器人研究的长期目标是实现比多足步行机或其他形式的机器运动更高的敏捷性和移动性，并最终为移动机器人提供在危险操作或崎岖地形中增强或替代人类的能力。