Using Motion Primitives in Probabilistic Sample-Based Planning for Humanoid Robots

被引：0

作者：

Hauser, Kris ^{[1
]}

Bretl, Timothy ^{[1
]}

Harada, Kensuke ^{[2
]}

Latombe, Jean-Claude ^{[1
]}

机构：

[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA

[2] Natl Inst Adv Ind Sci & Technol, Tokyo, Japan

来源：

ALGORITHMIC FOUNDATION OF ROBOTICS VII | 2008年 / 47卷

基金：

美国国家科学基金会;

关键词：

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a method of computing efficient and natural-looking motions for humanoid robots walking on varied terrain. It uses a small set of high-quality motion primitives (such as a fixed gait on flat ground) that have been generated offline. But rather than restrict motion to these primitives, it uses them to derive a sampling strategy for a probabilistic, sample-based planner. Results in simulation on several different terrains demonstrate a reduction in planning time and a, marked increase in motion quality.

引用

页码：507 / +

页数：3

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