An improved geodesic algorithm for trajectory planning of multi-joint robots

The present work proposes an improved geodesic algorithm for the trajectory planning of multi-joint robots. First, all of the joint variables are chosen to set up a generalized local coordinate system for the product of the positional space and the orientational one. Second, by defining a new Rieman...

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Main Authors: Youdong Chen, Ling Li, Wei Tang
Format: Article
Language:English
Published: SAGE Publishing 2016-09-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.1177/1729881416657742
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spelling doaj-e4465642ffb14e7ebaf0dc678a1f93e92020-11-25T03:34:20ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142016-09-011310.1177/172988141665774210.1177_1729881416657742An improved geodesic algorithm for trajectory planning of multi-joint robotsYoudong Chen0Ling Li1Wei Tang2 The school of Mechanical Engineering and Automation, Beihang University, Beijing, China The school of Mechanical Engineering and Automation, Beihang University, Beijing, China Hefei University of Technology, Hefei, Anhui, ChinaThe present work proposes an improved geodesic algorithm for the trajectory planning of multi-joint robots. First, all of the joint variables are chosen to set up a generalized local coordinate system for the product of the positional space and the orientational one. Second, by defining a new Riemannian metric that contains both the positional and rational parameters, the traditional geodesic algorithm is improved so that it becomes capable of planning robot trajectories that include both the position and the orientation. To demonstrate the effectiveness of the improvement, trajectories are planned for two typical joint robots: one being a planar 3R and the other a spatial RRPP. It is verified that the improved algorithm can generate not only smooth motions for the joints but also smooth and accurate motions for the end-effector. The improved algorithm applies to multi-joint robots with no more than six degrees of freedom.https://doi.org/10.1177/1729881416657742
collection DOAJ
language English
format Article
sources DOAJ
author Youdong Chen
Ling Li
Wei Tang
spellingShingle Youdong Chen
Ling Li
Wei Tang
An improved geodesic algorithm for trajectory planning of multi-joint robots
International Journal of Advanced Robotic Systems
author_facet Youdong Chen
Ling Li
Wei Tang
author_sort Youdong Chen
title An improved geodesic algorithm for trajectory planning of multi-joint robots
title_short An improved geodesic algorithm for trajectory planning of multi-joint robots
title_full An improved geodesic algorithm for trajectory planning of multi-joint robots
title_fullStr An improved geodesic algorithm for trajectory planning of multi-joint robots
title_full_unstemmed An improved geodesic algorithm for trajectory planning of multi-joint robots
title_sort improved geodesic algorithm for trajectory planning of multi-joint robots
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2016-09-01
description The present work proposes an improved geodesic algorithm for the trajectory planning of multi-joint robots. First, all of the joint variables are chosen to set up a generalized local coordinate system for the product of the positional space and the orientational one. Second, by defining a new Riemannian metric that contains both the positional and rational parameters, the traditional geodesic algorithm is improved so that it becomes capable of planning robot trajectories that include both the position and the orientation. To demonstrate the effectiveness of the improvement, trajectories are planned for two typical joint robots: one being a planar 3R and the other a spatial RRPP. It is verified that the improved algorithm can generate not only smooth motions for the joints but also smooth and accurate motions for the end-effector. The improved algorithm applies to multi-joint robots with no more than six degrees of freedom.
url https://doi.org/10.1177/1729881416657742
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