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Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming

Many algorithms in probabilistic sampling-based motion planning have been proposed to create a path for a robot in an environment with obstacles. Due to the randomness of sampling, they can efficiently compute the collision-free paths made of segments lying in the configuration space with probabilis...

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Autores principales: Liu, Yizhou, Zha, Fusheng, Li, Mantian, Guo, Wei, Jia, Yunxin, Wang, Pengfei, Zang, Yajing, Sun, Lining
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8381225/
https://www.ncbi.nlm.nih.gov/pubmed/34434099
http://dx.doi.org/10.3389/fnbot.2021.724116
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author Liu, Yizhou
Zha, Fusheng
Li, Mantian
Guo, Wei
Jia, Yunxin
Wang, Pengfei
Zang, Yajing
Sun, Lining
author_facet Liu, Yizhou
Zha, Fusheng
Li, Mantian
Guo, Wei
Jia, Yunxin
Wang, Pengfei
Zang, Yajing
Sun, Lining
author_sort Liu, Yizhou
collection PubMed
description Many algorithms in probabilistic sampling-based motion planning have been proposed to create a path for a robot in an environment with obstacles. Due to the randomness of sampling, they can efficiently compute the collision-free paths made of segments lying in the configuration space with probabilistic completeness. However, this property also makes the trajectories have some unnecessary redundant or jerky motions, which need to be optimized. For most robotics applications, the trajectories should be short, smooth and keep away from obstacles. This paper proposes a new trajectory optimization technique which transforms a polygon collision-free path into a smooth path, and can deal with trajectories which contain various task constraints. The technique removes redundant motions by quadratic programming in the parameter space of trajectory, and converts collision avoidance conditions to linear constraints to ensure absolute safety of trajectories. Furthermore, the technique uses a projection operator to realize the optimization of trajectories which are subject to some hard kinematic constraints, like keeping a glass of water upright or coordinating operation with dual robots. The experimental results proved the feasibility and effectiveness of the proposed method, when it is compared with other trajectory optimization methods.
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spelling pubmed-83812252021-08-24 Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming Liu, Yizhou Zha, Fusheng Li, Mantian Guo, Wei Jia, Yunxin Wang, Pengfei Zang, Yajing Sun, Lining Front Neurorobot Neuroscience Many algorithms in probabilistic sampling-based motion planning have been proposed to create a path for a robot in an environment with obstacles. Due to the randomness of sampling, they can efficiently compute the collision-free paths made of segments lying in the configuration space with probabilistic completeness. However, this property also makes the trajectories have some unnecessary redundant or jerky motions, which need to be optimized. For most robotics applications, the trajectories should be short, smooth and keep away from obstacles. This paper proposes a new trajectory optimization technique which transforms a polygon collision-free path into a smooth path, and can deal with trajectories which contain various task constraints. The technique removes redundant motions by quadratic programming in the parameter space of trajectory, and converts collision avoidance conditions to linear constraints to ensure absolute safety of trajectories. Furthermore, the technique uses a projection operator to realize the optimization of trajectories which are subject to some hard kinematic constraints, like keeping a glass of water upright or coordinating operation with dual robots. The experimental results proved the feasibility and effectiveness of the proposed method, when it is compared with other trajectory optimization methods. Frontiers Media S.A. 2021-08-09 /pmc/articles/PMC8381225/ /pubmed/34434099 http://dx.doi.org/10.3389/fnbot.2021.724116 Text en Copyright © 2021 Liu, Zha, Li, Guo, Jia, Wang, Zang and Sun. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Liu, Yizhou
Zha, Fusheng
Li, Mantian
Guo, Wei
Jia, Yunxin
Wang, Pengfei
Zang, Yajing
Sun, Lining
Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming
title Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming
title_full Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming
title_fullStr Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming
title_full_unstemmed Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming
title_short Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming
title_sort creating better collision-free trajectory for robot motion planning by linearly constrained quadratic programming
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8381225/
https://www.ncbi.nlm.nih.gov/pubmed/34434099
http://dx.doi.org/10.3389/fnbot.2021.724116
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