An Efficient Stochastic Constrained Path Planner for Redundant Manipulators

This brief proposes a novel stochastic method that exploits the particular kinematics of mechanisms with redundant actuation and a well-known manipulability measure to track the desired end-effector task-space motion in an efficient manner. Whilst closed-form optimal solutions to maximise manipulabi...

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Autores principales:	Arturo Gil Aparicio, Jaime Valls Miro
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	manipulator motion planning manipulability stochastic planner Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/00d753fd98d64984a6c12593634ebfc1
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id	oai:doaj.org-article:00d753fd98d64984a6c12593634ebfc1
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spelling	oai:doaj.org-article:00d753fd98d64984a6c12593634ebfc12021-11-25T16:34:00ZAn Efficient Stochastic Constrained Path Planner for Redundant Manipulators10.3390/app1122106362076-3417https://doaj.org/article/00d753fd98d64984a6c12593634ebfc12021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10636https://doaj.org/toc/2076-3417This brief proposes a novel stochastic method that exploits the particular kinematics of mechanisms with redundant actuation and a well-known manipulability measure to track the desired end-effector task-space motion in an efficient manner. Whilst closed-form optimal solutions to maximise manipulability along a desired trajectory have been proposed in the literature, the solvers become unfeasible in the presence of obstacles. A manageable alternative to functional motion planning is thus proposed that exploits the inherent characteristics of null-space configurations to construct a generic solution able to improve manipulability along a task-space trajectory in the presence of obstacles. The proposed Stochastic Constrained Optimization (SCO) solution remains close to optimal whilst exhibiting computational tractability, being an attractive proposition for implementation on real robots, as shown with results in challenging simulation scenarios, as well as with a real 7R Sawyer manipulator, during surface conditioning tasks.Arturo Gil AparicioJaime Valls MiroMDPI AGarticlemanipulator motion planningmanipulabilitystochastic plannerTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10636, p 10636 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	manipulator motion planning manipulability stochastic planner Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	manipulator motion planning manipulability stochastic planner Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Arturo Gil Aparicio Jaime Valls Miro An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
description	This brief proposes a novel stochastic method that exploits the particular kinematics of mechanisms with redundant actuation and a well-known manipulability measure to track the desired end-effector task-space motion in an efficient manner. Whilst closed-form optimal solutions to maximise manipulability along a desired trajectory have been proposed in the literature, the solvers become unfeasible in the presence of obstacles. A manageable alternative to functional motion planning is thus proposed that exploits the inherent characteristics of null-space configurations to construct a generic solution able to improve manipulability along a task-space trajectory in the presence of obstacles. The proposed Stochastic Constrained Optimization (SCO) solution remains close to optimal whilst exhibiting computational tractability, being an attractive proposition for implementation on real robots, as shown with results in challenging simulation scenarios, as well as with a real 7R Sawyer manipulator, during surface conditioning tasks.
format	article
author	Arturo Gil Aparicio Jaime Valls Miro
author_facet	Arturo Gil Aparicio Jaime Valls Miro
author_sort	Arturo Gil Aparicio
title	An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
title_short	An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
title_full	An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
title_fullStr	An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
title_full_unstemmed	An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
title_sort	efficient stochastic constrained path planner for redundant manipulators
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/00d753fd98d64984a6c12593634ebfc1
work_keys_str_mv	AT arturogilaparicio anefficientstochasticconstrainedpathplannerforredundantmanipulators AT jaimevallsmiro anefficientstochasticconstrainedpathplannerforredundantmanipulators AT arturogilaparicio efficientstochasticconstrainedpathplannerforredundantmanipulators AT jaimevallsmiro efficientstochasticconstrainedpathplannerforredundantmanipulators
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An Efficient Stochastic Constrained Path Planner for Redundant Manipulators

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