An efficient stochastic constrained path planner for redundant manipulators

Aparicio, AG; Miro, JV

An efficient stochastic constrained path planner for redundant manipulators

Aparicio, AG Miro, JV

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Applied Sciences (Switzerland), 2021, 11, (22)
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Aparicio, AG
dc.contributor.author	Miro, JV
dc.date.accessioned	2022-02-01T07:41:19Z
dc.date.available	2022-02-01T07:41:19Z
dc.date.issued	2021-11-01
dc.identifier.citation	Applied Sciences (Switzerland), 2021, 11, (22)
dc.identifier.issn	1454-5101
dc.identifier.issn	2076-3417
dc.identifier.uri	http://hdl.handle.net/10453/154047
dc.description.abstract	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.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Applied Sciences (Switzerland)
dc.relation.isbasedon	10.3390/app112210636
dc.rights	info:eu-repo/semantics/openAccess
dc.title	An efficient stochastic constrained path planner for redundant manipulators
dc.type	Journal Article
utslib.citation.volume	11
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2022-02-01T07:41:16Z
pubs.issue	22
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	22

Abstract:

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.

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http://hdl.handle.net/10453/154047