Heterogeneous Dimensionality Reduction for Efficient Motion Planning in High-Dimensional Spaces

Yu, H; Lu, W; Han, Y; Liu, D; Zhang, M

Heterogeneous Dimensionality Reduction for Efficient Motion Planning in High-Dimensional Spaces

Yu, H Lu, W Han, Y Liu, D

Zhang, M

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Access, 2020, 8, pp. 42619-42632
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Yu, H
dc.contributor.author	Lu, W
dc.contributor.author	Han, Y
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582
dc.contributor.author	Zhang, M
dc.date.accessioned	2021-01-15T05:06:59Z
dc.date.available	2021-01-15T05:06:59Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Access, 2020, 8, pp. 42619-42632
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/145451
dc.description.abstract	© 2013 IEEE. Increasing the dimensionality of the configuration space quickly makes trajectory planning computationally intractable. This paper presents an efficient motion planning approach that exploits the heterogeneous low-dimensional structures of a given planning problem. These heterogeneous structures are obtained via a Dirichlet process (DP) mixture model and together cover the entire configuration space, resulting in more dimensionality reduction than single-structure approaches from the existing literature. Then, a unified low-dimensional trajectory optimization problem is formulated based on the obtained heterogeneous structures and a proposed transversality condition which is further solved via SQP in our implementation. The positive results demonstrate the feasibility and efficiency of our trajectory planning approach on an autonomous underwater vehicle (AUV) and a high-dimensional intervention autonomous underwater vehicle (I-AUV) in cluttered 3D environments.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/LP150100935
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2020.2977379
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Heterogeneous Dimensionality Reduction for Efficient Motion Planning in High-Dimensional Spaces
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-01-15T05:06:39Z
pubs.publication-status	Published
pubs.volume	8

Abstract:

© 2013 IEEE. Increasing the dimensionality of the configuration space quickly makes trajectory planning computationally intractable. This paper presents an efficient motion planning approach that exploits the heterogeneous low-dimensional structures of a given planning problem. These heterogeneous structures are obtained via a Dirichlet process (DP) mixture model and together cover the entire configuration space, resulting in more dimensionality reduction than single-structure approaches from the existing literature. Then, a unified low-dimensional trajectory optimization problem is formulated based on the obtained heterogeneous structures and a proposed transversality condition which is further solved via SQP in our implementation. The positive results demonstrate the feasibility and efficiency of our trajectory planning approach on an autonomous underwater vehicle (AUV) and a high-dimensional intervention autonomous underwater vehicle (I-AUV) in cluttered 3D environments.

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http://hdl.handle.net/10453/145451