Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring

Ghaffari Jadidi, M; Valls Miro, J; Dissanayake, G

Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring

Ghaffari Jadidi, M

Valls Miro, J

Dissanayake, G

Permalink

Publisher:: SAGE PUBLICATIONS LTD
Publication Type:: Journal Article
Citation:: International Journal of Robotics Research, 2019, 38, (6), pp. 658-685
Issue Date:: 2019-05-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted manuscriptAdobe PDF (2.45 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ghaffari Jadidi, M https://orcid.org/0000-0002-4734-4295
dc.contributor.author	Valls Miro, J https://orcid.org/0000-0002-0083-7797
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680
dc.date.accessioned	2020-05-12T03:39:51Z
dc.date.available	2020-05-12T03:39:51Z
dc.date.issued	2019-05-01
dc.identifier.citation	International Journal of Robotics Research, 2019, 38, (6), pp. 658-685
dc.identifier.issn	0278-3649
dc.identifier.issn	1741-3176
dc.identifier.uri	http://hdl.handle.net/10453/140666
dc.description.abstract	© The Author(s) 2019. We propose a sampling-based motion-planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly exploring information-gathering algorithms and benefits from the advantages of sampling-based optimal motion-planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information-gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle.
dc.language	English
dc.publisher	SAGE PUBLICATIONS LTD
dc.relation.ispartof	International Journal of Robotics Research
dc.relation.isbasedon	10.1177/0278364919844575
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	International Journal of Robotics Research, 2019, 38, (6), pp. 658-685. Copyright © 2019 SAGE PUBLICATIONS LTD. DOI: https://dx.doi.org/10.1177/0278364919844575	en_US
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring
dc.type	Journal Article
utslib.citation.volume	38
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-12T03:39:45Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	38
utslib.start-page	658
utslib.citation.issue	6

Abstract:

© The Author(s) 2019. We propose a sampling-based motion-planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly exploring information-gathering algorithms and benefits from the advantages of sampling-based optimal motion-planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information-gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/140666