Multistage bayesian autonomy for high-precision operation in a large field

Hodges, J; Attia, T; Arukgoda, J; Kang, C; Cowden, M; Doan, L; Ranasinghe, R; Abdelatty, K; Dissanayake, G; Furukawa, T

Multistage bayesian autonomy for high-precision operation in a large field

Hodges, J Attia, T Arukgoda, J Kang, C Cowden, M Doan, L Ranasinghe, R

Abdelatty, K Dissanayake, G

Furukawa, T

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Publication Type:: Journal Article
Citation:: Journal of Field Robotics, 2019, 36 (1), pp. 183 - 203
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Hodges, J	en_US
dc.contributor.author	Attia, T	en_US
dc.contributor.author	Arukgoda, J	en_US
dc.contributor.author	Kang, C	en_US
dc.contributor.author	Cowden, M	en_US
dc.contributor.author	Doan, L	en_US
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.contributor.author	Abdelatty, K	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Furukawa, T	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Journal of Field Robotics, 2019, 36 (1), pp. 183 - 203	en_US
dc.identifier.issn	1556-4959	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128665
dc.description.abstract	© 2018 Wiley Periodicals, Inc. This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high-precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high-precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real-world problems. A successful demonstration using the UAV is also presented.	en_US
dc.relation.ispartof	Journal of Field Robotics	en_US
dc.relation.isbasedon	10.1002/rob.21829	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Multistage bayesian autonomy for high-precision operation in a large field	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	36	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access	*
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

© 2018 Wiley Periodicals, Inc. This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high-precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high-precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real-world problems. A successful demonstration using the UAV is also presented.

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http://hdl.handle.net/10453/128665