Probabilistic search for a moving target in an indoor environment

Lau, H; Huang, S; Dissanayake, G

Probabilistic search for a moving target in an indoor environment

Lau, H Huang, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Intelligent Robots and Systems, 2006, pp. 3393 - 3398
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Lau, H	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	IEEE International Conference on Intelligent Robots and Systems, 2006, pp. 3393 - 3398	en_US
dc.identifier.isbn	142440259X	en_US
dc.identifier.isbn	9781424402595	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2727
dc.description.abstract	We consider a search for a target moving within a known indoor environment partitioned into interconnected regions of varying sizes. The knowledge of target location is described as a probability distribution over the regions, and the searcher can only move from one region to another as the structure allows. The objective is to find a feasible path through the regions that maximizes the probability of locating the target within fixed time. This problem generalizes the existing optimal searcher path problem (OSP) by additionally stipulating a minimum amount of time that a finite-speed searcher must spend to travel through a region before reaching the next. We propose a technique to obtain the upper bound of detection for solving the problem in a branch and bound framework. Comparisons show that the technique is also superior to known bounding methods for the original optimal searcher path problem. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Intelligent Robots and Systems	en_US
dc.relation.isbasedon	10.1109/IROS.2006.282575	en_US
dc.title	Probabilistic search for a moving target in an indoor environment	en_US
dc.type	Conference Proceeding
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Beijing, China	en_US
dc.location.activity	Beijing, PEOPLES R CHINA
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.publication-status	Published	en_US

Abstract:

We consider a search for a target moving within a known indoor environment partitioned into interconnected regions of varying sizes. The knowledge of target location is described as a probability distribution over the regions, and the searcher can only move from one region to another as the structure allows. The objective is to find a feasible path through the regions that maximizes the probability of locating the target within fixed time. This problem generalizes the existing optimal searcher path problem (OSP) by additionally stipulating a minimum amount of time that a finite-speed searcher must spend to travel through a region before reaching the next. We propose a technique to obtain the upper bound of detection for solving the problem in a branch and bound framework. Comparisons show that the technique is also superior to known bounding methods for the original optimal searcher path problem. © 2006 IEEE.

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http://hdl.handle.net/10453/2727