Minimally Invasive Social Navigation

Kiss, SH; To, KYC; Yoo, C; Fitch, R; Alempijevic, A

Minimally Invasive Social Navigation

Kiss, SH To, KYC Yoo, C

Fitch, R

Alempijevic, A

Permalink

Publisher:: ARAA
Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation 2019, 2019, pp. 1 - 7 (7)
Issue Date:: 2019-12-09

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript versionAdobe PDF (4.59 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Kiss, SH	en_US
dc.contributor.author	To, KYC	en_US
dc.contributor.author	Yoo, C https://orcid.org/0000-0002-2773-0527	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.contributor.author	Alempijevic, A https://orcid.org/0000-0002-1769-8041	en_US
dc.date	2019-12-09	en_US
dc.date.available	2019-11-13	en_US
dc.date.issued	2019-12-09	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation 2019, 2019, pp. 1 - 7 (7)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138030
dc.description.abstract	Integrating mobile robots into human society involves the fundamental problem of navigation in crowds. This problem has been studied by considering the behaviour of humans at the level of individuals, but this representation limits the computational efficiency of motion planning algorithms. We explore the idea of representing a crowd as a flow field, and propose a formal definition of path quality based on the concept of invasiveness; a robot should attempt to navigate in a way that is minimally invasive to humans in its environment. We develop an algorithmic framework for path planning based on this definition and present experimental results that indicate its effectiveness. These results open new algorithmic questions motivated by the flow field representation of crowds and are a necessary step on the path to end-to-end implementations.	en_US
dc.publisher	ARAA	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation 2019	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Minimally Invasive Social Navigation	en_US
dc.type	Conference Proceeding
utslib.location	Sydney, Australia	en_US
utslib.location.activity	Adelaide, Australia	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	091302 Automation and Control 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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.consider-herdc	false	en_US
pubs.finish-date	2019-12-11	en_US
pubs.place-of-publication	Sydney, Australia	en_US
pubs.publication-status	Published online	en_US
pubs.start-date	2019-12-09	en_US

Abstract:

Integrating mobile robots into human society involves the fundamental problem of navigation in crowds. This problem has been studied by considering the behaviour of humans at the level of individuals, but this representation limits the computational efficiency of motion planning algorithms. We explore the idea of representing a crowd as a flow field, and propose a formal definition of path quality based on the concept of invasiveness; a robot should attempt to navigate in a way that is minimally invasive to humans in its environment. We develop an algorithmic framework for path planning based on this definition and present experimental results that indicate its effectiveness. These results open new algorithmic questions motivated by the flow field representation of crowds and are a necessary step on the path to end-to-end implementations.

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

http://hdl.handle.net/10453/138026