Efficient learning of motion patterns for robots

Sehestedt, S; Kodagoda, S; Alempijevic, A; Dissanayake, G

Efficient learning of motion patterns for robots

Sehestedt, S Kodagoda, S

Alempijevic, A

Dissanayake, G

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2009 Australasian Conference on Robotics and Automation, ACRA 2009, 2009
Issue Date:: 2009-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (707.46 kB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Sehestedt, S	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Alempijevic, A https://orcid.org/0000-0002-1769-8041	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Proceedings of the 2009 Australasian Conference on Robotics and Automation, ACRA 2009, 2009	en_US
dc.identifier.isbn	9780980740400	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11169
dc.description.abstract	In this work we present a novel approach to learning dynamics of an environment perceived by a mobile robot. More precisely, we are interested in general motion patterns occurring in the environment rather than object dependent ones. A sampling algorithm is used to update a sample set, which represents observed dynamics, using the Bayes rule. From this set of samples a Hidden Markov Model is learnt online, which allows fast and efficient matching and prediction in the learnt model. Such models are useful for a number of tasks such as path planning, localisation and compliant motion. The approach is validated through simulation as well as experiments.	en_US
dc.relation.ispartof	Proceedings of the 2009 Australasian Conference on Robotics and Automation, ACRA 2009	en_US
dc.title	Efficient learning of motion patterns for robots	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Sydney, Australia	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	open_access
pubs.publication-status	Published	en_US

Abstract:

In this work we present a novel approach to learning dynamics of an environment perceived by a mobile robot. More precisely, we are interested in general motion patterns occurring in the environment rather than object dependent ones. A sampling algorithm is used to update a sample set, which represents observed dynamics, using the Bayes rule. From this set of samples a Hidden Markov Model is learnt online, which allows fast and efficient matching and prediction in the learnt model. Such models are useful for a number of tasks such as path planning, localisation and compliant motion. The approach is validated through simulation as well as experiments.

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

http://hdl.handle.net/10453/11169