Robot path planning in a social context

Sehestedt, S; Kodagoda, S; Dissanayake, G

Robot path planning in a social context

Sehestedt, S Kodagoda, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 2010 IEEE Conference on Robotics, Automation and Mechatronics, RAM 2010, 2010, pp. 206 - 211
Issue Date:: 2010-08-23

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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	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2010-08-23	en_US
dc.identifier.citation	2010 IEEE Conference on Robotics, Automation and Mechatronics, RAM 2010, 2010, pp. 206 - 211	en_US
dc.identifier.isbn	9781424465033	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16363
dc.description.abstract	Human robot interaction has attracted significant attention over the last couple of years. An important aspect of such robotic systems is to share the working space with humans and carry out the tasks in a socially acceptable way. In this paper, we address the problem of fusing socially acceptable behaviours into robot path planning. By observing an environment for a while, the robot learns human motion patterns based on sampled Hidden Markov Models and utilises them in a Probabilistic Roadmap based path planning algorithm. This will minimise the social distractions, such as going through someone else's working space (due to the shortest path), by planning the path through minimal distractions, leading to human-like behaviours. The algorithm is implemented in Orca/C++ with appealing results in real world experiments. ©2010 IEEE.	en_US
dc.relation.ispartof	2010 IEEE Conference on Robotics, Automation and Mechatronics, RAM 2010	en_US
dc.relation.isbasedon	10.1109/RAMECH.2010.5513126	en_US
dc.title	Robot path planning in a social context	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Singapore	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:

Human robot interaction has attracted significant attention over the last couple of years. An important aspect of such robotic systems is to share the working space with humans and carry out the tasks in a socially acceptable way. In this paper, we address the problem of fusing socially acceptable behaviours into robot path planning. By observing an environment for a while, the robot learns human motion patterns based on sampled Hidden Markov Models and utilises them in a Probabilistic Roadmap based path planning algorithm. This will minimise the social distractions, such as going through someone else's working space (due to the shortest path), by planning the path through minimal distractions, leading to human-like behaviours. The algorithm is implemented in Orca/C++ with appealing results in real world experiments. ©2010 IEEE.

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