Dr Nick Hawes

Reader in Autonomous Intelligent Robotics

School of Computer Science
University of Birmingham
Edgbaston, Birmingham, B15 2TT
United Kingdom

Email: n.a.hawes@cs.bham.ac.uk
Twitter: @hawesie
Phone: +44 (0) 121 41 43739
Office: 133 (first floor, back right)
Office Hours: Mon 12:00, Tues 11:00 (term-time only)
Availability: Doodle MeetMe
[duckworth2016] Paul Duckworth, Yiannis Gatsoulis, Ferdian Jovan, Nick Hawes, David C. Hogg and Anthony G. Cohn. Unsupervised Learning of Qualitative Motion Behaviours by a Mobile Robot. In Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016). May 2016. [pdf] [bib]
Abstract. The success of mobile robots, in daily living environments, depends on their capabilities to understand human movements and interact in a safe manner. This paper presents a novel unsupervised qualitative-relational framework for learning human motion patterns using a single mobile robot platform. It is capable of learning human motion patterns in real-world environments, in order to predict future behaviours. This previously untackled task is challenging because of the limited field of view provided by a single mobile robot. It is only able to observe one location at any time, resulting in incomplete and partial human detections and trajectories. Central to the success of the presented framework is mapping the detections into an abstract qualitative space, and then characterising motion invariant to exact metric position. This framework was used by a physical robot autonomously patrolling a company's office during a six week deployment. Experimental results from this deployment are discussed and demonstrate the effectiveness and applicability of the system.
Download:  pdf pdf (2.16 MB)  bib bib
Links: [Google Scholar] [CiteSeer]
Available from: http://dl.acm.org/citation.cfm?id=2937077