Unsupervised Learning of Qualitative Motion Behaviours by a Mobile Robot

Paul Duckworth, Yiannis Gatsoulis, Ferdian Jovan, Nick Hawes, David Hogg, Anthony Cohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

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.
Original languageEnglish
Title of host publication Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016)
EditorsJ Thangarajah, K Tuyls, C Jonker, S Marsella
PublisherAssociation for Computing Machinery
Pages1043-1051
ISBN (Print)978-1-4503-4239-1
Publication statusPublished - 9 May 2016
EventInternational Conference on Autonomous Agents and Multiagent Systems (AAMAS) - , Singapore
Duration: 9 May 201613 May 2016

Conference

ConferenceInternational Conference on Autonomous Agents and Multiagent Systems (AAMAS)
Abbreviated titleAAMAS 2016
Country/TerritorySingapore
Period9/05/1613/05/16

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