Title :
Spacio-temporal situation assessment for mobile robots
Author :
Beck, Anders Billesø ; Risager, Claus ; Andersen, Nils A. ; Ravn, Ole
Author_Institution :
Centre for Robot Technol., Danish Technol. Inst., Odense, Denmark
Abstract :
In this paper, we present a framework for situation modeling and assessment for mobile robot applications. We consider situations as data patterns that characterize unique circumstances for the robot, and represented not only by the data but also its temporal and spacial sequence. Dynamic Markov chains are used to model the situation states and sequence, where stream clustering is used for state matching and dealing with noise. In experiments using simulated and real data, we show that we are able to learn a situation sequence for a mobile robot passing through a narrow passage. After learning the situation models we are able to robustly recognize and predict the situation.
Keywords :
Markov processes; intelligent robots; mobile robots; pattern clustering; spatiotemporal phenomena; data pattern; dynamic Markov chain; mobile robot; spacial sequence; spatiotemporal situation assessment; state matching; stream clustering; temporal sequence; Clustering algorithms; Data models; Hidden Markov models; Markov processes; Mobile robots; Robot sensing systems; Automated Situation Awareness; Clustering; Markov Models; Streaming data;
Conference_Titel :
Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4577-0267-9