Spacio-temporal situation assessment for mobile robots

Anders Billesø Beck, Claus Risager, Nils Axel Andersen, Ole Ravn

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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.
Original languageEnglish
Title of host publicationProceedings of the fourteenth International fusion conference on Information Fusion
Publication date2011
ISBN (Print)978-1-4577-0267-9
Publication statusPublished - 2011
EventFusion 2011 : 14th International Conference on Information Fusion - Chicago, Illinois, USA
Duration: 1 Jan 2011 → …
Conference number: 14


ConferenceFusion 2011 : 14th International Conference on Information Fusion
CityChicago, Illinois, USA
Period01/01/2011 → …


  • Streaming data
  • Automated Situation Awareness
  • Markov Models
  • Clustering


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