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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the fourteenth International fusion conference on Information Fusion |
| Publication date | 2011 |
| ISBN (Print) | 978-1-4577-0267-9 |
| Publication status | Published - 2011 |
| Event | Fusion 2011 : 14th International Conference on Information Fusion - Chicago, Illinois, USA Duration: 1 Jan 2011 → … Conference number: 14 |
Conference
| Conference | Fusion 2011 : 14th International Conference on Information Fusion |
|---|---|
| Number | 14 |
| City | Chicago, Illinois, USA |
| Period | 01/01/2011 → … |
Keywords
- Streaming data
- Automated Situation Awareness
- Markov Models
- Clustering