Individual localization and tracking in multi-robot settings with dynamic landmarks
We generalize particle filtering [1,2] to multi-robot settings in order to localize the subject robot in a partially observable environment using landmarks and simultaneously track the uncertain location of the other non-cooperative robot(s). Our focus is on the subject robot's localization at...
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| Published in | Proceedings of the 10th international conference on Advanced Agent Technology pp. 277 - 280 |
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| Main Authors | , |
| Format | Conference Proceeding |
| Language | English |
| Published |
Berlin, Heidelberg
Springer-Verlag
02.05.2011
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| Series | ACM Conferences |
| Online Access | Get full text |
| ISBN | 9783642272158 3642272150 |
| DOI | 10.1007/978-3-642-27216-5_19 |
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| Abstract | We generalize particle filtering [1,2] to multi-robot settings in order to localize the subject robot in a partially observable environment using landmarks and simultaneously track the uncertain location of the other non-cooperative robot(s). Our focus is on the subject robot's localization at its own level in the presence of others who may not be cooperative. Consequently, our perspective and approach differs from previous work in multi-robot settings, which has predominantly focused on joint localization by multiple cooperating robots [3,4]. In this context, we introduce a nested set of particles to track the subject robot and others, and recursively project these particles as the subject robot moves and makes observations. Consequently, the subject robot attributes a behavioral model to the other in order to predict its actions. |
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| AbstractList | We generalize particle filtering [1,2] to multi-robot settings in order to localize the subject robot in a partially observable environment using landmarks and simultaneously track the uncertain location of the other non-cooperative robot(s). Our focus is on the subject robot's localization at its own level in the presence of others who may not be cooperative. Consequently, our perspective and approach differs from previous work in multi-robot settings, which has predominantly focused on joint localization by multiple cooperating robots [3,4]. In this context, we introduce a nested set of particles to track the subject robot and others, and recursively project these particles as the subject robot moves and makes observations. Consequently, the subject robot attributes a behavioral model to the other in order to predict its actions. |
| Author | Doshi, Prashant Mesbah, Anousha |
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| ContentType | Conference Proceeding |
| DOI | 10.1007/978-3-642-27216-5_19 |
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| Editor | Weyns, Danny Hattori, Hiromitsu Dechesne, Francien Mors, Adriaan Such, Jose Miguel |
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