Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Locating moving entities in indoor environments with teams of mobile robots
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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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.