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Learning probabilistic models for state tracking of mobile robots

We propose a learning algorithm for acquiring a stochastic model of the behavior of a mobile robot, which allows the robot to localize itself along the outer boundary of its environment while traversing it. Compared to previously suggested solutions based on learning self-organizing neural nets, our approach achieves much higher spatial resolution which is limited only by the control time-step of the robot. We demonstrate the successful work of the algorithm on a small robot with only three infrared range sensors and a digital compass, and suggest how this algorithm can be extended to learn probabilistic models for full decision-theoretic reasoning and planning.