Inferring probabilistic automata from sensor data for robot navigation

Abstract

We address the problem of guiding a robot in such a way, that it can decide, based on perceived sensor data, which future actions to choose, in order to reach a goal. In order to realize this guidance, the robot has access to a (probabilistic) automaton (PA), whose final states represent concepts, which have to be recognized in order to verify, that a goal has been achieved. The contribution of this work is to learn these PA's from classified sensor data of robot traces through known environments. Within this framework, we account for the uncertainties arising from ambiguous perceptions. We introduce a knowledge structure, called prefix tree , in which the sample data, represented as cases, is organized. The prefix tree is used to derive and estimate the parameters of deterministic, as well as probabilistic automata models, which reflect the inherent knowledge, implicit in the data, and which are used for recognition in a restricted first-order logic framework.