A probabilistic abstraction approach for planning and controlling mobile robots

The paper presents a procedure for creating a probabilistic finite-state model for a mobile robot and for finding a sequence of controllers ensuring the highest probability for reaching a desired region. The approach starts by using results for controlling affine systems in simpliceal partitions, and then it creates a finite representation with history-based probabilities on transition. This representation is embedded into a Petri Net model with probabilistic costs on transitions, and a highest probability path to reach a target region is found. This probabilistic framework is suitable for controlling mobile robots based on more complex specifications.