Autonomous Navigation of Mobile Robots Using Optimal Control of Finite State Automata

This paper presents a novel approach to autonomous intelligent navigation of mobile robotic platforms, which is based on the concept of language-theoretic discrete-event supervisory control. The proposed algorithm combines real-time sensor data and model-based information on motion dynamics into a probabilistic finite state automaton model to dynamically compute a time-varying supervisory algorithm. The performance and robustness of the autonomous intelligent navigation algorithm have been experimentally validated on Segway RMP robotic platforms