Ν☆: a Robot Path Planning Algorithm Based on Renormalised Measure of Probabilistic Regular Languages

This article introduces a novel path planning algorithm, called m, that reduces the problem of robot path planning to optimisation of a probabilistic finite state automaton. The m-algorithm makes use of renormalised measure m of regular languages to plan the optimal path for a specified goal. Although the underlying navigation model is probabilistic, the m-algorithm yields path plans that can be executed in a deterministic setting with automated optimal trade-off between path length and robustness under dynamic uncertainties. The m-algorithm has been experimentally validated on Segway Robotic Mobility Platforms in a laboratory environment.