A real time implementable All-Pair Dynamic Planning Algorithm for robot navigation based on the renormalized measure of probabilistic regular languages

Abstract

The recently reported v planning algorithm is modified to handle on-the-fly dynamic updates to the obstacle map. The modified algorithm called All-Pair-Dynamic-Planning(APDP), models the problem of robot path planning in the framework of finite state probabilistic automata and solves the all-pair planning problem in one setting. We use the concept of renormalized measure of regular languages to plan paths with automated trade-off between path length and robustness under dynamic uncertainties, from any starting location to any goal in the given map. The dynamic updating feature of APDP efficiently updates path plans to incorporate newly learnt information about the working environment.