Robot Path Planning in Uncertain Environments: A Language Measure Theoretic Approach

Abstract

This paper addresses the problem of goaldirected robot path planning in the presence of uncertainties that are induced by bounded environmental disturbances and actuation errors. The offline infinitehorizon optimal plan is locally updated by online finitehorizon adaptive replanning upon observation of unexpected events (e.g., detection of unanticipated obstacles). The underlying theory is developed as an extension of a gridbased path planning algorithm, called خ½â‹†, which was formulated in the framework of probabilistic finite state automata (PFSA) and language measure from a controltheoretic perspective. The proposed concept has been validated on a simulation test bed that is constructed upon a model of typical autonomous underwater vehicles (AUVs) in the presence of uncertainties.