Dynamic path planning for a mobile automaton with limited information on the environment

TLDR

Path planning for a mobile robot in an unknown obstacle‑filled environment is investigated. The study explores how additional information sources, such as vision sensors, affect the proposed planning approach. The authors derive a lower bound on path length under uncertainty and introduce two continuously‑updated, non‑heuristic algorithms that use local sensory feedback to guide motion toward the target. Ibis information is sufficient to guarantee reaching the target while generating reasonable paths, and the algorithms achieve these outcomes within the established lower‑bound length.

Abstract

The problem of path planning is studied for the case of a mobile robot moving in an environment filled with obstacles whose shape and positions are not known. Under the accepted model, the automaton knows its own and the target coordinates, and has a "sensory" feedback which provides it with local information on its immediate surroundings. Ibis information is shown to be sufficient to guarantee reaching a global objective (the target), while generating reasonable (if not optimal) paths. A lower bound on the length of paths generated by any algorithm operating with uncertainty is formulated, and two nonheuristic path planning algorithms are described. In the algorithms, motion planning is done continuously (dynamically), based on the automaton's current position and on its feedback. The effect of additional sources of information (e.g., from a vision sensor) on the outlined approach is discussed.