Abstract
A new approach to on-line path planning is derived in this paper. The planning algorithm is motivated by robot navigation and manipulation tasks in uncertain, unstructured, dynamic environments. A minimum entropy evidential classifier is used to recognize targets and obstacles in the environment. An iterative Newton scheme is then used to generate a sequence of knot points that guide the motion of the robot. The acquisition and processing of sensory data continue during the motion, thus reducing the uncertainty about the environment. The classification of targets and obstacles is updated, and the path is replanned (locally) to adapt to those changes. A graphical tool based on the concept of Julia sets is used to ensure the predictability and smoothness of the paths.
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Erkmen, A.M., Yegenoglu, F. & Stephanou, H.E. Entropy-driven on-line control of autonomous robots. J Intell Robot Syst 2, 109–121 (1989). https://doi.org/10.1007/BF00238684
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DOI: https://doi.org/10.1007/BF00238684