Abstract In this paper we introduce the k-Dynamic Dubins TSP with Neighborhoods (k-DDTSPN), the problem consisting of planning efficient paths among a set of target regions dynamically selected in the environment for multiple robots with bounded curvature (Dubins vehicle). We propose a decentralized auction-based technique, which uses a greedy constructive strategy to dynamically calculate the cost of insertion of the new region to each path and selects the one with the minimum impact on the length. We provide a formal analysis of the proposed technique, presenting an upper bound for the length of the longest tour. Several trials were executed in a simulated environment, allowing for a statistical investigation of the results.


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    Title :

    Adaptive Path Planning for Multiple Vehicles with Bounded Curvature


    Contributors:


    Publication date :

    2015-01-01


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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