Adaptive Path Planning for Multiple Vehicles with Bounded Curvature

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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