Efficient target visiting path planning for multiple vehicles with bounded curvature

In this paper, we introduce the k-Dubins Traveling Salesman Problem with Neighborhoods (k-DTSPN), the problem of planning efficient paths among target regions for multiple robots with bounded curvature constraints (Dubins vehicles). This paper presents two approaches for the problem. Firstly, we present a heuristic that solves it in two steps, based on classical techniques found in the literature. Secondly, we employ a Memetic Algorithm to solve both combinatorial and continuous phases of the problem in a combined manner. We provide formal analysis about both proposed techniques, presenting upper bounds to the length of the longest tour. Numerous trials in simulated environments were executed, providing statistical examination of the final results.