Abstract
In this paper, we address the problem of finding trajectories for multiple unmanned aerial vehicles deployed to perform a collaborative mission, requiring communication, coordination and situation awareness. Thus, we favor trajectories that are correlated in space and time, by proposing a metric to measure the dispersion between the trajectories. This dispersion metric is used as the objective function of the Minimum Dispersion Routing Problem. We propose a local search genetic algorithm as a method to solve this new routing problem, and we tested this approach using modified benchmark vehicle routing problem instances. Our computational results show that the approach is quite successful, yielding trajectories with the desired characteristics in terms of dispersion.
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