Abstract: This paper proposes a new evolutionary planner to determine the trajectories of several Unmanned Aerial Vehicles (UAVs) and the scan direction of their cameras for minimizing the expected detection time of a nondeterministically moving target of uncertain initial location. To achieve this, the planner can reorient the UAVs cameras and modify the UAVs heading, speed, and height with the purpose of making the UAV reach and the camera observe faster the areas with high probability of target presence. Besides, the planner uses a digital elevation model of the search region to capture its influence on the camera likelihood (changing the footprint dimensions and the probability of detection) and to help the operator to construct the initial belief of target presence and target motion model. The planner also lets the operator include intelligence information in the initial target belief and motion model, in order to let him/her model real-world scenarios systematically. All these characteristics let the planner adapt the UAV trajectories and sensor poses to the requirements of minimum time search operations over real-world scenarios, as the results of the paper, obtained over 3 scenarios built with the modeling aid-tools of the planner, show.

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