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This paper addresses the problem of forest-fire localization using unmanned aerial vehicles (UAVs). Due to the fast deployment of UAVs, it is practical to use them. In forest fires, usually the area to explore is unknown. Thus, existing studies use an automatic or semi-automatic exploration strategy following a zig-zag sweep pattern or expanding spiral search pattern. However, such an approach is not optimal in terms of exploration time since the mission execution and achievement in an unknown environment requires autonomous vehicle decision and control. This paper presents an enhanced approach for the fire localization mission via a decisional strategy considering a probabilistic model that uses the temperature to estimate the distance towards the forest fire. The UAV optimizes its trajectory according to the state of the forest-fire knowledge by using a map to represent its knowledge and updates it at each exploration step. We show in this paper that our planning and control methodology for forest-fire localization is efficient. Simulation results are carried out to evaluate the feasibility of the generated paths by the proposed methodology.

Autonomous Decisional High-level Planning for UAVs-based Forest-fire Localization