  Unmanned Aerial Vehicles (UAVs) are emerging as very important tools in
search and rescue (SAR) missions at sea, enabling swift and efficient
deployment for locating individuals or vessels in distress. The successful
execution of these critical missions heavily relies on effective path planning
algorithms that navigate UAVs through complex maritime environments while
considering dynamic factors such as water currents and wind flow. Furthermore,
they need to account for the uncertainty in search target locations. However,
existing path planning methods often fail to address the inherent uncertainty
associated with the precise location of search targets and the uncertainty of
oceanic forces. In this paper, we develop a framework to develop and
investigate trajectory planning algorithms for maritime SAR scenarios employing
UAVs. We adopt it to compare multiple planning strategies, some of them used in
practical applications by the United States Coast Guard. Furthermore, we
propose a novel planner that aims at bridging the gap between computation
heavy, precise algorithms and lightweight strategies applicable to real-world
scenarios.
