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Abstract This paper investigates the path-planning problem applied to an innovative Unmanned Air Vehicle teaming with a helicopter to increase safety during Helicopter Emergency Medical Services operations. The unmanned vehicle, a drone that optionally can be launched from the helicopter, has the mission to explore the area of operation to determine the meteorological and environmental conditions and to detect physical obstacles. It is initially found that the combination of probabilistically optimal Rapidly-exploring Random Tree (RRT ^* ∗) as the global planner and of Bidirectional Rapidly-exploring Random Tree (BiRRT) as the local planner provides a nearly optimal global path and a rapid replanning in case new obstacles are detected. Adopting a Savitzky–Golay filter in an optional post-processing phase enables trajectory smoothing, thus improving its practicability. The feasibility of the identified trajectory for a rigid-body helicopter model is assessed by computing a first estimate of attitude, forces, control inputs, and rotor power from the trajectory points and curvature. This assessment shows that the RRT ^* ∗ used as a local planner provides replanned trajectories more feasible than BiRRT with comparable computational times.
Roncolini et al. (Tue,) studied this question.