Planning for landing site selection in the aerial supply delivery

In the aerial supply delivery problem, an unmanned aircraft needs to deliver supplies as close as possible to the desired location. This involves choosing, flying to, sensing, and landing at a safe landing site that is most accessible from the goal. The problem is often complicated by the fact that the availability of these landing sites may be unknown before the mission begins. Therefore, the aircraft needs to compute a sequence of actions that will minimize the expected value of the objective function. The problem of computing this sequence corresponds to planning under uncertainty in the environment. In this paper, we show how it can be solved efficiently via a recently developed probabilistic planning framework, called Probabilistic Planning with Clear Preferences (PPCP). We show that the problem satisfies the Clear Preferences assumption required by PPCP, and therefore all the theoretical guarantees continue to hold. The experimental results in simulation demonstrate that our approach can solve large-scale problems in realtime while experiments on our custom quad-rotor helicopter provide a proof of concept for the planner.