Path planning strategies for drone delivery for life-saving pharmaceuticals

In the last years, Unmanned Aircraft Systems (UASs) are getting popular and are widely used to provide many applications. Specifically, a promising UAS application discussed in this thesis is the distribution of life-saving pharmaceutics using UASs. This thesis aims at defining a path planning strategy to be used to provide the distribution of life-saving pharmaceutics using UASs in populated areas. The proposed strategy is based on a method already suggested in the literature by the research group, consisting in two phases: first, a risk-map is generated analyzing the risk of a specific area; then, a path planning algorithm based on the well-known Rapidly-exploring Random Trees (RRT) algorithm searches for the optimal path that minimizes the overall risk and the flight time. Different optimization strategies are investigated and implemented to plan a specific flight mission for drone delivery, while satisfying specific requirements, such as: (i) minimization of the risk; (ii) minimization of the flight time; (iii) minimization of the risk, while forcing a constraint on the flight time; and, (iv), minimization of the flight time, while forcing a constraint on the risk. These strategies return different solutions adopted based on the type of life-saving pharmaceutics to be delivered. The proposed strategies are tested in simulations. First, simplified risk maps are used to demonstrate the effectiveness of the proposed solutions. Then, realistic risk maps are taken into account emulating a realistic scenario of life-saving pharmaceutics delivery in the city of Turin, Italy.