Noise Aware Path Planning and Power Management of Hybrid Fuel UAVs

Hybrid fuel Unmanned Aerial Vehicles (UAV), through their combination of multiple energy sources, offer several advantages over the standard single fuel source configuration, the primary one being increased range and efficiency. Multiple power or fuel sources also allow the distinct pitfalls of each source to be mitigated while exploiting the advantages within the mission or path planning. We consider here a UAV equipped with a combustion engine-generator and battery pack as energy sources. We consider the path planning and power-management of this platform in a noise-aware manner. To solve the path planning problem, we first present the Mixed Integer Linear Program (MILP) formulation of the problem. We then present and analyze a label-correcting algorithm, for which a pseudo-polynomial running time is proven. Results of extensive numerical testing are presented which analyze the performance and scalability of the labeling algorithm for various graph structures, problem parameters, and search heuristics. It is shown that the algorithm can solve instances on graphs as large as twenty thousand nodes in only a few seconds. Note to Practitioners—The problem and algorithms proposed in this paper are relevant to constrained planning problems in general and specifically to those focused on widespread usage of small aerial vehicles in congested, urban environments. We are concerned here with the path planning of hybrid-fuel aerial vehicles in a noise-aware manner. This is motivated by the increasing usage of aerial vehicles, envisioning a probable future restriction on noise production in certain airspaces and the planning of such vehicles in those airspaces. We explore this novel problem, and present an approach to quickly find the optimal path and power plan in the presence of the noise constraints. The approach here is a discrete one, where the solution is a discrete set of edges and discrete generator settings which must be smoothed to obtain control inputs for a real system. The discrete approach allows solutions to be found quickly while giving up the true optimal trajectory that can be found when considering from a continuous framework. In practice, environment sampling and graph construction will greatly affect time-to-solve and as overall solution quality relative to a continuous approach to the trajectory and generator control.