Noise Aware Path Planning and Power Management of Hybrid Fuel UAVs

TL;DR

This paper introduces a noise-aware path planning and power management approach for hybrid fuel UAVs, utilizing MILP and a label-correcting algorithm to efficiently handle large-scale problems.

Contribution

It presents a novel MILP formulation and a scalable label-correcting algorithm for noise-aware path planning and power management in hybrid fuel UAVs.

Findings

The algorithm can solve large instances with up to twenty thousand nodes in seconds.

Extensive numerical testing demonstrates the method's performance and scalability.

The approach effectively balances path planning and power management considering noise constraints.

Abstract

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.