Publikation
Hybrid Many-Objective Optimization in Probabilistic Mission Design for Compliant and Effective UAV Routing
Simon Kohaut; Nikolas Hohmann; Sebastian Brulin; Benedict Flade; Julian Eggert; Markus Olhofer; Jürgen Adamy; Devendra Singh Dhami; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2412.18514, Pages 1-24, arXiv, 2024.
Zusammenfassung
Advanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern
logistics and pave the way for various public services and industry uses. However, throughout its history, the
development of such systems has been impeded by the complexity of legal restrictions and physical constraints.
While airspaces are often tightly shaped by various legal requirements, Unmanned Aerial Vehicles (UAV)
must simultaneously consider, among others, energy demands, signal quality, and noise pollution. In this
work, we address this challenge by presenting a novel architecture that integrates methods of Probabilistic
Mission Design (ProMis) [ 1, 2 ] and Many-Objective Optimization [ 3] for UAV routing. Hereby, our framework
facilitates compliance with legal requirements under uncertainty while producing effective paths that minimize
various physical costs a UAV needs to consider when traversing human-inhabited spaces. To this end, we
combine hybrid probabilistic first-order logic for spatial reasoning with mixed deterministic-stochastic route
optimization, incorporating physical objectives such as energy consumption and radio interference with a
logical, probabilistic model of legal requirements. We demonstrate the versatility and advantages of our system
in a large-scale empirical evaluation over real-world, crowd-sourced data from a map extract from the city of
Paris, France, showing how a network of effective and compliant paths can be formed.