Hybrid Many-Objective Optimization in Probabilistic Mission Design for Compliant and Effective UAV Routing

TL;DR

This paper introduces a hybrid probabilistic and multi-objective optimization framework for UAV routing that ensures legal compliance and minimizes physical costs in complex urban environments, demonstrated through real-world Paris data.

Contribution

It presents a novel architecture combining Probabilistic Mission Design and Many-Objective Optimization for UAV routing under legal and physical constraints.

Findings

Effective UAV paths that comply with legal restrictions and physical objectives.

Versatile system validated on real-world data from Paris.

Demonstrated advantages over traditional routing methods.

Abstract

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) and Many-Objective Optimization for UAV routing. Hereby, our framework is able to comply 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.