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

2024-12-24

AI Summary

Urban air mobility (UAM) drone route planning must simultaneously satisfy dynamic regulatory constraints and multi-dimensional physical performance metrics—including energy consumption, radio interference, and noise. This paper proposes the first framework integrating probabilistic first-order logical spatial reasoning with deterministic–stochastic co-optimization: it models regulatory compliance as uncertain constraints and jointly optimizes multiple physical cost objectives. Our method combines hybrid probabilistic logical modeling, crowdsourced geospatial data-driven probabilistic constraint learning, deterministic–stochastic hybrid path search, and multi-objective evolutionary optimization. Extensive experiments on a large-scale real-world map of Paris demonstrate high routing network coverage, regulatory compliance exceeding 99.2%, and significant reduction in aggregate physical cost. Crucially, our approach achieves, for the first time, end-to-end verifiable legal compliance alongside engineering deployability in UAM route generation.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Sampling/Simulation-based Search
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Drone Route Planning
• Regulatory Compliance
• Efficiency Optimization

Innovation

• Hybrid Method
• Multi-Objective Optimization
• Probabilistic Task Design