Optimizing UAV Trajectory for Emergency Response Operations under Real 3D Environments: Integrating Priority Levels and LoS Constraints

TL;DR

This paper presents a novel approach to optimize UAV trajectories in complex 3D urban environments for emergency response, balancing energy, priority, and LoS constraints using genetic algorithms and real-world data.

Contribution

It introduces a comprehensive 3D modeling and trajectory optimization framework with new algorithms tailored for disaster scenarios.

Findings

Algorithms effectively optimize UAV paths in complex environments.

Priority weighting influences total service time significantly.

Real-world data validates the proposed methods' effectiveness.

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as a critical component in next-generation wireless networks, particularly for disaster recovery scenarios, due to their flexibility, mobility, and rapid deployment capabilities. This paper focuses on optimizing UAV trajectories to ensure effective communication in disaster-stricken areas using terahertz (THz) links. We address specific challenges such as energy consumption, user priority levels, and navigating complex urban environments to maintain Line of Sight (LoS) connections amidst 3D obstacles. Our contributions include the development of a detailed modeling approach using online 3D map data, the formulation of an optimal trajectory optimization problem, and the proposal of a Genetic Algorithm (GA)-based method alongside an enhanced heuristic algorithm for faster convergence. Through 3D simulations, we demonstrate the trade-off between minimizing total service time and prioritizing higher-weight nodes, showing the impact of different priority weight factors on the trajectory time. The proposed algorithms are evaluated using real-world data from the West Bay area of Doha, Qatar, demonstrating their effectiveness in optimizing UAV trajectories for emergency response.