Simulation-Based Unmanned Aerial Vehicle Fleet Management and Control System for Urban U-Space

Abstract

The Simulation-Based Unmanned Aerial Vehicle Fleet Management and Control System for Urban U-Space (S-UFMC) is an advanced framework designed to manage high-density UAV traffic within complex urban environments. To ensure safe, efficient and scalable operations, the system employs a grid-based airspace model that segments the urban environment into manageable 3D corridors. This structure is governed by a synthesis of artificial intelligence (AI), A* (A-STAR) pathfinding algorithms and a suite of simulated sensors. Performance monitoring relies on simulated IoT sensors, which operate using data models derived from extensive research and testing. These simulations track key metrics such as speed, total path length and average velocity to guide operational decisions. Safety is paramount, enforced by an anti-collision system that uses simulated LiDAR and proximity sensors to predict and resolve potential conflicts in real time. The system's AI core, enhanced by reinforcement learning, facilitates intelligent decision-making. This allows for real-time trajectory adjustments and dynamic rerouting to navigate obstacles, fluctuating traffic density and other environmental changes. A robust simulation platform validates the entire system, allowing for comprehensive testing of algorithms and behaviours across diverse operational scenarios to ensure real-world reliability which sets a new standard for urban air traffic, enabling safe drone applications in smart cities.