Robust digital-twin airspace discretization and trajectory optimization for autonomous unmanned aerial vehicles

Area of Science:

• Urban planning and logistics
• Autonomous systems and robotics
• Geospatial data analysis

Background:

• Increasing integration of autonomous Unmanned Aerial Vehicles (UAVs) in urban environments necessitates sophisticated airspace management.
• Current urban airspace management lacks dynamic capabilities to handle high-density autonomous aerial mobility.

Purpose of the Study:

• To propose a novel Advanced Aerial Mobility (AAM) logistical system for managing heterogeneous UAV fleets in high-density city centers.
• To develop a framework for real-time digital twin creation and dynamic 4D airspace discretization for autonomous UAV operations.

Main Methods:

• Real-time 3D geospatial mining of LiDAR data to create a dynamic digital twin of urban airspace.
• Development of a 4D discretization method (Skyroutes) for city airspace, integrating real-time constraints, regulations, and kinematic optimal operations.
• Simulation of 1138 trips using an hourly trip generation model to compare proposed methods against Cartesian discretization.

Main Results:

• The AAM system enables precise estimation of urban airspace capacity and operational costs through detailed 3D generation.
• Demonstrated an increase in airspace capacity by up to 10% compared to traditional methods.
• Generated UAV trajectories showed a 50% improvement in energy efficiency and enhanced safety.

Conclusions:

• The proposed AAM system offers a robust solution for managing autonomous UAVs in complex urban environments.
• Dynamic 4D discretization and digital twin modeling are crucial for efficient and safe aerial mobility.
• The AAM system significantly improves airspace utilization, energy efficiency, and safety for urban drone operations.