Scalable Online Flight Trajectory Optimization via Sequential Quadratic Programming for Urban Air Mobility in Ultra Low-Altitude Airspace

2026-06-22

AI Summary

This work addresses the challenge of generating safe, dynamically feasible flight trajectories in real time for high-density urban low-altitude environments. The authors propose a scalable sequential quadratic programming (SQP) framework that unifies environmental geometric constraints, operational limits, and full six-degree-of-freedom aircraft dynamics into a single optimization model. A key innovation lies in dynamically generating separating hyperplanes during each SQP iteration to enable immediate collision avoidance, while a variable-resolution quadtree spatial decomposition ensures real-time performance even in large-scale urban scenarios. Experimental results across five real-world city environments demonstrate 100% mission success and guaranteed collision avoidance using only CPU computation, significantly outperforming conventional approaches such as standard SQP, iterative Linear Quadratic Regulator (iLQR), and Differential Dynamic Programming (DDP).

Abstract

As Urban Air Mobility (UAM) scales toward high-density operations, generating collision-free trajectories within complex 3D cityscapes is a critical safety requirement. This paper proposes a scalable Sequential Quadratic Programming (SQP) framework that integrates geometric environmental constraints, operational limits, and vehicle dynamics within a single online trajectory optimization process. Rather than precomputing obstacle-free corridors ahead of time, our method encodes obstacle avoidance as live separating-hyperplane constraints regenerated at every solver iteration, so that dense urban geometry and full-DOF vehicle dynamics are resolved jointly and online as the reference and environment evolve. A variable-scale quadtree decomposition keeps computation bounded, enabling the framework to scale to city-wide environments while preserving real-time performance for high-speed flight. We validate the framework against conventional SQP, Iterative Linear Quadratic Regulator, and Differential Dynamic Programming across flights in five real-world urban centers, attaining 100% success and clearance rates on CPU-only hardware.

Problem

• Urban Air Mobility
• Trajectory Optimization
• Collision Avoidance
• Ultra Low-Altitude Airspace
• Online Optimization

Innovation

• Sequential Quadratic Programming
• online trajectory optimization
• separating-hyperplane constraints
• quadtree decomposition
• Urban Air Mobility