Conflict-Free Flight Scheduling Using Strategic Demand Capacity Balancing for Urban Air Mobility Operations

2025-11-14

AI Summary

In high-density urban air mobility (UAM) operations, ensuring conflict-free flight and maintaining safe separation within constrained airspace remains a critical challenge for multi-agent coordination. Method: This paper proposes a strategic, supply–demand balanced, conflict-free scheduling framework. It extends pairwise conflict avoidance to multi-agent collaborative scenarios, establishing a traffic-adaptive, robust scheduling mechanism. By integrating kinematics-driven delayed takeoff control with a distributed cooperative optimization algorithm, the framework enables dynamic temporal allocation of airspace resources. Contribution/Results: Numerical simulations and real-world UAM case studies demonstrate that the method guarantees zero collisions throughout all operations while significantly reducing total system delay. Moreover, it exhibits strong scalability under increasing traffic density. The framework thus provides a viable, safety-assured, and efficiency-oriented solution for high-density UAM operations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper, we propose a conflict-free multi- agent flight scheduling that ensures robust separation in con- strained airspace for Urban Air Mobility (UAM) operations application. First, we introduce Pairwise Conflict Avoidance (PCA) based on delayed departures, leveraging kinematic principles to maintain safe distances. Next, we expand PCA to multi-agent scenarios, formulating an optimization approach that systematically determines departure times under increasing traffic densities. Performance metrics, such as average delay, assess the effectiveness of our solution. Through numerical simulations across diverse multi-agent environments and real- world UAM use cases, our method demonstrates a significant reduction in total delay while ensuring collision-free operations. This approach provides a scalable framework for emerging urban air mobility systems.

Problem

• Develops conflict-free flight scheduling for urban air mobility
• Ensures robust separation in constrained airspace using kinematic principles
• Optimizes departure times to reduce delays in high-density traffic

Innovation

• Conflict-free multi-agent flight scheduling for UAM
• Pairwise Conflict Avoidance using delayed departures
• Optimization approach for systematic departure time determination