Diverge-Merge Formation and MAC Control in Structured Airspace

2026-07-19

AI Summary

In structured corridor–ramp airspace, conventional rigid formation control struggles to accommodate on-ramp merging and off-ramp splitting under high-density unmanned aerial vehicle (UAV) traffic, leading to degraded efficiency and safety. This work proposes a task-driven, real-time formation reconfiguration framework that integrates flight intent, spatial connectivity, and task-level interactions to enable dynamic geometric adaptation. Furthermore, a cluster-aware distributed TDMA protocol (CAD-TDMA) is designed to jointly optimize communication synchronization and spectrum reuse. Simulations demonstrate that the proposed approach maintains near-zero geometric misclassification even under severe congestion, while CAD-TDMA significantly outperforms fixed TDMA and WiFi MAC in terms of latency, packet loss rate, and throughput.

Abstract

The rapid scaling of advanced air mobility (AAM) makes corridor-based structured airspace a promising infrastructure for high-density unmanned aerial vehicle (UAV) traffic. Formation flight can improve corridor capacity by suppressing shockwave propagation, but rigid formations become inefficient or unsafe during ramp branching, merging, and congestion. To address this problem, this paper proposes a task-driven diverge-merge control framework for UAV formations in structured airspace. At the beginning, a corridor-ramp branching structured airspace model is established to characterize the traffic dynamics and spatial constraints. Building upon this, a fast task-driven clustering mechanism integrates spatial connectivity, flight intent, and aerial task interactions to enable real-time diverge and merge for ramp branching and traffic reshaping. To make the diverge-merge reconfigurations executable at the media access control (MAC) layer of the formation, a cluster-aware distributed time division multiple access (CAD-TDMA) protocol is further designed. It protects intra-cluster control synchronization while conservatively reusing low-risk inter-cluster slots. Simulation results show that the proposed diverge-merge algorithm maintains near-zero geometrical misclassification under severe physical overlapping and congestion. With the formation diverge-merge traces, CAD-TDMA achieves the best delay--loss--throughput tradeoff over fixed TDMA and WiFi MAC. It shows that the proposed formation control framework can jointly support real-time formation reconfiguration and reliable communication in corridor-ramp structured airspace.

Problem

• formation flight
• structured airspace
• ramp branching
• congestion
• UAV traffic

Innovation

• formation flight
• diverge-merge control
• structured airspace
• CAD-TDMA
• task-driven clustering