Safe Swarm Navigation in Constrained Environments: A Dynamic Tube-Based Distributed MPC Approach

Navigating large-scale Unmanned Aerial Vehicle (UAV) swarms through restricted airspaces requires a delicate balance between rigorous safety guarantees and high mission throughput. To address the limitations of rigid tube structures and over-constrained optimization problems, we present a Dynamic Tube-based Distributed Model Predictive Control (DMPC) approach. Unlike traditional methods, our framework introduces an elastic tube reconstruction mechanism that adaptively shifts boundaries to accommodate dynamic obstacles while maintaining a connected navigable tube. By integrating a predictive risk-triggered constraint activation policy, the proposed controller avoids the curse of over-conservativeness, ensuring recursive feasibility in high-density scenarios. Quantitative results indicate that our method reduces redundant computations while maximizing workspace utilization, providing a scalable solution for future Urban Air Mobility infrastructures (UAM).