GNLC-TCM: Integrated Global Navigation and Local Control with a Traffic Capacity Model for UAV Swarms in Constrained Environments

The increasing demand for autonomous Unmanned Aerial Vehicle (UAV) operations in constrained environments such as indoor spaces, industrial facilities, and urban infrastructure has led to a growing interest in swarm-based navigation and coordination strategies. Navigating multiple UAVs through complex, limited-access environments presents significant challenges in collision avoidance, dynamic path planning, and airspace management. This study investigates a hybrid approach to UAV swarm navigation in restricted spaces by integrating global and local control mechanisms. The proposed architecture, termed GNLC-TCM (Global Navigation and Local Control with a Traffic Capacity Model), combines local reactive navigation using the Artificial Potential Field (APF) algorithm with a graph-based global planner. The global planner models the environment as an undirected graph, where nodes represent navigable waypoints, and edge weights are based on an introduced concept of the traffic capacity model. This model accounts for the expected UAV flow through each connection, enabling better distribution of agents and minimizing congestion. A simulation environment was developed using a basic multi-agent configuration within sample environments to evaluate the proposed system. Performance metrics for target reachability rate and adaptability to traffic distribution were observed. Preliminary results demonstrate the feasibility of the dual-layer architecture and its potential for managing UAV swarms in confined spaces. The paper concludes with a discussion on the observed system behaviour and proposes several directions for future research, including dynamic re-weighting strategies and hardware implementation in real UAV platforms.