Decentralized adaptive control of multi-UAV formation in urban mobility with obstacle avoidance.

The deployment of unmanned aerial vehicle (UAV) swarms in urban mobility applications such as traffic monitoring, infrastructure inspection, and emergency response is challenged by dense obstacles, limited communication, and the need for scalable coordination strategies. This paper aims to develop a fully decentralized control framework that enables multiple UAVs to maintain formation, avoid obstacles, and track mission objectives in complex urban environments using only local information. To achieve this objective, a Laplacian-based consensus formation controller is integrated with an adaptive Artificial Potential Field (APF) method for obstacle avoidance, while a leader-based tracking strategy guides the swarm centroid toward user-defined waypoints. Stability of the overall system is established through Lyapunov-based analysis, demonstrating asymptotic convergence of formation and tracking errors. Python-based simulations validate the proposed approach in obstacle-rich scenarios, showing smooth formation convergence, collision-free navigation, and consistent preservation of safety distances (20 px inter-UAV and 30 px UAV–obstacle) under dynamic goal changes. The results indicate that the proposed decentralized and safety-aware control strategy is well suited for real-time urban UAV operations. From a policy and planning perspective, the framework supports scalable aerial cooperation with minimal communication requirements, providing a practical building block for integrating UAV swarms into future smart-city and urban mobility ecosystems.