This study presents a real-time integrated framework that enables Unmanned Aerial Vehicles (UAVs) to operate safely and reliably within complex 3-dimensional environments by multiple dynamic obstacles. The proposed integrated framework three functiona...
This study presents a real-time integrated framework that enables Unmanned Aerial Vehicles (UAVs) to operate safely and reliably within complex 3-dimensional environments by multiple dynamic obstacles. The proposed integrated framework three functionalities—path planning, collision avoidance, and trajectory-tracking control—into a hierarchical architecture designed for real-time execution. In Path Planning, various RRT-based algorithms were analyzed, and RRT* was selected as the global path generator due to its superior path cost and computational efficiency. In addition, the Line-of-Sight Path Optimization (LoSPO) algorithm was incorporated to further enhance optimality and ensure real-time performance. In Collision Avoidance, a distance-based collision detection logic was designed using trajectory segments of the aircraft and dynamic obstacles. To reduce misjudgment associated with purely distance-based methods, a time-based collision prediction logic was additionally formulated by considering the velocities of the aircraft and obstacles. Furthermore, the Airborne Collision Avoidance System (ACAS) concept was adopted to define a risk function, establishing quantitative thresholds for collision detection and path-replanning decisions. In Trajectory Tracking Control, a Lyapunov stability based nonlinear backstepping controller was further extended by incorporating incremental dynamics, thereby yielding an Incremental Backstepping Control (IBSC) law that provides enhanced robustness in the presence of modeling uncertainties and external disturbances. These components were then systematically integrated into a unified real-time framework. Validation was conducted through a series of single-aircraft and multi-aircraft encounter scenarios. Simulation results demonstrate that the proposed framework can accurately predict collision risks, rapidly generate safe avoidance trajectories, and maintain stable tracking performance, confirming its suitability for real-time autonomous flight operations in complex, dynamic environments.