Barrier Lyapunov Function-Based Control for Position-Based Visual Servoing of Fully Actuated UAVs within PX4

Position-Based Visual Servoing (PBVS) is a widely used technique for UAV control, enabling precise motion based on visual feedback. This paper presents a nonlinear control strategy based on a Barrier Lyapunov Function (BLF) to ensure exponential stability in fully actuated UAVs performing PBVS tasks. Unlike underactuated multirotors, fully actuated drones provide independent control of translation and orientation, making them well-suited for vision-based applications. We propose a velocity-state feedback control law that guarantees stability by leveraging a BLF-based approach. The method ensures that the velocity errors remain bounded while converging exponentially to zero, enhancing robustness in trajectory tracking. The control framework is integrated within the PX4 autopilot system and validated through Software-in-the-Loop (SITL) simulations in Gazebo, demonstrating its effectiveness in real-time UAV operations. Software-in-the-loop simulation results confirm the proposed controller's capability to track PBVS-generated velocity references accurately while maintaining stability under varying conditions. The integration of homography-based visual control further improves precision in vision-based UAV navigation. This work contributes to developing nonlinear control techniques for fully actuated UAVs, bridging the gap between theoretical control design and real-time implementation.