Enhancing Quadrotor Navigation with Path Smoothing in Curvature-Aware Model Predictive Contouring Control

This research presents the development and implementation of a 3D Curvature-Aware Model Predictive Contouring Control (3D CAMPCC) for autonomous quadcopter navigation. The primary objectives are to implement the 3D CAMPCC and to apply a smoothing path procedure to enhance trajectory planning and execution in complex environments. The 3D CAMPCC framework extends traditional contouring control techniques to unmanned aerial vehicles (UAVs), specifically racing drones. The proposed method incorporates curvature-aware path planning to effectively manage sharp turns and sudden path deviations while adhering to non-linear constraints such as passing through the gates of the track and flight stability. A significant component of this research is the implementation of a smoothing path procedure. This procedure refines generated paths to ensure smooth transitions and optimal performance during flight. The efficacy of the proposed methods is validated through extensive simulations, demonstrating improved path-following accuracy and enhanced real-time capabilities. The results indicate that the 3D CAMPCC, coupled with the smoothing path procedure, provides a robust and efficient solution for autonomous quadcopter navigation. The findings have potential applications in various fields, including aerial surveying, precision agriculture, and search and rescue operations, highlighting the broader impact and utility of this research.