Dynamic Path Optimization and Nonlinear Model Predictive Control for Autonomous UAV Landings on Mobile Aerial Platforms

Autonomous Unmanned Aerial Vehicles (UAVs) landings on moving aerial platforms present substantial challenges, including real-time feasible path planning and the design of robust control schemes to mitigate disturbances. This paper presents a novel docking strategy for UAVs that integrates dynamic path optimization with a Nonlinear Model Predictive Control (NMPC) framework. The proposed approach initiates with a global planner to generate collision-free, kinodynamically feasible paths, which are subsequently refined using a local planner that employs B-spline formulation alongside gradient-based optimization methods. To enhance control stability, particularly during the landing phase, the NMPC controller is augmented with a dynamic downwash model that compensates for aerodynamic disturbances. Extensive validation in both simulation and real-world experiments demonstrates that the proposed method achieves robust trajectory tracking, reduced landing errors, and improved platform stability. Simulation results show the proposed planner reaches a maximum velocity of 3.49 m/s and an average velocity of 2.07 m/s with a 100% landing success rate. Real-world experiments indicate that with the downwash model, vertical oscillations during landing are reduced by nearly 79% while the overall vertical landing error drops by over 60%.