Unified Planning and Control for Quadrotor Perching via Multimodal NMPC

This study proposes a control framework based on a multimodal nonlinear model predictive control (NMPC) algorithm to enable autonomous navigation and precise trajectory tracking of a quadrotor UAV during a perching task. This framework fully considers the dynamic constraints in the perching task scenario, and reconstructs the cost function of NMPC to improve the adaptability across different stages of the task process. Additionally, the feasibility evaluation mechanism is introduced into the Hybrid A* trajectory planning algorithm to facilitate rational mode-switching decisions. Simulation experiments are conducted to validate the effectiveness of the proposed control framework, with performance comparisons made against the advanced EGO-Planner algorithm. Experimental results demonstrate that the proposed framework achieves superior control performance and trajectory tracking accuracy in perching tasks, highlighting its strong potential for practical deployment.