无人机精准降落移动平台，安全又高效。
NMPC-Lander: Nonlinear MPC with Barrier Function for UAV Landing on a Mobile Platform
- 用非线性预测控制加屏障函数实现高精度降落
- 静态平台误差9厘米，动态平台11厘米
- 比传统方法快三倍，适合无人机自动充电
四旋翼无人机在众多关键应用中日益普及，但受限于电池寿命和飞行范围。为解决此问题，自主无人机在固定或移动充电/换电站着陆成为关键技术。本文提出NMPC-Lander，将非线性模型预测控制（NMPC）与控制屏障函数（CBF）结合，实现对静态和动态平台的精确安全着陆。该方法利用NMPC实现高精度轨迹跟踪，同时通过CBF保障与静态障碍物的避障能力。真实硬件实验表明，着陆位置平均误差分别为静态平台9.0厘米、动态平台11厘米。相比基于B样条与A*规划的方法，其位置跟踪性能提升近三倍，验证了方法在鲁棒性与实用性上的优越性。
原文摘要 · Abstract (English)
Quadcopters are versatile aerial robots gaining popularity in numerous critical applications. However, their operational effectiveness is constrained by limited battery life and restricted flight range. To address these challenges, autonomous drone landing on stationary or mobile charging and battery-swapping stations has become an essential capability. In this study, we present NMPC-Lander, a novel control architecture that integrates Nonlinear Model Predictive Control (NMPC) with Control Barrier Functions (CBF) to achieve precise and safe autonomous landing on both static and dynamic platforms. Our approach employs NMPC for accurate trajectory tracking and landing, while simultaneously incorporating CBF to ensure collision avoidance with static obstacles. Experimental evaluations on the real hardware demonstrate high precision in landing scenarios, with an average final position error of 9.0 cm and 11 cm for stationary and mobile platforms, respectively. Notably, NMPC-Lander outperforms the B-spline combined with the A* planning method by nearly threefold in terms of position tracking, underscoring its superior robustness and practical effectiveness.
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