This paper presents an optic flow-guided approach for achieving soft landings by resource-constrained unmanned aerial vehicles (UAVs) on dynamic platforms. An offline data-driven linear model based on Koopman operator theory is developed to describe the underlying (nonlinear) dynamics of optic flow output obtained from a single monocular camera that maps to vehicle acceleration as the control input. Moreover, a novel adaptation scheme within the Koopman framework is introduced online to handle uncertainties such as unknown platform motion and ground effect, which exert a significant influence during the terminal stage of the descent process. Further, to minimize computational overhead, an event-based adaptation trigger is incorporated into an event-driven Model Predictive Control (MPC) strategy to regulate optic flow and track a desired reference. A detailed convergence analysis ensures global convergence of the tracking error to a uniform ultimate bound while ensuring Zeno-free behavior. Simulation results demonstrate the algorithm's robustness and effectiveness in landing on dynamic platforms under ground effect and sensor noise, which compares favorably to non-adaptive event-triggered and time-triggered adaptive schemes.

翻译：本文提出一种光流引导方法，使资源受限的无人飞行器（UAV）能够在动态平台上实现软着陆。基于Koopman算子理论，我们建立了一种离线数据驱动的线性模型，用于描述从单目相机获取的光流输出与作为控制输入的飞行器加速度之间的映射关系，该模型刻画了底层（非线性）动力学特性。此外，我们在Koopman框架中引入了一种在线自适应机制，以处理未知平台运动和地面效应等不确定性因素，这些因素在下降过程的末段阶段会产生显著影响。为了进一步降低计算开销，我们将基于事件的自适应触发机制集成到事件驱动的模型预测控制（MPC）策略中，以调节光流并跟踪期望参考轨迹。详细的收敛性分析保证了跟踪误差全局收敛于一致最终界，同时确保无Zeno行为。仿真结果表明，该算法在地面效应和传感器噪声条件下，在动态平台着陆任务中具有鲁棒性和有效性，其性能优于非自适应事件触发及时间触发自适应方案。