用在线学习的分布式控制，让无人机在波浪中稳稳降在无人船。
A Time and Place to Land: Online Learning-Based Distributed MPC for Multirotor Landing on Surface Vessel in Waves
- 分步优化目标与输入，仅共享目标，降低通信延迟
- 引入高斯过程学习倾角与耦合代价，提升抗波浪能力
- 实验成功率达53%提升，适合海上巡检等场景
多旋翼无人机在无人水面艇（USV）上着陆可拓展海上及湖面应用的作业范围，并提供充电支持，适用于搜救和环境监测。然而，海浪引起的船只姿态与运动不确定性，使自主着陆面临挑战。现有技术依赖车辆间状态共享，常因通信延迟导致性能下降。本文提出一种基于在线学习的分布式模型预测控制（MPC）框架，用于波浪条件下无人机对无人船的自主着陆。各飞行器独立优化人工目标与输入，仅共享目标；目标代价包含耦合项与平台倾角项，由高斯过程（GP）学习获得。通过定制化平台安装于地面无人车，在室内完成模拟波浪晃动的全面实验验证。结果表明，相比忽略倾角影响的方法，本方案着陆成功率提升53%。
原文摘要 · Abstract (English)
Landing a multirotor unmanned aerial vehicle (UAV) on an uncrewed surface vessel (USV) extends the operational range and offers recharging capabilities for maritime and limnology applications, such as search-and-rescue and environmental monitoring. However, autonomous UAV landings on USVs are challenging due to the unpredictable tilt and motion of the vessel caused by waves. This movement introduces spatial and temporal uncertainties, complicating safe, precise landings. Existing autonomous landing techniques on unmanned ground vehicles (UGVs) rely on shared state information, often causing time delays due to communication limits. This paper introduces a learning-based distributed Model Predictive Control (MPC) framework for autonomous UAV landings on USVs in wave-like conditions. Each vehicle's MPC optimizes for an artificial goal and input, sharing only the goal with the other vehicle. These goals are penalized by coupling and platform tilt costs, learned as a Gaussian Process (GP). We validate our framework in comprehensive indoor experiments using a custom-designed platform attached to a UGV to simulate USV tilting motion. Our approach achieves a 53% increase in landing success compared to an approach that neglects the impact of tilt motion on landing.
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