用强化学习实现无人机在晃动海面平台的稳定着陆
WaveLander: A Generalizable Hierarchical Control Framework for UAV Landing on Wave-Disturbed Platforms via Reinforcement Learning
- 分层控制：上层用RL决策垂直速度，下层用传统控制器稳住姿态
- 在随机波浪扰动下实现稳定着陆，无需切换规则
- 适合需要高鲁棒性的海上无人机回收场景
无人飞行器（UAV）在波浪扰动的海上平台自主着陆仍具挑战，受限于平台运动的随机性、姿态时变性及触地条件不确定性。现有基于模型的方法常需精确运动预测与在线优化，而端到端学习方法则存在训练复杂度高、可解释性差等问题。本文提出WaveLander，一种基于强化学习的分层控制框架，将垂直着陆决策与低层飞行稳定分离。RL策略将紧凑的平台相对观测映射为标量垂直速度参考，而传统低层飞行控制器负责维持姿态稳定与横向跟踪。该设计将动态平台着陆问题简化为低维、时序感知的控制任务，实现无需显式切换规则的平滑着陆行为。在随机波浪诱导的平台运动仿真中，WaveLander表现出鲁棒着陆性能，并能泛化至未见扰动条件，验证了分层学习控制在海上无人机回收中的潜力。
原文摘要 · Abstract (English)
Autonomous landing of unmanned aerial vehicles (UAVs) on wave-disturbed marine platforms remains challenging due to stochastic platform motion, time-varying platform attitude, and uncertain touchdown conditions. Existing model-based methods often require accurate motion prediction and online optimization, while end-to-end learning approaches may suffer from high training complexity and limited interpretability. This paper presents WaveLander, a hierarchical control framework via reinforcement learning (RL) that decouples vertical landing decision-making from low-level flight stabilization. The RL policy maps a compact platform-relative observation to a scalar vertical velocity reference, while a conventional low-level flight controller maintains attitude stability and lateral tracking. This formulation reduces dynamic platform landing to a low-dimensional, timing-aware control problem and enables smooth landing behavior without explicit switching rules. Simulation results under randomized wave-induced platform motions show that WaveLander achieves robust landing performance and generalizes to unseen disturbance conditions, demonstrating the potential of hierarchical learning-based control for marine UAV recovery.
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