WaveLander: A Generalizable Hierarchical Control Framework for UAV Landing on Wave-Disturbed Platforms via Reinforcement Learning

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.

Coastal Underwater Evidence Search System with Surface-Underwater Collaboration

The Coastal underwater evidence search system with surface-underwater collaboration is designed to revolutionize the search for artificial objects in coastal underwater environments, overcoming limitations associated with traditional methods such as divers and tethered remotely operated vehicles. Our innovative multi-robot collaborative system consists of three parts, an autonomous surface vehicle as a mission control center, a towed underwater vehicle for wide-area search, and a biomimetic underwater robot inspired by marine organisms for detailed inspections of identified areas. We conduct extensive simulations and real-world experiments in pond environments and coastal fields to demonstrate the system potential to surpass the limitations of conventional underwater search methods, offering a robust and efficient solution for law enforcement and recovery operations in marine settings.