Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation

Francisco S. Neves; Pedro N. Pereira; Raul D. S. G. Campilho; Andry M. Pinto

Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation

Computer Vision and Pattern Recognition 2026-06-30 v1 Robotics

Authors: Francisco S. Neves , Pedro N. Pereira , Raul D. S. G. Campilho , Andry M. Pinto

Abstract

Autonomous aerial inspection of marine infrastructure is frequently compromised by stochastic sea states, introducing risks of high-kinetic impacts, post-landing toppling, and sensory occlusion. This paper proposes a decoupled, multi-vehicle landing framework synchronizing an Unmanned Surface Vehicle (USV) equipped with a 3-RPU stabilized platform with a robust Unmanned Aerial Vehicle (UAV). The architecture utilizes two independent Deep Reinforcement Learning (DRL) agents: a Soft Actor-Critic (SAC) agent providing high-frequency wave-motion compensation for the landing deck, and a multimodal RL agent for the UAVs final approach. Evaluated in high-fidelity maritime simulations, the system achieved a 100% landing success rate across 15 trials in wave states varying from calm to rough. Results show a mean stabilization efficacy of 87.8%, maintaining the landing surface within 1 degree of the horizontal plane for 96% of the mission duration in rough conditions, effectively contributing to safer landings.

Keywords

autonomous UAV landing maritime platforms multimodal agentic AI deep reinforcement learning wave compensation

Cite

@article{arxiv.2606.31613,
  title  = {Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation},
  author = {Francisco S. Neves and Pedro N. Pereira and Raul D. S. G. Campilho and Andry M. Pinto},
  journal= {arXiv preprint arXiv:2606.31613},
  year   = {2026}
}