マルチモーダルエージェントAIと能動的波浪補償による海事プラットフォームへの堅牢なUAV自律着陸
Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation
波浪による動揺を補償する安定化プラットフォームを備えたUSVと、強化学習エージェントで制御されるUAVを連携させ、海事プラットフォームへの安全な自律着陸を実現する枠組みを提案した。
著者: Francisco S. Neves, Pedro N. Pereira, Raul D. S. G. Campilho, Andry M. Pinto
分類: cs.CV, cs.RO
原文アブストラクト
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.