単眼カメラと強化学習によるUAV自律着陸
Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing
着陸パッド上の特殊なレンチキュラー円の見え方の変化から高さと奥行きを推定し、強化学習で着陸を最適化する単眼カメラのみのUAV自律着陸手法を提案した。
著者: Tarik Houichime, Younes EL Amrani
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
This paper introduces an innovative approach for the autonomous landing of Unmanned Aerial Vehicles (UAVs) using only a front-facing monocular camera, therefore obviating the requirement for depth estimation cameras. Drawing on the inherent human estimating process, the proposed method reframes the landing task as an optimization problem. The UAV employs variations in the visual characteristics of a specially designed lenticular circle on the landing pad, where the perceived color and form provide critical information for estimating both altitude and depth. Reinforcement learning algorithms are utilized to approximate the functions governing these estimations, enabling the UAV to ascertain ideal landing settings via training. This method's efficacy is assessed by simulations and experiments, showcasing its potential for robust and accurate autonomous landing without dependence on complex sensor setups. This research contributes to the advancement of cost-effective and efficient UAV landing solutions, paving the way for wider applicability across various fields.