用单目相机和强化学习实现无人机自主降落，无需测距设备。
Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing
- 将降落任务转化为视觉优化问题，利用特殊圆环的视觉变化估计高度与深度。
- 通过强化学习训练模型，在仿真与实验中实现精准降落。
- 适合低成本无人机系统，尤其适用于无复杂传感器的场景。
本文提出一种基于强化学习的单目视觉自主降落方法，仅使用前向单目相机实现无人机自主着陆，无需深度感知设备。借鉴人类估算过程，将降落任务重构为优化问题：通过分析着陆垫上特殊设计的透镜形圆环在视觉上的颜色与形状变化，获取关键信息以估计飞行器高度与深度。采用强化学习算法逼近相关函数，使无人机在训练中学会确定最佳着陆参数。该方法在仿真与实际实验中均验证了其有效性，展现出无需复杂传感器配置即可实现鲁棒、精确着陆的潜力。研究推动了低成本、高效无人机着陆解决方案的发展，有望广泛应用于多个领域。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。