In the field of autonomous Unmanned Aerial Vehicles (UAVs) landing, conventional approaches fall short in delivering not only the required precision but also the resilience against environmental disturbances. Yet, learning-based algorithms can offer promising solutions by leveraging their ability to learn the intelligent behaviour from data. On one hand, this paper introduces a novel multimodal transformer-based Deep Learning detector, that can provide reliable positioning for precise autonomous landing. It surpasses standard approaches by addressing individual sensor limitations, achieving high reliability even in diverse weather and sensor failure conditions. It was rigorously validated across varying environments, achieving optimal true positive rates and average precisions of up to 90%. On the other hand, it is proposed a Reinforcement Learning (RL) decision-making model, based on a Deep Q-Network (DQN) rationale. Initially trained in sumlation, its adaptive behaviour is successfully transferred and validated in a real outdoor scenario. Furthermore, this approach demonstrates rapid inference times of approximately 5ms, validating its applicability on edge devices.

翻译：在自主无人机（UAV）着陆领域，传统方法不仅在所需精度上有所欠缺，在抵御环境干扰方面也表现不足。然而，基于学习的算法通过利用从数据中学习智能行为的能力，能够提供有前景的解决方案。一方面，本文介绍了一种基于多模态Transformer的深度学习检测器，可为精准自主着陆提供可靠的定位。该方法通过解决单个传感器的局限性，即使在复杂天气和传感器故障条件下也能实现高可靠性，从而超越了标准方法。该检测器在不同环境中经过严格验证，实现了最优真阳性率和高达90%的平均精度。另一方面，本文提出了一种基于深度Q网络（DQN）原理的强化学习决策模型。该模型首先在仿真环境中训练，其自适应行为成功迁移并验证于真实室外场景。此外，该方法展现出约5毫秒的快速推理时间，验证了其在边缘设备上的适用性。