基于深度强化学习的微型飞行器（MAVs）在复杂无GPS室内环境中的自主导航
Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment
AI总结：
本文提出深度近端策略优化（D-PPO）算法，通过在Unreal Engine构建的3D元环境中训练，提升微型飞行器在无GPS室内环境中的实时导航效率，实验表明训练计算延迟降低91%且性能无明显下降。
摘要：
由于仓库、工厂和室内设施等封闭空间缺乏可靠的GPS信号，无人机（UAVs）在室内环境中的自主性面临重大挑战。微型飞行器（MAVs）因其灵活性高、功耗低和计算能力有限，更适合在这些复杂且无GPS的场景中导航。本文提出了一种基于强化学习的深度近端策略优化（D-PPO）算法，通过提高计算效率来增强实时导航能力。该端到端网络在使用Unreal Engine创建的3D逼真元环境中进行训练。利用这些训练得到的元权重，MAV系统在真实室内环境中进行了大量实验测试。结果表明，所提出的方法在训练阶段将计算延迟降低了91%，且性能没有显著下降。该算法在DJI Tello无人机上进行了测试，得到了相似的结果。
英文摘要：
The Autonomy of Unmanned Aerial Vehicles (UAVs) in indoor environments poses significant challenges due to the lack of reliable GPS signals in enclosed spaces such as warehouses, factories, and indoor facilities. Micro Aerial Vehicles (MAVs) are preferred for navigating in these complex, GPS-denied scenarios because of their agility, low power consumption, and limited computational capabilities. In this paper, we propose a Reinforcement Learning based Deep-Proximal Policy Optimization (D-PPO) algorithm to enhance realtime navigation through improving the computation efficiency. The end-to-end network is trained in 3D realistic meta-environments created using the Unreal Engine. With these trained meta-weights, the MAV system underwent extensive experimental trials in real-world indoor environments. The results indicate that the proposed method reduces computational latency by 91\% during training period without significant degradation in performance. The algorithm was tested on a DJI Tello drone, yielding similar results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
