用强化学习让微型无人机在无GPS室内环境高效自主导航
Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment
- 基于深度强化学习设计D-PPO算法，提升计算效率
- 训练时计算延迟降低91%，性能几乎不变
- 在真实无人机上验证，适合资源受限的室内场景
无人机在仓库、工厂等封闭空间中自主飞行面临缺乏可靠GPS信号的挑战。微型飞行器（MAVs）因机动性强、功耗低、计算能力有限，适合此类复杂环境。本文提出一种基于深度强化学习的近端策略优化（D-PPO）算法，通过在Unreal Engine构建的三维真实感元环境中训练端到端网络，提升实时导航效率。利用训练好的元权重，在真实室内环境中进行了大量实验。结果表明，该方法在训练阶段将计算延迟降低了91%，性能未明显下降。算法在DJI Tello无人机上测试，同样取得良好效果。
原文摘要 · Abstract (English)
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.
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