GPS不達の屋内環境におけるMAVの深層強化学習による自律ナビゲーション
Deep RL-based Autonomous Navigation of Micro Aerial Vehicles (MAVs) in a complex GPS-denied Indoor Environment
GPSが使えない屋内環境で小型ドローンを自律飛行させるため、D-PPOという強化学習手法を提案し、Unreal Engine上の3D環境で訓練して実機実験で有効性を確認した論文。
著者: Amit Kumar Singh, Prasanth Kumar Duba, P. Rajalakshmi
分類: cs.RO, cs.LG
原文アブストラクト
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.