動的環境を飛行するためのエンドツーエンド学習アプローチ
Flying in Highly Dynamic Environments with End-to-end Learning Approach
LiDAR点群を2次元障害物マップに圧縮し、深層強化学習でクアッドロータの加速度指令を生成して動的障害物を回避する手法を提案。
著者: Xiyu Fan, Minghao Lu, Bowen Xu, Peng Lu
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Obstacle avoidance for unmanned aerial vehicles like quadrotors is a popular research topic. Most existing research focuses only on static environments, and obstacle avoidance in environments with multiple dynamic obstacles remains challenging. This paper proposes a novel deep-reinforcement learning-based approach for the quadrotors to navigate through highly dynamic environments. We propose a lidar data encoder to extract obstacle information from the massive point cloud data from the lidar. Multi frames of historical scans will be compressed into a 2-dimension obstacle map while maintaining the obstacle features required. An end-to-end deep neural network is trained to extract the kinematics of dynamic and static obstacles from the obstacle map, and it will generate acceleration commands to the quadrotor to control it to avoid these obstacles. Our approach contains perception and navigating functions in a single neural network, which can change from a navigating state into a hovering state without mode switching. We also present simulations and real-world experiments to show the effectiveness of our approach while navigating in highly dynamic cluttered environments.
