点群で飛ぶ：強化学習による自律ドローン飛行
Flying on Point Clouds with Reinforcement Learning
機載3D LiDARとsim-to-real強化学習を組み合わせ、生の点群を軽量に扱う代理表現を用いて、狭い空間や薄い障害物を避けながら50Hzの低レベル制御でドローンを自律飛行させる手法を提案した。
著者: Guangtong Xu, Tianyue Wu, Zihan Wang, Qianhao Wang, Fei Gao
分類: cs.RO
原文アブストラクト
A long-cherished vision of drones is to autonomously traverse through clutter to reach every corner of the world using onboard sensing and computation. In this paper, we combine onboard 3D lidar sensing and sim-to-real reinforcement learning (RL) to enable autonomous flight in cluttered environments. Compared to vision sensors, lidars appear to be more straightforward and accurate for geometric modeling of surroundings, which is one of the most important cues for successful obstacle avoidance. On the other hand, sim-to-real RL approach facilitates the realization of low-latency control, without the hierarchy of trajectory generation and tracking. We demonstrate that, with design choices of practical significance, we can effectively combine the advantages of 3D lidar sensing and RL to control a quadrotor through a low-level control interface at 50Hz. The key to successfully learn the policy in a lightweight way lies in a specialized surrogate of the lidar's raw point clouds, which simplifies learning while retaining a fine-grained perception to detect narrow free space and thin obstacles. Simulation statistics demonstrate the advantages of the proposed system over alternatives, such as performing easier maneuvers and higher success rates at different speed constraints. With lightweight simulation techniques, the policy trained in the simulator can control a physical quadrotor, where the system can dodge thin obstacles and safely traverse randomly distributed obstacles.
