障害物環境飛行のための速度適応学習
Learning Speed Adaptation for Flight in Clutter
強化学習を用いて、未知の障害物環境を飛行するドローンの速度制約を動的に調整する方策を学習し、効率と安全性を両立させた。
著者: Guangyu Zhao, Tianyue Wu, Yeke Chen, Fei Gao
分類: cs.RO, cs.LG
原文アブストラクト
Animals learn to adapt speed of their movements to their capabilities and the environment they observe. Mobile robots should also demonstrate this ability to trade-off aggressiveness and safety for efficiently accomplishing tasks. The aim of this work is to endow flight vehicles with the ability of speed adaptation in prior unknown and partially observable cluttered environments. We propose a hierarchical learning and planning framework where we utilize both well-established methods of model-based trajectory generation and trial-and-error that comprehensively learns a policy to dynamically configure the speed constraint. Technically, we use online reinforcement learning to obtain the deployable policy. The statistical results in simulation demonstrate the advantages of our method over the constant speed constraint baselines and an alternative method in terms of flight efficiency and safety. In particular, the policy behaves perception awareness, which distinguish it from alternative approaches. By deploying the policy to hardware, we verify that these advantages can be brought to the real world.
