非標準環境下でのクアッドコプター姿勢制御に向けた形式性能指標付き強化学習
Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts
モーター故障や突風などの非標準条件下でクアッドコプターの姿勢制御器を強化学習で設計し、信号時相論理に基づく性能指標で評価した研究。
著者: Nicola Bernini, Mikhail Bessa, Rémi Delmas, Arthur Gold, Eric Goubault, Romain Pennec, Sylvie Putot, François Sillion
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
We explore the reinforcement learning approach to designing controllers by extensively discussing the case of a quadcopter attitude controller. We provide all details allowing to reproduce our approach, starting with a model of the dynamics of a crazyflie 2.0 under various nominal and non-nominal conditions, including partial motor failures and wind gusts. We develop a robust form of a signal temporal logic to quantitatively evaluate the vehicle's behavior and measure the performance of controllers. The paper thoroughly describes the choices in training algorithms, neural net architecture, hyperparameters, observation space in view of the different performance metrics we have introduced. We discuss the robustness of the obtained controllers, both to partial loss of power for one rotor and to wind gusts and finish by drawing conclusions on practical controller design by reinforcement learning.
