標題: | Low-level autonomous control and tracking of quadrotor using reinforcement learning |
作者: | Pi, Chen-Huan Hu, Kai-Chun Cheng, Stone Wu, I-Chen 機械工程學系 應用數學系 資訊工程學系 Department of Mechanical Engineering Department of Applied Mathematics Department of Computer Science |
關鍵字: | Reinforcement learning;Policy gradient;Quadrotor |
公開日期: | 1-二月-2020 |
摘要: | This paper proposes a low-level quadrotor control algorithm using neural networks with model-free reinforcement learning, then explores the algorithm's capabilities on quadrotor hover and tracking tasks. We provide a new point of view by examining the well-known policy gradient algorithm from reinforcement learning, then relaxing its requirements to improve training efficiency. Without requiring expert demonstrations, the improved algorithm is then applied to train a quadrotor controller with its output directly mapped to four actuators in a simulator, which is a technique used to control any linear or nonlinear system under unknown dynamic parameters and disturbances. We show two experimental tasks both in simulation and real-world quadrotors to verify our method and demonstrate performance: 1) hovering at a fixed position, and 2) tracking along a specific trajectory. |
URI: | http://dx.doi.org/10.1016/j.conengprac.2019.104222 http://hdl.handle.net/11536/153802 |
ISSN: | 0967-0661 |
DOI: | 10.1016/j.conengprac.2019.104222 |
期刊: | CONTROL ENGINEERING PRACTICE |
Volume: | 95 |
起始頁: | 0 |
結束頁: | 0 |
顯示於類別: | 期刊論文 |