Low-level autonomous control and tracking of quadrotor using reinforcement learning. (February 2020)

• Record Type: Journal Article
• Authors: Pi, Chen-Huan; Hu, Kai-Chun; Cheng, Stone; Wu, I-Chen
• Is Part Of: Control engineering practice. Volume 95(2020)
• Publication Date: 2020-02
• Subjects: Reinforcement learning -- Policy gradient -- Quadrotor; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2019.104222
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: 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. The video of our experiments can be found at https://youtu.be/oEVcdiFPnMo .