Continuous reinforcement learning based ramp jump control for single-track two-wheeled robots. (February 2022)

• Record Type: Journal Article
• Authors: Zheng, Qingyuan; Wang, Duo; Chen, Zhang; Sun, Yiyong; Liang, Bin
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 44:Number 4(2022)
• Page Start: 892
• Page End: 904
• Publication Date: 2022-02
• Subjects: Single-track two-wheeled robots -- continuous reinforcement learning -- ramp jump control -- motion control; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/01423312211037847
• Languages: English
• ISSNs: 0142-3312

Abstract

Single-track two-wheeled robots have become an important research topic in recent years, owing to their simple structure, energy savings and ability to run on narrow roads. However, the ramp jump remains a challenging task. In this study, we propose to realize a single-track two-wheeled robot ramp jump. We present a control method that employs continuous action reinforcement learning techniques for single-track two-wheeled robot control. We design a novel reward function for reinforcement learning, optimize the dimensions of the action space, and enable training under the deep deterministic policy gradient algorithm. Finally, we validate the control method through simulation experiments and successfully realize the single-track two-wheeled robot ramp jump task. Simulation results validate that the control method is effective and has several advantages over high-dimension action space control, reinforcement learning control of sparse reward function and discrete action reinforcement learning control.