This paper presents a study on the control of unmanned bicycle motion based on inertial wheels in the pybullet physics simulation environment. To address the nonlinear and strongly coupled nature of unmanned bicycles, the Proximal Policy Optimization deep reinforcement learning algorithm is employed in this study to achieve stable motion control of the bicycle. Two sets of independent reward functions are designed for balancing and path tracking of the unmanned bicycle to improve control effectiveness. Simulation results demonstrate that the designed control strategy can effectively maintain the balance of the unmanned bicycle and accurately track the desired path. This research provides a novel approach and insight into the intelligent control of unmanned bicycles, showcasing the application potential of reinforcement learning-based motion control in highly complex systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on Bicycle Motion Control Based on Reinforcement Learning


    Contributors:
    Deng, Hang (author) / Chen, Xiai (author) / Dong, Mingze (author) / Yang, Jongkun (author)


    Publication date :

    2024-07-26


    Size :

    448081 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Natural Residual Reinforcement Learning for Bicycle Robot Control

    Zhu, Xianjin / Zheng, Xudong / Zhang, Qiyuan et al. | British Library Conference Proceedings | 2021


    Bicycle component motion control

    HO ALEXANDER KON-I / HAHN SAGE / KIM SANG | European Patent Office | 2020

    Free access

    BICYCLE COMPONENT MOTION CONTROL

    HO ALEXANDER KON-I / HAHN SAGE / KIM SANG | European Patent Office | 2023

    Free access

    Bicycle component motion control

    HO ALEXANDER KON-I / HAHN SAGE / KIM SANG | European Patent Office | 2022

    Free access

    BICYCLE COMPONENT MOTION CONTROL

    HO ALEXANDER KON-I / HAHN SAGE / KIM SANG | European Patent Office | 2020

    Free access