In the fight against COVID-19, many robots replace human employees in various tasks that involve a risk of infection. Among these tasks, the fundamental problem of navigating robots among crowds, named robot crowd navigation, remains open and challenging. Therefore, we propose HGAT-DRL, a heterogeneous GAT-based deep reinforcement learning algorithm. This algorithm encodes the constrained human-robot-coexisting environment in a heterogeneous graph consisting of four types of nodes. It also constructs an interactive agent-level representation for objects surrounding the robot, and incorporates the kinodynamic constraints from the non-holonomic motion model into the deep reinforcement learning (DRL) framework. Simulation results show that our proposed algorithm achieves a success rate of 92%, at least 6% higher than four baseline algorithms. Furthermore, the hardware experiment on a Fetch robot demonstrates our algorithm’s successful and convenient migration to real robots.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Navigating Robots in Dynamic Environment With Deep Reinforcement Learning


    Beteiligte:
    Zhou, Zhiqian (Autor:in) / Zeng, Zhiwen (Autor:in) / Lang, Lin (Autor:in) / Yao, Weijia (Autor:in) / Lu, Huimin (Autor:in) / Zheng, Zhiqiang (Autor:in) / Zhou, Zongtan (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-12-01


    Format / Umfang :

    3159204 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    NAVIGATING AERIAL VEHICLES USING DEEP REINFORCEMENT LEARNING

    CANDIDO SALVATORE J / GONG JUN / GENDRON-BELLEMARE MARC et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Systems and Methods for Navigating Aerial Vehicles Using Deep Reinforcement Learning

    CANDIDO SALVATORE J / GONG JUN / GENDRON-BELLEMARE MARC | Europäisches Patentamt | 2021

    Freier Zugriff

    Navigating autonomous coverage robots

    OZICK DANIEL N / OKERHOLM ANDREA M / MAMMEN JEFFREY W et al. | Europäisches Patentamt | 2015

    Freier Zugriff

    Reinforcement learning with scene decomposition for navigating complex environments

    BOUTON MAXIME / NAKHAEI SARVEDANI ALIREZA / FUJIMURA KIKUO et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    Navigating semi-autonomous mobile robots

    STORFER LIOR / GAIDAR TAMARA / LAPIDOT IDO | Europäisches Patentamt | 2024

    Freier Zugriff