In this work, we consider the problem of fully autonomous navigation of a vehicle to reach its desired goal in a constrained environment, specifically when the vehicle dynamics are unknown. To this end, we design a modular framework comprised of (i) LiDAR-based Hector SLAM for building a map of the environment, detecting obstacles, and tracking vehicle's conformance to any given trajectory, (ii) motion primitives-based kinodynamic Rapidly-exploring Random Trees (RRTs) for building an obstacle-free trajectory between a source and destination vehicle configuration, and finally, (iii) a Proportional Integral Derivative (PID) controller for trajectory tracking and disturbance rejection. We demonstrate the execution of our framework on an actual laboratory vehicle to perform complex maneuvers such as parallel parking, perpendicular parking, and reversing motion in a constrained environment under diverse weather and illumination conditions, such as sunny days, rainy days, and night-time. The video demonstrations are available here: https://youtu.be/0SsLzYKWIFs


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-time Autonomous Vehicle Navigation under Unknown Dynamics


    Beteiligte:


    Erscheinungsdatum :

    2023-09-24


    Format / Umfang :

    2914358 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Unmanned Ground Vehicle Autonomous Navigation in Unknown Indoor Environments

    Xie, Mengjiao / Song, Chunlei / Wang, Jiahui et al. | IEEE | 2023





    Autonomous navigation in unknown environments using machine learning

    Richter, Charles Andrew | BASE | 2017

    Freier Zugriff