Terrain traversability analysis is a fundamental issue to achieve the autonomy of a robot at off-road environments. Geometry-based and appearance-based methods have been studied in decades, while behavior-based methods exploiting learning from demonstration (LfD) are new trends. Behavior-based methods learn cost functions that guide trajectory planning in compliance with experts' demonstrations, which can be more scalable to various scenes and driving behaviors. This research proposes a method of off-road traversability analysis and trajectory planning using Deep Maximum Entropy Inverse Reinforcement Learning. To incorporate the vehicle's kinematics while solving the problem of exponential increase of state-space complexity, two convolutional neural networks, i.e., RL ConvNet and Svf ConvNet, are developed to encode kinematics into convolution kernels and achieve efficient forward reinforcement learning. We conduct experiments in off-road environments. Scene maps are generated using 3D LiDAR data, and expert demonstrations are either the vehicle's real driving trajectories at the scene or synthesized ones to represent specific behaviors such as crossing negative obstacles. Different cost functions of traversability analysis are learned and tested at various scenes of capability in guiding the trajectory planning of different behaviors. We also demonstrate the peformance and computation efficiency of the proposed method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning


    Beteiligte:
    Zhu, Zeyu (Autor:in) / Li, Nan (Autor:in) / Sun, Ruoyu (Autor:in) / Xu, Donghao (Autor:in) / Zhao, Huijing (Autor:in)


    Erscheinungsdatum :

    19.10.2020


    Format / Umfang :

    2480336 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    OFF-ROAD AUTONOMOUS VEHICLES TRAVERSABILITY ANALYSIS AND TRAJECTORY PLANNING BASED ON DEEP INVERSE REINFORCEMENT LEARNING

    Zhu, Zeyu / Li, Nan / Sun, Ruoyu et al. | British Library Conference Proceedings | 2020


    Learning traversability models for autonomous mobile vehicles

    Shneier, M. | British Library Online Contents | 2008


    Autonomous Vehicles Traversability Mapping Fusing Semantic–Geometric in Off-Road Navigation

    Bo Zhang / Weili Chen / Chaoming Xu et al. | DOAJ | 2024

    Freier Zugriff

    Traversability Analysis and Path Planning for Autonomous Wheeled Vehicles on Rigid Terrains

    Nan Wang / Xiang Li / Zhe Suo et al. | DOAJ | 2024

    Freier Zugriff

    Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning

    Naveed, Kaleb Ben / Qiao, Zhiqian / Dolan, John M. | IEEE | 2021