In this paper, we present an improved RRT-based motion planner for autonomous vehicles to effectively navigate in cluttered environments with narrow passages. The planner first presents X-test that can identify passable narrow passages, and then perform an efficient obstacles-based extension operation within passable narrow passages. In order to generate a smooth trajectory for the vehicle to execute, a post-process algorithm with trajectory optimization is proposed. For the purpose of demonstrate benefits of our method, the proposed motion planner is implemented and tested on a real autonomous vehicle in cluttered scenarios with narrow passages. Experimental results show that our planner achieves up to 13.8 times and 7.6 times performance improvements over a basic RRT planner and a Bi-RRT planner respectively. Moreover, the resulting path of our planner is more smooth and reasonable.


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    Titel :

    An improved RRT-based motion planner for autonomous vehicle in cluttered environments


    Beteiligte:
    Du, Mingbo (Autor:in) / Chen, Jiajia (Autor:in) / Zhao, Pan (Autor:in) / Liang, Huawei (Autor:in) / Xin, Yu (Autor:in) / Mei, Tao (Autor:in)


    Erscheinungsdatum :

    2014-05-01


    Format / Umfang :

    376101 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch