The Rapidly-exploring Random Tree (RRT) is a classical algorithm of motion planning based on incremental sampling, which is widely used to solve the planning problem of mobile robots. But it, due to the meandering path, the inaccurate terminal state and the slow exploration, is often inefficient in many applications such as autonomous road vehicles. To address these issues and considering the realistic context of autonomous road vehicles, this paper proposes a fast RRT algorithm that introduces an off-line template set based on the traffic scenes and an aggressive extension strategy of search tree. Both improvements can lead to a faster and more accurate RRT towards the goal. Meanwhile, our approach combines the closed-loop prediction approach using the model of vehicle, which can smooth the portion of off-line template and the portion of on-line tree generated, while a trajectory and control sequence for the vehicle would be obtained. Experimental results illustrate that our method is fast and efficient in solving planning queries of autonomous road vehicle in urban environments.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A fast RRT algorithm for motion planning of autonomous road vehicles


    Beteiligte:
    Ma, Liang (Autor:in) / Xue, Jianru (Autor:in) / Kawabata, Kuniaki (Autor:in) / Zhu, Jihua (Autor:in) / Ma, Chao (Autor:in) / Zheng, Nanning (Autor:in)


    Erscheinungsdatum :

    01.10.2014


    Format / Umfang :

    691955 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Motion Planning of Autonomous Road Vehicles by Particle Filtering

    Berntorp, Karl / Hoang, Tru / Di Cairano, Stefano | IEEE | 2019


    Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning

    Yang Li / Fubin Zhang / Demin Xu et al. | DOAJ | 2017

    Freier Zugriff

    Pseudospectral Motion Planning for Autonomous Vehicles

    Gong, Qi / Lewis, L. R. / Ross, I. Michael | AIAA | 2009


    Dynamic motion planning of autonomous vehicles

    Shiller, Z. / Gwo, Y.R. | Tema Archiv | 1991