Mobile robot autonomous path planning is an essential factor for its wide deployment in real-world applications. Conventional sampling-based algorithms have gained tremendous success in the path planning field, but they usually take much time to find the optimal solution so that the planning quality (evaluated with time cost and path length) cannot be guaranteed. In this paper, based on Gaussian Mixture Regression (GMR) and the family of Rapidly-exploring Random Tree (RRT) schemes, we propose the GMR-RRT* algorithm to achieve fast path planning for mobile robots. The proposed GMR-RRT* consists of learning navigation behaviors from human demonstrations and planning a high-quality path for the robot. Using the GMR, the key features of human demonstrations are captured to form a probability density distribution of the human trajectory in the current environment. This distribution is further utilized to guide the RRT scheme’s sampling process to generate a feasible path in the current environment quickly. We test the proposed GMR-RRT* in different environments, comparing it with three state-of-the-art sampling-based algorithms. The experimental results demonstrate that the GMR-RRT* algorithm can achieve better performance in terms of time cost, memory usage, and path length.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    GMR-RRT*: Sampling-Based Path Planning Using Gaussian Mixture Regression


    Beteiligte:
    Wang, Jiankun (Autor:in) / Li, Tingguang (Autor:in) / Li, Baopu (Autor:in) / Meng, Max Q.-H. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-09-01


    Format / Umfang :

    4605098 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Gaussian Mixture Approximation by Another Gaussian Mixture for "Blob" Filter Re-Sampling

    Psiaki, Mark / Schoenberg, Jonathan / Miller, Isaac | AIAA | 2010



    Sequential Monte Carlo Filtering with Gaussian Mixture Sampling

    Yun, Sehyun / Zanetti, Renato | AIAA | 2019


    Sampling-Based Path Planning on Configuration-Space Costmaps

    Jaillet, Léonard / Cortés, Juan / Simeon, Thierry | BASE | 2010

    Freier Zugriff

    Sampling-based Path Planning for an Autonomous Helicopter

    Pettersson, Per Olof | BASE | 2006

    Freier Zugriff