Classical Q-learning algorithm is a reinforcement of learning algorithm that has been applied in path planning of mobile robots. However, classical Q-learning suffers from slow convergence rate and high computational time. This is due to the random decision making for direction during the early stage of path planning. Such weakness curtails the ability of mobile robot to make instantaneous decision in real world application. In this study, the distance aspect and moving target concept were added to Q-learning in order to enhance the direction decision making ability and bypassing dead end. With the addition of these features, Q-learning is able to converge faster and generate shorter path. Consequently, the proposed improved Q-learning is able to achieve average improvement of 29.34-94.85%, 18.29-29.69% and 75.76-99.50% in time used, shortest distance and total distance used, respectively.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Mobile Robot Path Planning using Q-Learning with Guided Distance and Moving Target Concept


    Beteiligte:
    Ong, Pauline (Autor:in) / Low, Ee Soong (Autor:in) / Low, Cheng Yee (Autor:in)

    Erscheinungsdatum :

    2020-12-03


    Anmerkungen:

    International Journal of Integrated Engineering; Vol 13 No 2 (2021): Special Issue 2021: Mechanical Engineering; 177-188 ; 2600-7916 ; 2229-838X


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629





    Mobile Robot Path Planning Using Deep reinforcement learning

    Abedi, Ali / Anari, Reza Ghaderizadeh / Mohammadi, Hossein | IEEE | 2023


    Path planning-by-learning with a Nomad 200 mobile robot

    Araujo, R. / Almeida, A.T. de | Tema Archiv | 1997



    Mobile Robot Path Planning Utilizing Probability Recursive Function

    Ethar H. Khalil / Bahaa I. Kazem | BASE | 2009

    Freier Zugriff