The goal of the mobile robot path planning is to find a path from start position to final position. Path should be collision free and the path should also be optimized for the shortest path. This paper is rooted on implementation of Q-Learning algorithm to make robot learn how to avoid unknown obstacles. When the robot learned to avoid obstacles, it used a hybrid controller that uses the learned Q-table to navigate in an unknown environment to reach the goal. The proposed algorithm used ROS2 (Robot Operating System 2) foxy and Gazebo 11 to simulate the proposed algorithm on a mobile robot. Further, results are depicted through simulation and experiments to exhibit the effects of introduced path planning algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Q-Learning Strategy for Path Planning of Robots in Unknown Terrains


    Contributors:


    Publication date :

    2022-07-04


    Size :

    411408 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A novel optimal path-planning and following algorithm for wheeled robots on deformable terrains

    Taghavifar, Hamid / Rakheja, Subhash / Reina, Giulio | Elsevier | 2020


    Reactive Path Planning Approach for Docking Robots in Unknown Environment

    Peng Cui / Weisheng Yan / Yintao Wang | DOAJ | 2017

    Free access

    Legged Mobile Robots for Challenging Terrains

    Max Q.-H. Meng / Rui Song | DOAJ | 2022

    Free access

    Adaptive Rover Formation Control over Unknown Terrains

    Ganji, Farid / Joshi, Sanjay / Bayard, David | AIAA | 2005


    Space D*: a path-planning algorithm for multiple robots in unknown environments

    Silveira, Luan / Maffei, Renan de Queiroz / Botelho, Silvia Silva da Costa et al. | BASE | 2012

    Free access