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.


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    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



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