The environment map is the basis of robot navigation. Autonomous robot exploration is the process by which a robot autonomously constructs a map in an unknown environment. How to make the robot reduce the traveling distance when completing an unknown environment is a worthwhile research problem. This work proposes a transformer-based decision network for autonomous exploration and a deep reinforcement learning framework for autonomous robot exploration tasks. Experiments show that our proposed method is feasible and saves an average of 6.7% on distance traveled compared to traditional methods.


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    Title :

    A Transformer-Based Robot Autonomous Exploration Method


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liu, Lianqing (editor) / Niu, Yifeng (editor) / Fu, Wenxing (editor) / Qu, Yi (editor) / Wang, Rui (author) / Zhao, Xin (author) / Lyu, Ming (author) / Zhang, Jie (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2024 ; Shenyang, China September 19, 2024 - September 21, 2024



    Publication date :

    2025-03-28


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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