Learning-based Swarm Control

Learning-Based Swarm Control is a control framework for heterogeneous robot swarms in which collective behaviors and coordination strategies are autonomously acquired and refined through machine learning techniques, distinguishing it from rule-based or predefined control paradigms by its reliance on data-driven adaptation and experience-based improvement.

29

Publications

1.2K

Citations

103

Authors

59

Institutions

Publications per year

2017–2026

23

Authors

103

  1. GL
    Guangyu Liu

    Hangzhou Dianzi University, China

    2 publications · 46 citations

    2

    h-index

  2. JW
    Jiajun Wang

    Hangzhou Dianzi University, China

    2 publications · 46 citations

    2

    h-index

  3. FP
    Francesco Pase

    University of Padua, Italy

    1 publications · 28 citations

    1

    h-index

  4. WM
    William M. Spears

    United States Naval Research Laboratory, United States

    1 publications · 36 citations

    1

    h-index

  5. NA
    Naila Aziza Houacine

    University of Sciences and Technology Houari Boumediene, Algeria

    1 publications · 23 citations

    1

    h-index

1–5 of 103

Venues

  1. Neural Computing and Applications

    2 publications · 69 citations

    2

    h-index

  2. IEEE Transactions on Robotics

    1 publications · 77 citations

    1

    h-index

  3. Vehicular Communications

    1 publications · 43 citations

    1

    h-index

  4. Information Sciences

    1 publications · 22 citations

    1

    h-index

  5. Applied Soft Computing

    1 publications · 27 citations

    1

    h-index

1–5