Probabilistic Coverage Control

Probabilistic Coverage Control is a coverage control framework that distinguishes itself by incorporating probabilistic models to govern sensor placement and sensing performance, accounting for uncertainty in environment representation, sensor measurements, or actuation, thereby optimizing coverage in stochastic or partially known environments.

23

Publications

2.6K

Citations

65

Authors

34

Institutions

Publications per year

2017–2026

2

Authors

65

  1. MF
    Masayuki Fujita

    Tokyo Institute of Technology, Japan

    2 publications · 106 citations

    2

    h-index

  2. MZ
    Minyi Zhong

    Boston University, United States

    2 publications · 228 citations

    2

    h-index

  3. YW
    Yu Wang

    University of North Carolina at Charlotte, United States

    2 publications · 183 citations

    2

    h-index

  4. TH
    Takeshi Hatanaka

    Tokyo Institute of Technology, Japan

    2 publications · 106 citations

    2

    h-index

  5. CG
    Christos G. Cassandras

    Boston University, United States

    2 publications · 228 citations

    2

    h-index

1–5 of 65

Venues

  1. IEEE Transactions on Mobile Computing

    TMCCI · 2 publications · 88 citations

    2

    h-index

  2. IFAC Proceedings Volumes

    1 publications · 43 citations

    1

    h-index

  3. International Journal of Sensor Networks

    1 publications · 13 citations

    1

    h-index

  4. 1

    h-index

  5. IEEE Transactions on Automatic Control

    1 publications · 201 citations

    1

    h-index

1–5