Sparse Roadmaps

Sparse Roadmaps is a probabilistic roadmap method in motion planning that distinguishes itself by significantly reducing the density of sampled configurations and connecting edges, thereby optimizing computational efficiency while maintaining sufficient connectivity for path discovery in high-dimensional spaces.

29

Publications

1.4K

Citations

78

Authors

36

Institutions

Publications per year

2017–2026

10

Variants

Sparse Probabilistic Roadmaps

Authors

78

  1. KE
    Kostas E. Bekris

    Rutgers, The State University of New Jersey, United States

    6 publications · 353 citations

    6

    h-index

  2. AD
    Andrew Dobson

    Rutgers, The State University of New Jersey, United States

    3 publications · 193 citations

    3

    h-index

  3. JD
    James D. Marble

    University of Nevada, Reno, United States

    3 publications · 160 citations

    3

    h-index

  4. NM
    Nancy M. Amato

    Texas A&M University, United States

    3 publications · 83 citations

    3

    h-index

  5. DS
    Doron Shaharabani

    Tel Aviv University, Israel

    2 publications · 43 citations

    2

    h-index

1–5 of 78

Venues

  1. The International Journal of Robotics Research

    IJRR · 3 publications · 158 citations

    3

    h-index

  2. IEEE Robotics and Automation Letters

    3 publications · 76 citations

    3

    h-index

  3. IEEE Transactions on Robotics

    2 publications · 417 citations

    2

    h-index

  4. Sensors

    1 publications · 34 citations

    1

    h-index

  5. 1

    h-index

1–5