Graph Search Planning

Graph Search Planning is a motion planning method that belongs to the class of discrete search-based approaches, wherein feasible paths in a configuration space are computed by exploring a graph representation of the environment through systematic node expansion and edge traversal to find an optimal or feasible solution from start to goal.

62

Publications

2.2K

Citations

161

Authors

70

Institutions

Publications per year

2017–2026

14

Variants

Search-based Motion Planning

Authors

161

  1. ML
    Maxim Likhachev

    Carnegie Mellon University, United States

    4 publications · 215 citations

    4

    h-index

  2. SK
    Subbarao Kambhampati

    Arizona State University, United States

    4 publications · 80 citations

    4

    h-index

  3. RS
    Roland Siegwart

    ETH Zurich, Switzerland

    3 publications · 67 citations

    3

    h-index

  4. MR
    Martin Rufli

    ETH Zurich, Switzerland

    3 publications · 67 citations

    3

    h-index

  5. DN
    Dana Nau

    University of Maryland, College Park, United States

    2 publications · 36 citations

    2

    h-index

1–5 of 161

Venues

  1. Journal of Artificial Intelligence Research

    JAIR · 3 publications · 136 citations

    3

    h-index

  2. 3

    h-index

  3. National Conference on Artificial Intelligence

    Conference · 2 publications · 38 citations

    2

    h-index

  4. Repository for Publications and Research Data (ETH Zurich)

    Repository · 2 publications · 41 citations

    2

    h-index

  5. IEEE Robotics and Automation Letters

    2 publications · 96 citations

    2

    h-index

1–5