Sampling-based Trajectory Planning

Sampling-Based Trajectory Planning is a trajectory generation method that belongs to the class of motion planning techniques and is distinguished by its reliance on randomly or strategically sampling configurations in the state space to construct feasible trajectories, rather than exhaustively exploring or optimizing over the entire space.

277

Publications

15.7K

Citations

639

Authors

249

Institutions

Publications per year

2017–2026

130

Authors

639

  1. LE
    Lydia E. Kavraki

    Rice University, United States

    10 publications · 2.1K citations

    10

    h-index

  2. EP
    Erion Plaku

    Catholic University of America, United States

    10 publications · 547 citations

    10

    h-index

  3. EF
    Emilio Frazzoli

    Massachusetts Institute of Technology, United States

    8 publications · 530 citations

    8

    h-index

  4. SM
    Steven M. LaValle

    University of Illinois Urbana-Champaign, United States

    8 publications · 353 citations

    8

    h-index

  5. SK
    Sertaç Karaman

    Massachusetts Institute of Technology, United States

    8 publications · 616 citations

    8

    h-index

1–5 of 639

Venues

  1. The International Journal of Robotics Research

    IJRR · 15 publications · 2K citations

    14

    h-index

  2. IEEE Robotics and Automation Letters

    15 publications · 560 citations

    13

    h-index

  3. arXiv (Cornell University)

    Repository · 8 publications · 211 citations

    8

    h-index

  4. IEEE Transactions on Robotics

    7 publications · 691 citations

    7

    h-index

  5. Sensors

    5 publications · 200 citations

    5

    h-index

1–5