Motion Primitive Planning

Motion Primitive Planning is a motion planning methodology that generates robot trajectories by composing predefined, parameterized motion segments, distinguished from other planning approaches through its reliance on a library of feasible, low-level dynamic actions to efficiently navigate complex environments.

39

Publications

2.2K

Citations

151

Authors

63

Institutions

Publications per year

2017–2026

20

Authors

151

  1. EC
    Efe Camci

    Nanyang Technological University, Singapore

    3 publications · 55 citations

    3

    h-index

  2. EK
    Erdal Kayacan

    Nanyang Technological University, Singapore

    3 publications · 55 citations

    3

    h-index

  3. ML
    Maxim Likhachev

    Carnegie Mellon University, United States

    3 publications · 208 citations

    3

    h-index

  4. SC
    Sachin Chitta

    Willow Wood (United States), United States

    2 publications · 196 citations

    2

    h-index

  5. BJ
    Benjamin J. Cohen

    University of California, Santa Barbara, United States

    2 publications · 196 citations

    2

    h-index

1–5 of 151

Venues

  1. Robotics and Autonomous Systems

    3 publications · 125 citations

    3

    h-index

  2. IEEE Robotics and Automation Letters

    3 publications · 193 citations

    3

    h-index

  3. IEEE Transactions on Robotics

    2 publications · 440 citations

    2

    h-index

  4. Autonomous Robots

    2 publications · 81 citations

    2

    h-index

  5. 1

    h-index

1–5