Rapidly Exploring Random Trees

Rapidly Exploring Random Trees is a randomized sampling-based motion-planning algorithm and associated tree data structure that incrementally builds a space-filling tree in high-dimensional configuration spaces by extending from the nearest existing node toward randomly drawn samples, thereby efficiently exploring large unsearched regions while accommodating obstacles and differential constraints.

639

Publications

48K

Citations

1.8K

Authors

528

Institutions

Publications per year

2017–2026

345

Authors

1.8K

  1. MQ
    Max Q.‐H. Meng

    Chinese University of Hong Kong, Hong Kong SAR China

    18 publications · 1.4K citations

    17

    h-index

  2. JP
    Jonathan P. How

    Massachusetts Institute of Technology, United States

    15 publications · 2.3K citations

    15

    h-index

  3. JW
    Jiankun Wang

    Chinese University of Hong Kong, Hong Kong SAR China

    14 publications · 1.4K citations

    14

    h-index

  4. EF
    Emilio Frazzoli

    Massachusetts Institute of Technology, United States

    15 publications · 4.1K citations

    14

    h-index

  5. JK
    James Kuffner

    Carnegie Mellon University, United States

    13 publications · 8.8K citations

    13

    h-index

1–5 of 1.8K

Venues

  1. IEEE Access

    18 publications · 1.2K citations

    16

    h-index

  2. Sensors

    19 publications · 822 citations

    16

    h-index

  3. IEEE Transactions on Robotics

    15 publications · 1.3K citations

    14

    h-index

  4. International Journal of Advanced Robotic Systems

    ARS · 16 publications · 598 citations

    13

    h-index

  5. 12

    h-index

1–5