Probabilistic Path Planning

Probabilistic Path Planning is a path planning methodology that belongs to the class of stochastic motion planning techniques, distinguished by its incorporation of probabilistic models to account for uncertainty in environment, sensor data, or system dynamics when generating feasible trajectories for obstacle avoidance.

229

Publications

20.8K

Citations

611

Authors

240

Institutions

Publications per year

2017–2026

73

Variants

Probabilistic Motion Planning

Authors

611

  1. JL
    Jean‐Paul Laumond

    Laboratoire d'Analyse et d'Architecture des Systèmes, France

    8 publications · 593 citations

    8

    h-index

  2. JP
    Jonathan P. How

    Massachusetts Institute of Technology, United States

    6 publications · 581 citations

    6

    h-index

  3. TS
    Thierry Siméon

    Laboratoire d'Analyse et d'Architecture des Systèmes, France

    5 publications · 751 citations

    5

    h-index

  4. LE
    Lydia E. Kavraki

    Rice University, United States

    5 publications · 1.8K citations

    5

    h-index

  5. MO
    M.H. Overmars

    Utrecht University, Netherlands

    4 publications · 178 citations

    4

    h-index

1–5 of 611

Venues

  1. Journal of Intelligent & Robotic Systems

    5 publications · 237 citations

    5

    h-index

  2. IEEE Access

    4 publications · 200 citations

    4

    h-index

  3. Sensors

    4 publications · 195 citations

    4

    h-index

  4. The International Journal of Robotics Research

    IJRR · 4 publications · 680 citations

    4

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 152 citations

    4

    h-index

1–5