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Estimation, Search, and Planning (ESP) Research Group

Adaptively Informed Trees (AIT*)

Source Code

AIT* is now part of the Open Motion Planning Library (OMPL). You can find details on how to download and install OMPL on their website.

Description

Adaptively Informed Trees (AIT*) is an almost-surely asymptotically optimal path planning algorithm. It uses an asymmetric bidirectional search in which both searches continuously inform each other. This allows it to find initial solutions quickly by allocating computational resources on promising paths. AIT* outperforms other almost-surely asymptotically optimal algorithms (e.g., RRT* and BIT*) on problems with expensive edge evaluations by finding initial solutions as fast as RRT-Connect and converging to the optimum in an anytime manner.

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  1. Authors
    1. M. P. Strub
    2. J. D. Gammell
    Title
    Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*): Asymmetric bidirectional sampling-based path planning
    Publication
    Journal
    The International Journal of Robotics Research (IJRR)
    Volume
    41
    Number
    4
    Pages
    390–417
    Date
    Videos
    Video
    PDFs (Recommended)
    PDF (original formatting)
    Digital Object Identifier (DOI)
    doi: 10.1177/02783649211069572
    arXiv
    Google Scholar
    Google Scholar
  2. Author
    M. P. Strub
    Title
    Leveraging multiple sources of information to search continuous spaces
    Publication
    Type
    D.Phil. Thesis
    School
    University of Oxford
    Date
    PDFs
    PDF
    Google Scholar
    Google Scholar
  3. Authors
    1. M. P. Strub
    2. J. D. Gammell
    Title
    Adaptively Informed Trees (AIT*): Fast asymptotically optimal path planning through adaptive heuristics
    Publication
    Conference
    Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
    Pages
    3191–3198
    Date
    Code
    Code
    Videos
    Video
    Presentations
    Presentation
    PDFs
    PDF
    Digital Object Identifier (DOI)
    doi: 10.1109/ICRA40945.2020.9197338
    arXiv
    Google Scholar
    Google Scholar