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Estimation, Search, and Planning (ESP) Research Group

Batch Informed Trees (BIT*)

Source Code

BIT* 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

Batch Informed Trees (BIT*) is an anytime optimal sampling-based planner that uses the principles of dynamic programming to search the implicit random geometric graph (RGG) given by batches of samples drawn from the problem domain. To do this efficiently, it uses a heuristic much like A* to prioritize both expansion towards the goal and high-quality paths. Experimental results show that it outperforms existing optimal sampling-based planning algorithms (e.g., RRT*, FMT*) in terms of computational cost to find equivalent results while still providing an anytime solution.

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  1. Authors
    1. J. D. Gammell
    2. T. D. Barfoot
    3. S. S. Srinivasa
    Title
    Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search
    Publication
    Journal
    The International Journal of Robotics Research (IJRR)
    Volume
    39
    Number
    5
    Pages
    543–567
    Date
    Code
    Code
    PDFs
    PDF
    Digital Object Identifier (DOI)
    doi: 10.1177/0278364919890396
    Google Scholar
    Google Scholar
  2. Award
    Author
    J. D. Gammell
    Title
    Informed anytime search for continuous planning problems
    Publication
    Type
    Ph.D. Thesis
    School
    University of Toronto
    Date
    Notes
    2017 CIPPRS Doctoral Dissertation Award
    Digital Object Identifier (DOI)
    doi: 1807/78630
    Google Scholar
    Google Scholar
  3. Authors
    1. J. D. Gammell
    2. S. S. Srinivasa
    3. T. D. Barfoot
    Title
    Batch Informed Trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs
    Publication
    Conference
    Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
    Pages
    3067–3074
    Location
    Seattle, WA, USA
    Date
    Code
    Code
    Videos
    Video
    PDFs
    PDF
    Digital Object Identifier (DOI)
    doi: 10.1109/ICRA.2015.7139620
    Google Scholar
    Google Scholar