Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search

@article{Gammell2020BatchIT,
  title={Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search},
  author={J. Gammell and T. Barfoot and S. Srinivasa},
  journal={The International Journal of Robotics Research},
  year={2020},
  volume={39},
  pages={543 - 567}
}
  • J. Gammell, T. Barfoot, S. Srinivasa
  • Published 2020
  • Computer Science, Engineering, Mathematics
  • The International Journal of Robotics Research
  • Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as used by informed graph-based searches and anytime sampling-based planners, respectively. Informed graph-based searches, such as A*, traditionally use heuristics to search a priori graphs in order of potential… CONTINUE READING
    Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics
    • 2
    • PDF
    Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
    • 4
    • PDF
    A Survey of Asymptotically Optimal Sampling-based Motion Planning Methods
    Multilevel Motion Planning: A Fiber Bundle Formulation
    Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 73 REFERENCES
    BIT*: Batch Informed Trees for Optimal Sampling-based Planning via Dynamic Programming on Implicit Random Geometric Graphs
    • 29
    • PDF
    Informed Anytime Search for Continuous Planning Problems
    • 9
    • PDF
    Informed Sampling for Asymptotically Optimal Path Planning
    • 31
    • PDF
    Truncated incremental search
    • 20
    • Highly Influential
    • PDF
    Anytime search in dynamic graphs
    • 198
    • PDF
    Multi-Heuristic A*
    • 89
    • PDF
    Use of relaxation methods in sampling-based algorithms for optimal motion planning
    • 110
    • PDF
    RRTX: Asymptotically optimal single-query sampling-based motion planning with quick replanning
    • 81
    • PDF