# Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques

arXiv: https://arxiv.org/abs/2002.06589 · HTML: https://mlsanity.com/paper/2002.06589/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [Motion Policy Networks](https://mlsanity.com/paper/2210.12209/) (2022)
- [Potential Based Diffusion Motion Planning](https://mlsanity.com/paper/2407.06169/) (2024)
- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation](https://mlsanity.com/paper/2503.03045/) (2025)
- [Vision-based Wearable Steering Assistance for People with Impaired Vision in Jogging](https://mlsanity.com/paper/2408.00332/) (2024)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models](https://mlsanity.com/paper/2604.07084/) (2026)
- [Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot](https://mlsanity.com/paper/2108.06789/) (2021)
- [PPNet: A Two-Stage Neural Network for End-to-end Path Planning](https://mlsanity.com/paper/2401.09819/) (2024)
- [Fast End-to-End Generation of Belief Space Paths for Minimum Sensing Navigation](https://mlsanity.com/paper/2409.12902/) (2024)

## Abstract (arXiv)

Path planning is an active area of research essential for many applications in robotics. Popular techniques include graph-based searches and sampling-based planners. These approaches are powerful but have limitations. This paper continues work to combine their strengths and mitigate their limitations using a unified planning paradigm. It does this by viewing the path planning problem as the two subproblems of search and approximation and using advanced graph-search techniques on a sampling-based approximation. This perspective leads to Advanced BIT*. ABIT* combines truncated anytime graph-based searches, such as ATD*, with anytime almost-surely asymptotically optimal sampling-based planners, such as RRT*. This allows it to quickly find initial solutions and then converge towards the optimum in an anytime manner. ABIT* outperforms existing single-query, sampling-based planners on the tested problems in ℝ4 and ℝ8, and was demonstrated on real-world problems with NASA/JPL-Caltech.