Advanced BIT* (ABIT*): Sampling-Based Planning with Advanced Graph-Search Techniques
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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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 4; 2025: 1; 2026: 2.
10 papers.
- 2022Motion Policy Networkscited by 53
- 2024Potential Based Diffusion Motion Planningcited by 40
- 2022Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networkscited by 16
- 2025ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulationcited by 10
- 2024Vision-based Wearable Steering Assistance for People with Impaired Vision in Joggingcited by 1
- 2026Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunitiescited by 1
- 2021Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robotcited by 0
- 2024PPNet: A Two-Stage Neural Network for End-to-end Path Planningcited by 0
- 2024Fast End-to-End Generation of Belief Space Paths for Minimum Sensing Navigationcited by 0
- 2026Flow Motion Policy: Manipulator Motion Planning with Flow Matching Modelscited by 0
- 2026Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models8 Apr 2026
- 2026Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities25 Mar 2026
- 2025ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation4 Mar 2025
- 2024Fast End-to-End Generation of Belief Space Paths for Minimum Sensing Navigation19 Sep 2024
- 2024Vision-based Wearable Steering Assistance for People with Impaired Vision in Jogging1 Aug 2024
- 2024Potential Based Diffusion Motion Planning8 Jul 2024
- 2024PPNet: A Two-Stage Neural Network for End-to-end Path Planning18 Jan 2024
- 2022Motion Policy Networks21 Oct 2022
- 2022Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks17 Oct 2022
- 2021Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot15 Aug 2021