# Multi-Resolution A*

arXiv: https://arxiv.org/abs/2004.06684 · HTML: https://mlsanity.com/paper/2004.06684/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 13 of 13)

- [Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability](https://mlsanity.com/paper/2101.05916/) (2021)
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- [A case study of spatiotemporal forecasting techniques for weather forecasting](https://mlsanity.com/paper/2209.14782/) (2022)
- [AMRA*: Anytime Multi-Resolution Multi-Heuristic A*](https://mlsanity.com/paper/2110.05328/) (2021)
- [Enhancing Deep Learning Performance of Massive MIMO CSI Feedback](https://mlsanity.com/paper/2208.11333/) (2022)
- [Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales](https://mlsanity.com/paper/2310.00010/) (2023)
- [Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization](https://mlsanity.com/paper/2502.12672/) (2025)
- [Cryo-em images are intrinsically low dimensional](https://mlsanity.com/paper/2504.11249/) (2025)
- [Domain Adaptive Lung Nodule Detection in X-ray Image](https://mlsanity.com/paper/2407.19397/) (2024)
- [Search-Based Robot Motion Planning With Distance-Based Adaptive Motion Primitives](https://mlsanity.com/paper/2507.01198/) (2025)
- [Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learning](https://mlsanity.com/paper/2511.06958/) (2025)
- [Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids](https://mlsanity.com/paper/2602.21174/) (2026)
- [From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology](https://mlsanity.com/paper/2608.03508/) (2026)

## Abstract (arXiv)

Heuristic search-based planning techniques are commonly used for motion planning on discretized spaces. The performance of these algorithms is heavily affected by the resolution at which the search space is discretized. Typically a fixed resolution is chosen for a given domain. While a finer resolution allows for better maneuverability, it significantly increases the size of the state space, and hence demands more search efforts. On the contrary, a coarser resolution gives a fast exploratory behavior but compromises on maneuverability and the completeness of the search. To effectively leverage the advantages of both high and low resolution discretizations, we propose Multi-Resolution A* (MRA*) algorithm, that runs multiple weighted-A*(WA*) searches having different resolution levels simultaneously and combines the strengths of all of them. In addition to these searches, MRA* uses one anchor search to control expansions from these searches. We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete. We performed experiments on several motion planning domains including 2D, 3D grid planning and 7 DOF manipulation planning and compared our approach with several search-based and sampling-based baselines.