# Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids

arXiv: https://arxiv.org/abs/2602.21174 · HTML: https://mlsanity.com/paper/2602.21174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Theta*: Any-Angle Path Planning on Grids](https://mlsanity.com/paper/1401.3843/) (2014)
- [Multi-Camera LiDAR Inertial Extension to the Newer College Dataset](https://mlsanity.com/paper/2112.08854/) (2021)
- [Multi-Resolution A*](https://mlsanity.com/paper/2004.06684/) (2020)
- [Efficient volumetric mapping of multi-scale environments using wavelet-based compression](https://mlsanity.com/paper/2306.01279/) (2023)
- [Information-Theoretic Abstractions for Planning in Agents with Computational Constraints](https://mlsanity.com/paper/2005.09611/) (2020)

## Abstract (arXiv)

Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connectivity information. Yet widely used path planning methods such as sampling and trajectory optimization do not exploit this explicit connectivity information, and search-based methods such as A* suffer from scalability issues in large-scale high-resolution maps. In many applications, Euclidean shortest paths form the underpinning of the navigation system. For such applications, any-angle planning methods, which find optimal paths by connecting corners of obstacles with straight-line segments, provide a simple and efficient solution. In this paper, we present a method that has the optimality and completeness properties of any-angle planners while overcoming computational tractability issues common to search-based methods by exploiting multi-resolution representations. Extensive experiments on real and synthetic environments demonstrate the proposed approach's solution quality and speed, outperforming even sampling-based methods. The framework is open-sourced to allow the robotics and planning community to build on our research.