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

2025-06-21

Robotics

AI Summary

This work addresses the challenge of achieving optimality, completeness, and computational efficiency in path planning within large-scale, high-resolution 3D environments. Traditional methods struggle to simultaneously satisfy these criteria and fail to exploit the explicit connectivity offered by multi-resolution voxel maps. To overcome this limitation, the paper proposes a hierarchical search framework that tightly integrates any-angle path planning with multi-resolution 3D grids. By leveraging obstacle corner-guided line-of-sight connections, hierarchical graph construction, and an efficient search mechanism, the approach guarantees Euclidean shortest-path optimality and completeness while significantly improving computational performance. Experimental results demonstrate consistent superiority over state-of-the-art sampling- and search-based algorithms in both real-world and synthetic scenarios. The implementation has been open-sourced to facilitate community adoption and further research.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• any-angle path planning
• multi-resolution 3D grids
• hierarchical planning
• computational tractability
• Euclidean shortest paths

Innovation

• any-angle path planning
• multi-resolution grids
• hierarchical planning
• 3D volumetric mapping
• optimal pathfinding