Abstract
Path planning in large-scale, complex 3D environments is fundamentally constrained by a trade-off between path quality and computational speed. This paper presents RUSH (Recursive and Scalable 3D Coarse To Fine Path Planning), a hierarchical framework that resolves this trade-off. RUSH decomposes the long-range planning task into a coarse plan followed by fine-grained, independent subproblems that can be solved in parallel. These subproblems are addressed by a unified, diffusion-based network that refines an initial estimate path by learning its residual to an optimal path. This approach allows RUSH to leverage rich geometric information directly from 3D voxel maps without being bottlenecked by the full map’s complexity. We validate our method on large-scale outdoor (KITTI, MulRan) and indoor (HM3D) datasets, each spanning a 200m×200m×6m map. Experimental results demonstrate that RUSH generates feasible, high-quality paths with remarkable efficiency, achieving up to a 12.59× speedup over a hierarchically accelerated A* baseline, while maintaining a path cost within 24% of the optimal solution. This performance gain positions RUSH as a powerful and practical solution for applications requiring rapid global path planning in large-scale 3D maps.
