Voxel Benchmarks for 3D Pathfinding: Sandstone, Descent, and Industrial Plants

Authors

  • Thomas K. Nobes Monash University
  • Daniel Harabor Monash University
  • Michael Wybrow Monash University
  • Stuart D.C. Walsh Monash University

DOI:

https://doi.org/10.1609/socs.v16i1.27283

Keywords:

Combinatorial Optimization, Search Space Discretization For Continuous State-space Problems, Real-life Applications, Analysis Of Search Algorithms

Abstract

Voxel grids are an increasingly common enabler for pathfinding in 3D spaces. Currently in this area there exists only a limited number of publicly available benchmarks. This makes it difficult to establish state-of-the-art performance and to compare the strengths and weaknesses of competing search techniques. In this work, we introduce three new and diverse sets of voxel benchmarks intended to help fill this gap. We further describe our methodology for generating and selecting a representative set of pathfinding queries. Our dataset comprises 46 distinct voxel maps and 92,000 problem instances. The data is drawn from distinct application domains: computer video games, industrial plant layouts and sandstone porosity scans. Featuring distinctive geometric properties and a variety of challenging query types, these new datasets allow practitioners to evaluate algorithmic performance across a variety of settings encountered when pathfinding in practice.

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Published

2023-07-02