Inspection-SPARS: Task-Oriented Sparse Roadmaps for Inspection Planning

2026-09-28

AI Summary

This study addresses the challenge of excessive combinatorial search spaces in dense roadmaps, which hinders high-quality robot inspection planning within time limits. We propose a task-oriented sparsification method that extends the SPARS framework by introducing an inspection-aware vertex admission mechanism. Using point-of-interest (POI) coverage as the core criterion and integrating graph search with combinatorial optimization solvers, our approach constructs compact roadmaps while preserving connectivity and path quality. As the first sparsification scheme to jointly guarantee POI coverage and path quality, it overcomes the limitations of conventional geometry-based or task-agnostic methods. Experiments demonstrate that the proposed method reduces the number of nodes and edges by 4–8× and yields paths 25% shorter than those derived from dense roadmaps, significantly improving both computational efficiency and solution quality.

Abstract

Inspection planning seeks a minimum-length collision-free robot tour that observes a given set of points of interest (POIs). Sampling-based methods reduce this continuous problem to a graph inspection planning (GIP) problem over a discrete roadmap, which is then solved using combinatorial solvers. Dense roadmaps capture diverse inspection viewpoints and motion shortcuts, and thus admit higher-quality solutions, but they induce large combinatorial search spaces on which state-of-the-art GIP solvers struggle to find good solutions within practical time budgets. Roadmap sparsification---restructuring a dense roadmap into a compact representation that preserves connectivity and path lengths---can alleviate this burden. However, existing sparsification approaches are either agnostic to the underlying inspection task, or strive to ensure coverage of the POIs without accounting for the quality of the resulting inspection plan. We present Inspection-SPARS, which is, to our knowledge, the first inspection-roadmap sparsifier with POI coverage and path-quality guarantees relative to the dense roadmap. To this end, we generalize the SPARS framework, a popular task-agnostic sparsifier, from purely geometric criteria to task-oriented ones, introducing an inspection-aware vertex admission mechanism that treats POI coverage as a first-class sparsification criterion alongside connectivity and path quality. Experiments in realistic 3D environments show that Inspection-SPARS reduces vertex and edge counts by 4-8x while preserving coverage, allowing the GIP solver to compute tours up to 25% shorter than with the dense roadmap or state-of-the-art inspection roadmap. More broadly, Inspection-SPARS shows that sparsification can be made task-aware without sacrificing guarantees on solution quality.

Problem

• Inspection planning
• Roadmap sparsification
• Graph inspection planning
• Points of interest coverage
• Path quality

Innovation

• Inspection Planning
• Roadmap Sparsification
• Task-Oriented Sparse Roadmaps
• Graph Inspection Planning
• POI Coverage Guarantee