Inspection-SPARS：面向任务的稀疏路标图用于检测规划
Inspection-SPARS: Task-Oriented Sparse Roadmaps for Inspection Planning
- Technion–Israel Institute of Technology（以色列理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Inspection-SPARS提出首个面向检测任务的稀疏化方法，在保持POI覆盖和路径质量的同时，将路标图规模缩减4-8倍，使求解器路径缩短达25%。
AI中文摘要：
检测规划旨在寻找一条最短的无碰撞机器人路径，以观察给定的兴趣点（POIs）。基于采样的方法将这一连续问题简化为在离散路标图上的图检测规划（GIP）问题，随后使用组合求解器进行求解。密集路标图能够捕获多样的检测视角和运动捷径，因此能产生更高质量的解决方案，但它们会带来庞大的组合搜索空间，使得最先进的GIP求解器难以在实用时间预算内找到良好解决方案。路标图稀疏化——将密集路标图重构为保持连通性和路径长度的紧凑表示——可以减轻这一负担。然而，现有的稀疏化方法要么对底层检测任务不敏感，要么仅致力于确保POI的覆盖而不考虑所得检测计划的质量。我们提出了Inspection-SPARS，据我们所知，这是第一个相对于密集路标图具有POI覆盖和路径质量保证的检测路标图稀疏化器。为此，我们将流行的任务无关稀疏化器SPARS框架从纯几何标准推广到面向任务的标准，引入了一种检测感知的顶点接纳机制，该机制将POI覆盖视为与连通性和路径质量并列的一级稀疏化标准。在真实3D环境中的实验表明，Inspection-SPARS在保持覆盖的同时将顶点和边数量减少了4-8倍，使得GIP求解器能够计算出比使用密集路标图或最先进的检测路标图短达25%的路径。更广泛地说，Inspection-SPARS表明，稀疏化可以在不牺牲解决方案质量保证的情况下实现任务感知。
英文摘要：
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.
