グラフ誘導と軌道最適化による階層的な情報経路計画
Hierarchical Informative Path Planning via Graph Guidance and Trajectory Optimization
複雑な環境でガウス過程の不確かさを減らすため、グラフベースの大域計画とスプラインによる局所軌道最適化を組み合わせた階層的手法を提案し、従来法より高精度かつ高速に情報収集経路を計画できることを示した。
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著者: Avraiem Iskandar, Shamak Dutta, Kevin Murrant, Yash Vardhan Pant, Stephen L. Smith
分類: cs.RO
原文アブストラクト
We study informative path planning (IPP) with travel budgets in cluttered environments, where an agent collects measurements of a latent field modeled as a Gaussian process (GP) to reduce uncertainty at target locations. Graph-based solvers provide global guarantees but assume pre-selected measurement locations, while continuous trajectory optimization supports path-based sensing but is computationally intensive and sensitive to initialization in obstacle-dense settings. We propose a hierarchical framework with three stages: (i) graph-based global planning, (ii) segment-wise budget allocation using geometric and kernel bounds, and (iii) spline-based refinement of each segment with hard constraints and obstacle pruning. By combining global guidance with local refinement, our method achieves lower posterior uncertainty than graph-only and continuous baselines, while running faster than continuous-space solvers (up to 9x faster than gradient-based methods and 20x faster than black-box optimizers) across synthetic cluttered environments and Arctic datasets.
