Hierarchical Informative Path Planning via Graph Guidance and Trajectory Optimization

2026-01-23

AI Summary

This work addresses the problem of planning information-rich trajectories under a limited travel budget in cluttered environments to minimize uncertainty about a target location in a Gaussian process field. The authors propose a three-layer hierarchical framework: first, global path planning via graph search; second, segment-wise budget allocation leveraging geometric and kernel-induced bounds; and third, local refinement through spline-based trajectory optimization with hard constraints and obstacle-aware pruning. By integrating the completeness of discrete graph search with the perceptual fidelity of continuous optimization, the method achieves high information gain while significantly improving computational efficiency and robustness. Experiments demonstrate that the approach yields lower posterior uncertainty compared to pure graph-based or continuous optimization baselines, with runtime speedups of up to 9× over gradient-based methods and 20× over black-box optimizers.

Technology Category

• Search and Optimization: Heuristic Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• informative path planning
• Gaussian process
• travel budget
• cluttered environments
• uncertainty reduction

Innovation

• Hierarchical Planning
• Informative Path Planning
• Gaussian Process
• Trajectory Optimization
• Graph Guidance