Multi-Step Gaussian Process Propagation for Adaptive Path Planning

2026-04-21

AI Summary

This work addresses the challenge of efficiently integrating multi-source sensory information and managing environmental uncertainty in robotic exploration and monitoring tasks. The authors propose an adaptive path planning approach based on Gaussian processes, which uniquely incorporates a multi-step Gaussian process posterior explicitly into the receding horizon optimization objective. By jointly accounting for state and input constraints alongside multimodal sensor data within the path cost function, the method actively perceives and exploits environmental uncertainty. Evaluated on an algal bloom monitoring task, the proposed approach significantly reduces both misclassification probability and binary error rates compared to existing methods under the same sampling budget, yielding observation trajectories that are substantially more informative.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Efficient and robust path planning hinges on combining all accessible information sources. In particular, the task of path planning for robotic environmental exploration and monitoring depends highly on the current belief of the world. To capture the uncertainty in the belief, we present a Gaussian process based path planning method that adapts to multi-modal environmental sensing data and incorporates state and input constraints. To solve the path planning problem, we optimize over future waypoints in a receding horizon fashion, and our cost is thus a function of the Gaussian process posterior over all these waypoints. We demonstrate this method, dubbed OLAhGP, on an autonomous surface vessel using oceanic algal bloom data from both a high-fidelity model and in-situ sensing data in a monitoring scenario. Our simulated and experimental results demonstrate significant improvement over existing methods. With the same number of samples, our method generates more informative paths and achieves greater accuracy in identifying algal blooms in chlorophyll a rich waters, measured with respect to total misclassification probability and binary misclassification rate over the domain of interest.

Problem

• path planning
• environmental monitoring
• uncertainty quantification
• robotic exploration
• algal bloom detection

Innovation

• Gaussian Process
• Adaptive Path Planning
• Multi-Modal Sensing
• Receding Horizon Optimization
• Uncertainty-Aware Planning