Multi-Step Gaussian Process Propagation for Adaptive Path Planning

TL;DR

This paper introduces OLAhGP, a Gaussian process-based path planning method that adaptively integrates multi-modal environmental data for robotic exploration, demonstrating improved accuracy in oceanic algal bloom monitoring.

Contribution

The paper presents a novel Gaussian process path planning approach that handles multi-modal data and constraints, optimizing over future waypoints in a receding horizon framework.

Findings

OLAhGP outperforms existing methods in identifying algal blooms.

The method achieves higher accuracy with the same number of samples.

Simulated and real-world experiments validate the effectiveness of OLAhGP.

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.