Experiments in Underwater Feature Tracking with Performance Guarantees Using a Small AUV

TL;DR

This paper demonstrates the use of a small autonomous underwater vehicle to map an isobath with real-time planning and performance guarantees, integrating recent advances in environmental mapping and path planning.

Contribution

It implements and tests a receding horizon planning approach with performance guarantees for underwater feature tracking using Gaussian process modeling.

Findings

Successful real-time mapping of an isobath in a practical environment

Integration of recent planning algorithms with performance guarantees

Validation of the approach with experimental results

Abstract

We present the results of experiments performed using a small autonomous underwater vehicle to determine the location of an isobath within a bounded area. The primary contribution of this work is to implement and integrate several recent developments real-time planning for environmental mapping, and to demonstrate their utility in a challenging practical example. We model the bathymetry within the operational area using a Gaussian process and propose a reward function that represents the task of mapping a desired isobath. As is common in applications where plans must be continually updated based on real-time sensor measurements, we adopt a receding horizon framework where the vehicle continually computes near-optimal paths. The sequence of paths does not, in general, inherit the optimality properties of each individual path. Our real-time planning implementation incorporates recent results that lead to performance guarantees for receding-horizon planning.