Efficient Non-Myopic Layered Bayesian Optimization For Large-Scale Bathymetric Informative Path Planning

TL;DR

This paper introduces a scalable, non-myopic Bayesian optimization method for autonomous underwater vehicle bathymetric mapping, enabling efficient large-scale area coverage with real-time planning and improved accuracy.

Contribution

A novel two-layered Bayesian optimization framework for non-myopic, real-time bathymetric path planning over large stochastic Gaussian process maps.

Findings

Outperforms traditional lawn-mowing patterns in efficiency.

Achieves better mapping accuracy in hardware-in-the-loop experiments.

Operates effectively on embedded platforms for large-scale areas.

Abstract

Informative path planning (IPP) applied to bathymetric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) maps work well on small scenarios but they are short-sighted and computationally heavy when mapping larger areas, hindering deployment in real applications. To overcome this, we present a 2-layered BO IPP method that performs non-myopic, real-time planning in a tree search fashion over large Stochastic Variational GP maps, while respecting the AUV motion constraints and accounting for localization uncertainty. Our framework outperforms the standard industrial lawn-mowing pattern and a myopic baseline in a set of hardware in the loop (HIL) experiments in an embedded platform over real bathymetry.