Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

TL;DR

This paper presents algorithms enabling an Autonomous Surface Vessel to perform depth-constrained bathymetric mapping by following and mapping specific depth contours, with online Gaussian Process updates and efficient coverage path planning.

Contribution

It introduces new algorithms for online Gaussian Process fitting and polygon partitioning for efficient autonomous bathymetric mapping.

Findings

Algorithms successfully tested in simulation and field trials.

Effective online GP updates on embedded hardware.

Improved coverage path planning within depth contours.

Abstract

This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.