Adaptive path planning for depth-constrained bathymetric mapping with an autonomous surface vessel

摘要

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. Efficient methods are implemented enabling online fitting, prediction and hyperparameter optimization within the GP framework 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.