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This paper describes the design, implementation and testing of a suite ofalgorithms to enable depth constrained autonomous bathymetric (underwatertopography) mapping by an Autonomous Surface Vessel (ASV). Given a target depthand a bounding polygon, the ASV will find and follow the intersection of thebounding polygon and the depth contour as modeled online with a GaussianProcess (GP). This intersection, once mapped, will then be used as a boundarywithin which a path will be planned for coverage to build a map of theBathymetry. Methods for sequential updates to GP's are described allowingonline fitting, prediction and hyper-parameter optimisation on a small embeddedPC. New algorithms are introduced for the partitioning of convex polygons toallow efficient path planning for coverage. These algorithms are tested both insimulation and in the field with a small twin hull differential thrust vesselbuilt for the task.

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