Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel
This work addresses the challenge of efficient and adaptive bathymetric mapping for marine surveying applications, representing an incremental improvement in path planning methods.
The paper tackles the problem of enabling an Autonomous Surface Vessel (ASV) to autonomously map underwater topography (bathymetry) under depth constraints, by developing algorithms for online Gaussian Process modeling and convex polygon partitioning, which were tested in simulation and field experiments.
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.