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

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.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field Robotic

Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficulty

Assessing pattern printability in new large layouts faces important challenges of runtime and false detection. Lithographic simulation tools and classification techniques do not scale well. We propose a fast pattern detection method by learning an overcomplete basis representing each reference pattern. A pattern from a new design is detected “novel” if its reconstruction error, when coded in the learned basis, is large. We show high speedup (1000x) compared to nearest neighbor search. A new boundary detection technique selects the minimal set of the novel patterns to predict problematic patterns; 14.93% of the novel patterns suffice to predict ORC hotspots, while 53.77% are needed using traditional methods