13 research outputs found
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 ânovelâ 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