Autonomous underwater vehicles (AUVs) are robotic platforms that are commonly used to map the sea floor, for example for benthic surveys or for naval mine countermeasures (MCM) operations. AUVs create an acoustic image of the survey area, such that objects on the seabed can be identified and, in the case of MCM, mines can be found and disposed of. The common method for creating such seabed maps is to run a lawnmower survey, which is a standard method in coverage path planning. We are interested in exploring alternate techniques for surveying areas of interest, in order to reduce mission time or assess feasible actions, such as finding a safe path through a hazardous region. In this paper, we use Gaussian Process regression to build models of seabed complexity data, obtained through lawnmower surveys. We evaluate several commonly used kernels to assess their modeling performance, which includes modeling discontinuities in the data. Our results show that an additive Mat 'ern kernel is most suitable for modeling seabed complexity data. On top of the GP model, we use adaptations of two standard path planning methods, A* and RRT*, to find safe paths through the modeled areas. We evaluate the planned paths and also run a vehicle dynamics simulator to assess potential performance by a marine vessel.
GP regression is also known as Kriging in the field of spatial statistics, where it is used primarily as an interpolation technique. Geospatial Kriging assumes there is some spatially related component that represents a trend or a random error term @cite_3 , based on what is informally called Tobler's first law of geography: Everything is related to everything else, but near things are more related than distant things.'' @cite_0 . will explain the basics of GP regression.
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considered a demographic model whose primary objectives are instructional.' The model developed here may be used for forecasting, but was not constructed for this specific purpose, and it is a demographic model since it describes only population growth, with particular emphasis on the geographical distribution of this growth. As a premise, I make the assumption that everything is related to everything else. Superficially considered this would suggest a model of infinite complexity; a corollary inference often made is that social systems are difficult because they contain many variables; numerous people confuse the number of variables with the 1 Introduction2 Coordinate Systems3 Georelational Vector Data Model4 Object-Based Vector Data Model5 Raster Data Model6 Data Input7 Geometric Transformation8 Spatial Data Editing9 Attribute Data Input and Management10 Data Display and Cartography11 Data Exploration12 Vector Data Analysis13 Raster Data Analysis14 Terrain Mapping and Analysis15 Viewsheds and Watersheds16 Spatial Interpolation17 Geocoding and Dynamic Segmentation18 Path Analysis and Network Applications19 GIS Models and Modeling