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Autonomous underwater vehicles (AUVs) are robotic platforms that are commonlyused to map the sea floor, for example for benthic surveys or for naval minecountermeasures (MCM) operations. AUVs create an acoustic image of the surveyarea, such that objects on the seabed can be identified and, in the case ofMCM, mines can be found and disposed of. The common method for creating suchseabed maps is to run a lawnmower survey, which is a standard method incoverage path planning. We are interested in exploring alternate techniques forsurveying areas of interest, in order to reduce mission time or assess feasibleactions, 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 kernelsto assess their modeling performance, which includes modeling discontinuitiesin the data. Our results show that an additive Mat\'ern kernel is most suitablefor modeling seabed complexity data. On top of the GP model, we use adaptationsof two standard path planning methods, A* and RRT*, to find safe paths formarine vessels through the modeled areas. We evaluate the planned paths andalso run a vehicle dynamics simulator to assess potential performance by amarine vessel.

Planning Safe Paths through Hazardous Environments