  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 for
marine vessels through the modeled areas. We evaluate the planned paths and
also run a vehicle dynamics simulator to assess potential performance by a
marine vessel.
