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.
Sea minefields obstructing naval missions have to be breached with minimum eort and acceptable risk. With the advent of autonomous mapping, it is feasible to have high quality information of obstructing sea minefields and their environment before moving into them. The challenge remains to use this knowledge optimally to minimise risk, time and eort in crossing the sea minefield. To achieve this, two interlinked processes are required, consisting of finding the shortest route with acceptable risk or route with minimum risk through the sea minefield, and if no route of acceptable risk exists, removing the optimum combination of sea mines to create a route of acceptable risk. This paper describes the use of Dijkstra’s Algorithm and a genetic algorithm to achieve practical strategies and a method in which two optimisation techniques interact to provide a safe route considering the risk of both the sea mine and the environment and making it applicable to sea mine avoidance. This methodology may readily be applied to general ship routing in risk areas and may be expanded to routing through any area where no known routes exist.