The use of autonomous marine vehicles (AMV) have seen a significant growth in the last few decades. This growth has been driven not only by advances in vehicle technology, but also by a need from the scientific and industrial communities for increased autonomy in marine environments. The use of AMVs for scientific activities in aquatic environments has increased data availability, reliability and consistency while their use in commercial activities has made marine based systems safer and more reliable. However, these platforms are generally small and resource constrained. While this helps with maneuverability and allows for unobtrusive monitoring of marine phenomena, it also means that the AMV missions have limited durations. To increase the utility of these vehicles and further increase their adoption in marine applications, their mission durations have to be made longer. In our work, efficient navigation is explored as a means of improving mission durations.
The high inertia environment that AMVs operate in presents a unique opportunity for vehicles to exploit the surrounding flows for efficient navigation. To this end, we consider the problem of optimal path planning in time-varying flow fields for resource constrained marine vehicles. We develop a graph based framework to obtain solutions to this problem where we employ a novel adaptive discretization strategy for graph construction, that leads increased accuracies without compromising computation times. The developed framework encompasses the inherently time varying nature of the problem and also considers uncertainties in flow velocity forecasts available for planning.