Glider Path-Planning for Optimal Sampling of Mesoscale Eddies

Ocean gliders constitute an important advance in the highly demanding ocean monitoring scenario. Their efficiency, endurance and increasing robustness make these vehicles an ideal observing platform for many long term oceanographic applications [1]. However, they have proved to be also useful in the opportunistic short term characterization of dynamic structures. Among these, mesoscale eddies are of particular interest due to the relevance they have in many oceanographic processes. Path planning plays a main role in glider navigation [2] as a consequence of the special motion characteristics these vehicles present. Indeed, ocean current velocities are comparable to or even exceed a glider’s low speed, typically around 1 km/h (0.28 m/s). In such situations a feasible path must be prescribed to make the glider reach the desired destination. This can be accomplished by analyzing the evolution of the ocean currents predicted by a numerical model. The problem is not trivial, as the planner must take into account a 4D, spatiotemporallyvarying field over which to optimize. Different solutions to the glider path planning problem can be found in the literature. Inanc et al. [3] propose a method that applies Nonlinear Trajectory Generation (NTG) on a Lagrangian Coherent Structures (LCS) model to generate near-optimal routes for gliders on dynamic environments. Alvarez et al. [4] use Genetic Algorithms to produce suitable paths in presence of strong currents while trying to minimize energy consumption. Other authors have put the focus on the coordination of glider fleets to define optimal sampling strategies [5]. In the particular case of eddies, the complexity of the path planning scenario is aggravated by the high spatio-temporal variability of these structures and their specific sampling requirements [6]. Garau et al. [7] use an A* search algorithm to find optimal paths over a set of eddies with variable scale and dynamics. Smith et al. [8] propose an iterative optimization method based on the Regional Ocean Modeling System (ROMS) predictions to generate optimal tracking and sampling trajectories for evolving ocean processes. Their scheme includes near real-time data assimilation and has been tested both in simulation and real field experiments.