Bartolomé Garau

Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial Variability: an A* Approach

Autonomous Underwater Vehicles (AUVs) operate in ocean environments characterized by complex spatial variability which can jeopardize their missions. To avoid this, planning safety routes with minimum energy cost is of primary importance. This work explores the benefits, in terms of energy cost, of path planning in marine environments showing certain spatial variability. Extensive computations have been carried out to calculate, by means of an A* search procedure, optimal paths on ocean environments with different length scale of eddies and different current intensities. To get statistical confidence, different realizations of the eddy field and starting-ending points of the path have been considered for each environment. Unlike previous works, the more realistic and applied case of constant thrust power navigation is considered. Results indicate that substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity of the eddy structures and the vehicle speed are comparable. Conversely, the straight line path between starting and ending points can be considered an optimum path when the current speed does not exceed half of the vehicle velocity. In both situations, benefits of path planning seem dependent of the spatial structure of the eddy field.

Thermal Lag Correction on Slocum CTD Glider Data

AbstractIn this work a new methodology is proposed to correct the thermal lag error in data from unpumped CTD sensors installed on Slocum gliders. The advantage of the new approach is twofold: first, it takes into account the variable speed of the glider; and second, it can be applied to CTD profiles from an autonomous platform either with or without a reference cast. The proposed methodology finds values for four correction parameters that minimize the area between two temperature–salinity curves given by two CTD profiles. A field experiment with a Slocum glider and a standard CTD was conducted to test the method. Thermal lag–induced salinity error of about 0.3 psu was found and successfully corrected.

AUV navigation through turbulent ocean environments supported by onboard H-ADCP

Frequently, autonomous underwater vehicles (AUVs) must operate in turbulent ocean environments with complex spatio-temporal variability. Energetic flows, instabilities and currents can strongly perturb safety conditions and development of AUVs operations. A priori knowledge of this ocean variability would allow to adequately plan AUV missions, minimizing the possible negative effects of the environment on its operation. Unfortunately, present oceanographic technology is only capable to forecast large and slow components of the ocean variability, being fast and small scales of variability still unpredictable. This partial knowledge about future environmental conditions degrades AUV robustness, reducing its autonomy and safety. In this article, we propose a class of real-time algorithm to support AUVs navigation in strong turbulent ocean environments characterized by unpredictable small scale variability. The algorithm infers the two-dimensional structure of the current field in a limited region in front of the AUV, using the one-dimensional information obtained from an horizontal acoustic doppler current profiler (H-ADCP). Then, a planner locally optimizes the performance of the AUV in the inferred field. In this work reducing travelling time was of interest. The proposed procedure has been tested on simulated turbulent environments. Results indicate that, although sub-optimals, solutions provided by the algorithm required significantly less travelling time than straight line paths. An example where the proposed methodology increases AUV safety and autonomy is also provided

Combining networks of drifting profiling floats and gliders for adaptive sampling of the Ocean

Monitoring ocean dynamics is extremely difficult due to its enormous physical dimensions and the wide range of spatio-temporal scales involved in its dynamical behaviour. It has been recently proposed that the most efficient way to monitor the ocean is through networks of small, intelligent and cheap robotic platforms. Drifting profiling floats and gliders were developed in this context. Floats move with the currents meanwhile they periodically sample the water column through controlled immersions. Conversely, gliders are underwater autonomous vehicles with controllable motion at sea. Both platforms are extensively employed in oceanography due to their high autonomy. A network called Argo of around 3000 profiling floats spreads out around the worlds ocean. Glider networks are starting to settle down at smaller scale in different places. The advent of these networks and the still scarce resources for ocean sampling, create a demand for quantitative tools for optimizing their use. In this work, the problem of optimally merging networks of profiling floats and gliders is considered. Specifically, a genetic algorithm is employed to find optimal glider trajectories to get together an unevenly distributed network of floats the best quality of the sampled field. A measure of the quality of the oceanographic field (objective function to minimize) is defined in terms of the mean formal error obtained from an optimum interpolation scheme. Results show that the quality of the sampled field can be greatly improved by merging both networks if the resolution of glider observations is adequately selected. The spatial lag between glider observations is related to the geometry of the network of profiling floats and must be order of the grid spacing obtained from the mean data spacing of the network of floats.