Adaptive sampling using fleets of underwater gliders in the presence of fixed buoys using a constrained clustering algorithm
Author(s)Cococcioni, Marco; Lazzerini, Beatrice; Lermusiaux, Pierre
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This paper presents a novel way to approach the problem of how to adaptively sample the ocean using fleets of underwater gliders. The technique is particularly suited for those situations where the covariance of the field to sample is unknown or unreliable but some information on the variance is known. The proposed algorithm, which is a variant of the well-known fuzzy C-means clustering algorithm, is able to exploit the presence of non-maneuverable assets, such as fixed buoys. We modified the fuzzy C-means optimization problem statement by including additional constraints. Then we provided an algorithmic solution to the new, constrained problem.
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
OCEANS 2015 - Genova
Cococcioni, Marco, Beatrice Lazzerini, and Pierre F.J. Lermusiaux. “Adaptive Sampling Using Fleets of Underwater Gliders in the Presence of Fixed Buoys Using a Constrained Clustering Algorithm.” OCEANS 2015 - Genova (May 2015).
Author's final manuscript