Adaptive sampling using fleets of underwater gliders in the presence of fixed buoys using a constrained clustering algorithm

• Main Authors: Cococcioni, Marco (Author), Lazzerini, Beatrice (Author), Lermusiaux, Pierre (Contributor)
• Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
• Format: Article
• Language: English
• Published: IEEE, 2017-04-13T22:05:54Z.
• Subjects: Article
• Online Access: Get fulltext

 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.