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A Deep-Learning Model for Underwater Position Sensing of a Wake's Source Using Artificial Seal Whiskers

Elshalakani, M., Muthuramalingam, M. & Bruecker, C. ORCID: 0000-0001-5834-3020 (2020). A Deep-Learning Model for Underwater Position Sensing of a Wake's Source Using Artificial Seal Whiskers. Sensors, 20(12), article number 3522. doi: 10.3390/s20123522


Various marine animals possess the ability to track their preys and navigate dark aquatic environments using hydrodynamic sensing of the surrounding flow. In the present study, a deep-learning model is applied to a biomimetic sensor for underwater position detection of a wake-generating body. The sensor is composed of a bundle of spatially-distributed optical fibers that act as artificial seal-like whiskers and interact with the body's wake in the form of time-variant (bending) deflections. Supervised learning is employed to relate the vibrations of the artificial whiskers to the position of an upstream cylinder. The labeled training data are prepared based on the processing and reduction of the recorded bending responses of the artificial whiskers while the cylinder is placed at various locations. An iterative training algorithm is performed on two neural-network models while using the 10-fold cross-validation technique. The models are able to predict the coordinates of the cylinder in the two-dimensional (2D) space with a high degree of accuracy. The current implementation of the sensor can passively sense the wake generated by the cylinder at Re ≃ 6000 and estimate its position with an average error smaller than the characteristic diameter D of the cylinder and for inter-distances (in the water tunnel) up to 25-times D.

Publication Type: Article
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: biomimetics; underwater target tracking; deep learning; smart sensors; underwater robotics
Subjects: Q Science > QL Zoology
T Technology > TJ Mechanical engineering and machinery
Departments: School of Science & Technology > Engineering
SWORD Depositor:
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