Deep Learning-Driven Modeling of Dynamic Acoustic Sensing in Biommetic Soft Robotic Pinnae

Bats possess remarkably sophisticated biosonar systems that seamlessly integrate the physical encoding of information through intricate ear motions with the neural extraction and processsing of sensory information. While previous studies have endeavored to mimic the pinna (outer ear) dynamics of bats using fixed deformation patterns in biomimetic soft-robotic sonar heads, such physical approaches are inherently limited in their ability to comprehensively explore the vast actuation pattern space that may enable bats to adaptively sense across diverse environments and tasks.To overcome these limitations, this thesis presents the development of deep regression neural networks capable of predicting the beampattern (acoustic radiation pattern) of a soft-robotic pinna as function of its actuator states. The pinna model geometry is derived from a tomographic scan of the right ear of the greater horseshoe bat (textit{Rhinolophus ferrumequinum}. Three virtual actuators are incorporated into this model to simulate a range of shape deformations. For each unique actuation pattern producing a distinct pinna shape conformation, the corresponding ultrasonic beampattern is numerically estimated using a frequency-domain boundary element method (BEM) simulation, providing ground truth data. Two neural networks architectures, a multilayer perceptron (MLP) and a radial basis function network (RBFN) based on von Mises functions were evaluated for their ability to accurately reproduce these numerical beampattern estimates as a function of spherical coordinates azimuth and elevation. Both networks demonstrate comparably low errors in replicating the beampattern data. However, the MLP exhibits significantly higher computational efficiency, reducing training time by 7.4 seconds and inference time by 0.7 seconds compared to the RBFN. The superior computational performance of deep neural network models in inferring biomimetic pinna beampatterns from actuator states enables an extensive exploration of the vast actuation pattern space to identify pinna actuation patterns optimally suited for specific biosonar sensing tasks. This simulation-based approach provides a powerful framework for elucidating the functional principles underlying the dynamic shape adaptations observed in bat biosonar systems.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Chakrabarti, Sounak

Chair dc:contributor.committeechair

• Mueller, Rolf

Committee members dc:contributor.committeemember

• Rainey, Katie
• Losey, Dylan Patrick

• biosonar
• deep learning
• digital twin

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41494

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/121265