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. / Master of Science / The aim is to understand how bats can dynamically change the shape of their outer ears (pinnae) to optimally detect sounds in different environments and for different tasks. Previous studies tried to mimic bat ear motions using fixed deformation patterns in robotic ear models, but this approach is limited. Instead this thesis uses deep learning neural networks to predict how changing the shape of a robotic bat pinna model affects its acoustic beampattern (how it radiates and receives sound). The pinna geometry is based on a 3D scan of a greater horseshoe bat ear, with three virtual "actuators" to deform the shape. For many different actuator patterns deforming the pinna, the resulting beampattern is calculated using computer simulations. Neural networks ( multilayer perceptron and radial basis function network) are trained on this data to accurately predict the beampattern from the actuator states. The multilayer perceptron network is found to be significantly more computationally efficient for this task. This neural network based approach allows rapidly exploring the vast range of possible pinna actuations to identify optimal shapes for specific biosonar sensing tasks, shedding light on principles of dynamic ear shape control in bats.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/121265 |
| Date | 02 October 2024 |
| Creators | Chakrabarti, Sounak |
| Contributors | Mechanical Engineering, Mueller, Rolf, Rainey, Katie, Losey, Dylan Patrick |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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