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Preprints

Guzman, Naughton, Majumdar, Damon, and Kersh. Assessment of mechanically induced changes in helical fiber microstructure using diffusion tensor imaging. engrXiv:3120

Naughton, Cahoon, Sutton and Georgiadis. Accelerated, physics-inspired inference of skeletal muscle microstructure from diffusion-weighted MRI . arXiv:2306.11125

Tekinapl, Naughton, Kim, Halder, Gillette, Mehta, Kier and Gazzola. Topology, dynamics, and control of an octopus-analog muscular hydrostat. arXiv:2304.08413

2023

Shih, Naughton, Halder, Chang, Kim, Gillette, Mehta and Gazzola. Hierarchical control and learning of a foraging CyberOctopus. Advanced Intelligent Systems, 2023; 0(00):2300088. _Cover_

Chang, Halder, Shih, Naughton, Gazzola and Mehta. Energy Shaping Control of a Muscular Octopus Arm Moving in Three Dimensions. Proceedings of the Royal Society A, 2023; 479:20220593. _Cover_

2021

Naughton, Sun, Tekinalp, Parthasarathy, Chowdhary and Gazzola. Elastica: A compliant mechanics environment for soft robotic control. IEEE Robotics and Automation Letters, 2021; 6(2):3389-3396.

Zhang, Naughton, Parthasarathy and Gazzola. Friction modulation in limbless, three-dimensional gaits and heterogeneous terrains. Nature Communications, 2021; 12:6076.

Chang, Halder, Gribkova, Tekinalp, Naughton, Gazzola and Mehta. Controlling a CyberOctopus Soft Arm with Muscle-like Actuation. 60th IEEE Conference on Decision and Control (CDC), 2021; p.1383-1390.

Sullivan, Wu, Gallo, Naughton, Georgiadis and Pelegri. Sensitivity analysis of effective transverse viscoelastic and diffusional properties of tissue with myelinated axons. Physics in Medicine and Biology, 2021; 66(3):035027.

2020

Naughton, Tennyson and Georgiadis. Lattice Boltzmann method for simulation of diffusion magnetic resonance imaging physics in multiphase tissue models. Physical Review E, 2020; 102(4):043305.

Naughton and Georgiadis. Global sensitivity analysis of skeletal muscle dMRI metrics: Effects of microstructural and pulse parameters. Magnetic Resonance in Medicine; 2020; 83:1458–1470.

2019

Naughton and Georgiadis. Comparison of two-compartment exchange and continuum models of dMRI in skeletal muscle. Physics in Medicine and Biology, 2019; 64(15):155004.

Naughton and Georgiadis. Connecting Diffusion MRI to Skeletal Muscle Microstructure: Leveraging Meta-Models and GPU-acceleration. Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC ‘19). p7, (July 2019), Chicago, IL, USA.

2014

Naughton, Plourde, Stark, Hodis and Abraham. Impacts of waveforms on the fluid flow, wall shear stress, and flow distribution in cerebral aneurysms and the development of a universal reduced pressure. Journal of Biomedical Science and Engineering. 2014; 7(01):7.

• September 2023 – Bokun joins lab
• August 2023 – Apoorva joins lab
• August 2023 – Biomechancial Systems Lab founded at Virginia Tech

We are interested in understanding the biomechanics of musculoskeletal structures to improve both healthcare and robotics applications. By modeling the muscles, tendons, and ligaments using slender rods, we can efficiently capture their organization and model their mechanics. For healthcare, we are interested in how human muscle changes with age, to better understand the effects of diet and exercise.  For robotics, we explore how unique creatures such as octopuses or snakes control their bodies to identify new design and control principles for robotics.

We are working on computational modeling of diffusion-weighted magnetic resonance imaging (dMRI) in fibrous tissue, with a particular focus on skeletal muscle. We have developed numerical models of how dMRI is influenced by a tissues microstructural environment, characterized the sensitivity of this signal to microstructural changes, and developed data-driven inverse models that predict microstructural parameters from dMRI images.

We are exploring how learning-based, bio-inspired, and neuromorphic control techniques can be combined with the compliant physics of a soft robot to improve control. A soft robot’s infinite degrees of freedom often frustrate traditional control techniques. Use of learning-based techniques such as reinforcement learning allow us to avoid having to explicitly model these compliant dynamics with the RL algorithm instead implicitly learning a control policy. Bio-inspired and neuromorphic control extends this by adopting strategies evolved by biological creates to solve this challenge.