Musculoskeletal Simulation
Introduction
This project aims to integrate several neuromuscular modeling approaches into a single simulation platform and develop adaptable models of muscles that capture how muscle grows in response to strength training. The simulation environment will allow integration with machine learning tools and parallel computing. With this system, we seek to design novel adaptable robot geometries for bioprinting. We also plan on developing a GUI for this simulation environment to allow it to be used in outreach and teaching to broaden opportunities for participation in biohybrid robotics.
A Python-based simulation package called PyElastica is used to achieve these goals. The primary feature of PyElastica is to model structures with slender geometry, i.e., rods with lengths significantly bigger than their radii. This feature is leveraged to model muscles, making the package suitable for this project.
My contributions have been to design a streamlined process to set up muscles, rods, data collection, and write custom functions to export and analyze the simulation results by generating appropriate plots and data. I also developed the foundational muscle adaptationn and fatigue logic. Furthermore, I also modeled the Aplysia Californica’s I3 muscle model based on data from Webster et al. 2016.
PyElastica
PyElastica is an open-source Python package designed for Cosserat rod based soft body simulations and is developed by the Gazzola Lab at the University of Illinois.
Contact
If you are interested in learning more about this project, feel free to reach out to our lab: Biohybrid and Organic Robotics Group
Funding Information
This undertaking is funded by NSF CAREER award. More information is available here: CAREER: Adaptive Actuation and Control in Embodied Biohybrid Robots