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Computational Modeling of Muscle Dynamics for Biohybrid Robots
This research aims to advance biohybrid robotics by integrating living biological components with artificial materials. The focus is on developing computational models for artificial muscle cells, a critical element in creating biohybrid robots. Challenges include modeling the complex and nonlinear nature of biological muscles, considering factors like elasticity and muscle fatigue, as well as accounting for fluid-structure interaction in the artificial muscle's environment. The research combines first principle soft body simulation methods and machine learning to improve understanding and control of biohybrid systems.
Keywords: Biohybrid Robotics, Computational Models, Soft Body Simulation, Finite Element Method (FEM), Muscle Dynamics, Soft Robotics
The objective in biohybrid robotics is to combine living biological components with artificial materials to
create adaptable and functional systems capable of performing various tasks. As a part of our research
group, you will be working on the development of computational models for muscle dynamics, a crucial
element in the creation of biohybrid robots. Modeling the intricate structure and behavior of biological
muscles poses a challenge due to their complex and nonlinear characteristics. Simulation models must
capture the biomechanical and physiological aspects of muscle function, including factors like elasticity
or muscle fatigue. Furthermore, artificial muscles exist within a fluidic environment, introducing the
relevance of fluid-structure interaction (FSI). This also highlights the importance of the dynamic interplay
between the deformable structures of the muscles and the surrounding fluid, further complicating the
modeling and control aspects in biohybrid robotic systems. Our research involves leveraging both first
principle soft body simulation methods and machine learning techniques to enhance our understanding
and control of biohybrid systems.
The objective in biohybrid robotics is to combine living biological components with artificial materials to create adaptable and functional systems capable of performing various tasks. As a part of our research group, you will be working on the development of computational models for muscle dynamics, a crucial element in the creation of biohybrid robots. Modeling the intricate structure and behavior of biological muscles poses a challenge due to their complex and nonlinear characteristics. Simulation models must capture the biomechanical and physiological aspects of muscle function, including factors like elasticity or muscle fatigue. Furthermore, artificial muscles exist within a fluidic environment, introducing the relevance of fluid-structure interaction (FSI). This also highlights the importance of the dynamic interplay between the deformable structures of the muscles and the surrounding fluid, further complicating the modeling and control aspects in biohybrid robotic systems. Our research involves leveraging both first principle soft body simulation methods and machine learning techniques to enhance our understanding and control of biohybrid systems.
1. **Literature Review and State-of-the-Art Analysis**: Gain comprehensive knowledge of current methods in muscle modeling and soft body simulation in biohybrid robotics.
2. **Development of First-Principle Models**: Develop mathematical models for simulating muscle cells based on physical and biological principles.
3. **Simulation Framework Integration**: Incorporate the developed muscle models into a broader soft body simulation framework.
1. **Literature Review and State-of-the-Art Analysis**: Gain comprehensive knowledge of current methods in muscle modeling and soft body simulation in biohybrid robotics. 2. **Development of First-Principle Models**: Develop mathematical models for simulating muscle cells based on physical and biological principles. 3. **Simulation Framework Integration**: Incorporate the developed muscle models into a broader soft body simulation framework.
- Strong academic background with exceptional grades in physics, computer science, mechanical
engineering, biomedical engineering, or related fields.
- Strong programming skills (C++ and Python).
- Proficiency in FEM-based simulations.
- Prior knowledge in muscle biology is a plus.
- Enthusiasm for interdisciplinary research and a keen interest in soft robotics.
- Capable of both working independently and cooperating with mentors and teammates.
- Strong academic background with exceptional grades in physics, computer science, mechanical engineering, biomedical engineering, or related fields. - Strong programming skills (C++ and Python). - Proficiency in FEM-based simulations. - Prior knowledge in muscle biology is a plus. - Enthusiasm for interdisciplinary research and a keen interest in soft robotics. - Capable of both working independently and cooperating with mentors and teammates.
Manuel Mekkattu, manuel.mekkattu@srl.ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich
Prof. Robert Katzschmann, rkk@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich
Earliest start: February 1, 2025
If you are interested, please submit a motivation letter, your CV, and transcripts.
Manuel Mekkattu, manuel.mekkattu@srl.ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich
Prof. Robert Katzschmann, rkk@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich
Earliest start: February 1, 2025
If you are interested, please submit a motivation letter, your CV, and transcripts.
MuscleModeling.pdf
