Defense Date
2024
Document Type
Thesis
Degree Name
Master of Science
Department
Mechanical and Nuclear Engineering
First Advisor
Hong Zhao
Abstract
Soft robotics has drawn tremendous interest in recent years because the compliance and motion of soft robotics enable biocompatibility and versatility for many applications, such as human-machine interaction, wearable and assistive devices, and health monitoring. This study introduces a novel predictive modeling approach using neural networks for shape control of magnetic soft robots. The robots are made of silicone materials embedded with hard magnetic particles, which respond to the external magnetic field provided by a ring-type of permanent magnet. These robots, free from physical connections to external devices, i.e., non-tethered actuation, hold significant potential for applications in healthcare, such as artificial limbs and surgical aids. The research methodology combines image processing, inverse kinematics, and neural networks to analyze and predict the movements of the soft robot under magnetic field, aiming to optimize parameters that achieve desired robot shapes. A specialized setup allows for precise manipulation of a permanent magnet's position relative to the robot, facilitating controlled actuation. The implementation of neural networks is pivotal, providing a predictive model that can adaptively calculate the magnet position to achieve the targeted robot morphological changes. This capability is crucial for refining design and control strategies in real-time applications
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-12-2024
Included in
Biomechanical Engineering Commons, Manufacturing Commons, Other Engineering Commons, Robotics Commons