Dr. Mustafa Boyvat works on development of highly functional battery-free wireless robotic devices by combining wireless power transmission techniques with the latest robotic technologies. Previously, he worked on small scale wireless origami robots and ultra-strong wireless soft actuators at Harvard University. He received his doctoral and master's degrees from ETH Zurich, focusing on electromagnetics and micro-optoelectronics, and earned his bachelor's degree from METU (Middle East Technical University). His research interests include electromagnetics, small scale robots, soft robots, MRI-based robotics, and biomedical applications of wireless robotic devices. More information about him can be found on https://mustafaboyvat.wordpress.com.
Electromagnetics robotics small scale robots soft robots wireless power wireless actuation multi-physics simulation metamaterials photonic crystals optics RF radio frequency resonators
Magnetic resonance imaging (MRI) system–driven medical robotics is an emerging field that aims to use clinical MRI systems not only for medical imaging but also for actuation, localization, and control of medical robots. Submillimeter scale resolution of MR images for soft tissues combined with the electromagnetic gradient coil–based magnetic actuation available inside MR scanners can enable theranostic applications of medical robots for precise image‐guided minimally invasive interventions. MRI‐driven robotics typically does not introduce new MRI instrumentation for actuation but instead focuses on converting already available instrumentation for robotic purposes. To use the advantages of this technology, various medical devices such as untethered mobile magnetic robots and tethered active catheters have been designed to be powered magnetically inside MRI systems. Herein, the state‐of‐the‐art progress, challenges, and future directions of MRI‐driven medical robotic systems are reviewed.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems