Michael Siebold
Papers
12
Total Citations
314
H-Index
9
About
Michael Siebold is a pioneering researcher at the intersection of swarm robotics and surgical automation, whose work spans from multi-robot search algorithms to minimally invasive ear surgery. His early contributions established foundational methods for using Particle Swarm Optimization (PSO) to control robotic swarms, most notably in his highly cited 2007 paper (88 citations) where each robot acts as an individual particle in the optimization algorithm, enabling decentralized search and exploration. This work, along with subsequent studies on scalable swarm dispersion, has influenced how autonomous robot teams coordinate without central control. In a striking pivot to medical robotics, Siebold engineered a miniature robotic endoscope small enough to pass through the Eustachian tube—a device that provides unprecedented visualization of the middle ear (39 citations). He also developed a compact, bone-attached robot for mastoidectomy (36 citations) and demonstrated its safety in cadaveric testing for vestibular schwannoma removal (25 citations). His research on probabilistic error modeling and registration uncertainty has established rigorous safety frameworks for robotic bone milling near vital anatomy, with multiple papers addressing how to incorporate target registration error into surgical planning. Siebold's unique ability to translate swarm intelligence principles into life-saving surgical tools marks him as a transformative figure in both robotics and otologic surgery.
Research Focus
Key Achievements
Top Papers
- 1
- 2Multi-robot search using a physically-embedded Particle Swarm Optimization54 citations · 2008
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- 4A Compact, Bone-Attached Robot for Mastoidectomy36 citations · 2015
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- 8Incorporating target registration error into robotic bone milling12 citations · 2015
- 9Easily scalable algorithms for dispersing autonomous robots10 citations · 2008
- 10