{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T07:20:35Z","timestamp":1774682435365,"version":"3.50.1"},"reference-count":36,"publisher":"SAGE Publications","issue":"11-12","license":[{"start":{"date-parts":[[2009,7,21]],"date-time":"2009-07-21T00:00:00Z","timestamp":1248134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2009,11]]},"abstract":"<jats:p> Microrobots have the potential to dramatically change many aspects of medicine by navigating through bodily fluids to perform targeted diagnosis and therapy. Researchers have proposed numerous micro-robotic swimming methods, with the vast majority utilizing magnetic fields to wirelessly power and control the microrobot. In this paper, we compare three promising methods of microrobot swimming (using magnetic fields to rotate helical propellers that mimic bacterial flagella, using magnetic fields to oscillate a magnetic head with a rigidly attached elastic tail, and pulling directly with magnetic field gradients) considering practical hardware limitations in the generation of magnetic fields. We find that helical propellers and elastic tails have very comparable performance, and they generally become more desirable than gradient pulling as size decreases and as distance from the magnetic-field-generation source increases. We provide a discussion of why helical propellers are likely the best overall choice for in vivo applications. <\/jats:p>","DOI":"10.1177\/0278364909341658","type":"journal-article","created":{"date-parts":[[2009,7,22]],"date-time":"2009-07-22T01:40:26Z","timestamp":1248226826000},"page":"1434-1447","source":"Crossref","is-referenced-by-count":612,"title":["How Should Microrobots Swim?"],"prefix":"10.1177","volume":"28","author":[{"given":"Jake J.","family":"Abbott","sequence":"first","affiliation":[{"name":"Institute of Robotics and Intelligent Systems, ETH Zurich, 8092 Zurich, Switzerland, Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA,"}]},{"given":"Kathrin E.","family":"Peyer","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Intelligent Systems, ETH Zurich, 8092 Zurich, Switzerland"}]},{"given":"Marco Cosentino","family":"Lagomarsino","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Milan, 20133 Milan, Italy"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Intelligent Systems, ETH Zurich, 8092 Zurich, Switzerland"}]},{"given":"Lixin","family":"Dong","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Intelligent Systems, ETH Zurich, 8092 Zurich, Switzerland, Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA"}]},{"given":"Ioannis K.","family":"Kaliakatsos","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Intelligent Systems, ETH Zurich, 8092 Zurich, Switzerland"}]},{"given":"Bradley J.","family":"Nelson","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Intelligent Systems, ETH Zurich, 8092 Zurich, 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