COLMAG - A Novel Human-Aware Motion Framework Exploiting Magnetic Sensing in Collaborative Robotics

COLMAG - A Novel Human-Aware Motion Framework Exploiting Magnetic Sensing in Collaborative Robotics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article COLMAG - A Novel Human-Aware Motion Framework Exploiting Magnetic Sensing in Collaborative Robotics Federico Masiero, Giuliano Giampietro, Sandro Ferrari, Valentina Condorelli, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7471918/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The integration of human-friendly robots into dynamic, shared environments has led to advancements in sensing and control strategies to ensure safe Human-Robot Interaction. Existing solutions rely on vision-based systems or capacitive skins for human motion tracking and collision avoidance, but these approaches can face limitations including occlusions, sensitivity to environmental conditions, or restricted detection range. Here, we propose COLMAG, a novel approach that merges COLlaborative robotics with MAGnetic sensing to enhance collaboration in physical Human-Robot Interaction. The system incorporates a magnetic marker worn by the human operator, whose position is tracked by a sensor array mounted on the endeffector of a robotic manipulator. A computationally efficient algorithm decodes the magnetic field data, enabling precise localization and intent recognition. We present a multi-modal control architecture and experimentally validated it in three representative motion generation modalities: collision avoidance, safety, and contactless guidance, using velocity fields in task space. Results highlight the potential of magnetic sensing to guarantee robot responsiveness and adaptability, offering a robust complementary solution to traditional sensing methods and opening new avenues for research in safe, intuitive, and contactless human-robot collaboration. Physical sciences/Engineering/Electrical and electronic engineering Scientific community and society/Business and industry/Technology Full Text Additional Declarations There is NO Competing Interest. Supplementary Files MovieS01.mp4 Presentation of COLMAG and the study MovieS02.mp4 Magnetic Tracking MovieS03.mp4 Static Calibration of the magnetic detector MovieS04.mp4 Dynamic disturbance rejection of the magnetic detector MovieS05.mp4 Demonstration of the collision avoidance motion generator MovieS06.mp4 Demonstration of the guidance motion generator MovieS07.mp4 Finite State Machine logic for the selection of the motion generator MovieS08.mp4 Experiments of the collision avoidance motion generator MovieS09.mp4 Experiments of the safety motion generator MovieS10.mp4 Safety stopping at more challenging speeds MovieS11.mp4 Experiments of the guidance motion generator Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7471918","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508322634,"identity":"c4f36b91-5cc5-474c-ba12-5a4878408f51","order_by":0,"name":"Federico 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