Design and Control of a Sensorized End-Effector for Curvature-Based Haptic Recognition

Design and Control of a Sensorized End-Effector for Curvature-Based Haptic Recognition | 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 Research Article Design and Control of a Sensorized End-Effector for Curvature-Based Haptic Recognition Laia Vidal, Tomás van den Heijkant, Òscar de Jesús, Pau Nonell, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8037253/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper presents the design and control of a sensor-integrated three-finger end-effector for real-time curvature-based haptic recognition in robotic systems. The device, mounted on a commercial UR3 robotic arm, employs embedded optical sensors to detect contact timing during object exploration. An onboard microcontroller estimates local surface curvature from the time-differential response across the fingers, enabling closed-loop control with deterministic behavior and sub-10 ms latency at a 100 Hz sampling rate. A probability density–based classifier, trained on simulated data and validated on hardware, achieves 95% recognition accuracy on curvature-defined stimuli, with optical sensing proving more reliable and repeatable than mechanical alternatives. A virtual environment was implemented to replicate sensor dynamics and robot kinematics, supporting scalable data generation and seamless virtual-to-real transfer. The lightweight and reproducible framework demonstrates how time-differential haptic signals can be integrated into automation-oriented control systems, offering robust, low-latency surface perception for industrial tasks such as object classification, material sorting, and automated inspection. haptic sensing curvature-based recognition optical sensors sensorized robotic end-effector embedded control system robotic automation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8037253","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562060517,"identity":"e2a941f5-15d4-4b6b-9460-425e4923a0c1","order_by":0,"name":"Laia Vidal","email":"","orcid":"","institution":"Ramon Llull University","correspondingAuthor":false,"prefix":"","firstName":"Laia","middleName":"","lastName":"Vidal","suffix":""},{"id":562060518,"identity":"baf6d63a-6d62-44a5-820c-4c203f3fc804","order_by":1,"name":"Tomás van den Heijkant","email":"","orcid":"","institution":"Ramon Llull 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