Path Following of Mobile Robots: Teach and Repeat-based and Learning-based Methods

Path Following of Mobile Robots: Teach and Repeat-based and Learning-based Methods | 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 Path Following of Mobile Robots: Teach and Repeat-based and Learning-based Methods Jardel dos Santos Dyonisio, Stephanie Loi Brião, Eduardo Lawson da Silva, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7755838/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Navigation of service robots is a relevant research field in mobile robotics. This field opens up the study of robots following paths of autonomous way. This article analyzes path-following techniques. Firstly, a teach and repeat (T\&R) method is proposed, utilizing the representation of the taught paths via Bézier curves. For each trajectory, three experiments were conducted using a real differential mobile robot. More specifically, the user must generate possible paths by teleoperation for the system to be capable of autonomously repeating the trajectories. Experimental results show that the metric of maximum average percentage error (MPE) for the T\&R method reached 1.361% during the teaching process. In the repetition step, the worst MPE was 9.983%. Furthermore, experiments utilizing the Reinforcement Learning (RL) approach for path following enable the robot to learn navigation behaviors directly from experience, achieving 1.821% of MPE. Thus, dual approaches allow an evaluation of geometric-based and learning-based methods, highlighting their strengths and limitations in terms of accuracy and adaptability. Teach and Repeat Autonomous Mobile Robot Path Following Reinforcement Learning Autonomous Navigation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 02 Oct, 2025 Submission checks completed at journal 02 Oct, 2025 First submitted to journal 30 Sep, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7755838","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558180015,"identity":"c2ff7ec1-d31c-4600-a36d-4b400ca8749b","order_by":0,"name":"Jardel dos Santos Dyonisio","email":"","orcid":"","institution":"Federal University of Rio Grande","correspondingAuthor":false,"prefix":"","firstName":"Jardel","middleName":"dos Santos","lastName":"Dyonisio","suffix":""},{"id":558180016,"identity":"b575bdd2-e49f-4e12-bce6-cb3b7672f40c","order_by":1,"name":"Stephanie Loi 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