Integrated Robotic System for Autonomous Inspection of Power Transmission Lines Using Multimodal Perception and Reconfigurable Topologies

Integrated Robotic System for Autonomous Inspection of Power Transmission Lines Using Multimodal Perception and Reconfigurable Topologies | 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 Integrated Robotic System for Autonomous Inspection of Power Transmission Lines Using Multimodal Perception and Reconfigurable Topologies José Mário Nishihara, Alexandre Domingues, Davi Riiti Goto do Valle, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8226089/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 This paper presents an integrated robotic system for autonomous power transmission line inspection, evaluating three mechanical topologies—Line Walker, ModuClimber, and FlexRover—and a multimodal perception suite combining depth sensors. The comparative analysis reveals a distinct trade-off between mechanical simplicity and obstacle negotiation capabilities, with complex articulated designs proving essential for traversing obstacles. Crucially, the study uncovers significant challenges in algorithm portability; machine learning models trained on a specific robot topology suffered severe performance degradation when transferred to others due to geometric sensor variations. However, results demonstrate that fusing geometric depth features with depth statistics allows lightweight classifiers to recover up to 100% accuracy across different platforms. The findings establish that while deep learning models like SqueezeNet offer inherent robustness, feature-based sensor fusion is the key enabler for developing portable autonomous inspection systems. Categories: (3), (4), (8) Autonomous Inspection Machine Learning Sensor Fusion Robotic Topologies Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 08 Dec, 2025 Submission checks completed at journal 08 Dec, 2025 First submitted to journal 27 Nov, 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-8226089","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590835050,"identity":"a45cfd0a-dd0c-4186-9779-897a677c51f9","order_by":0,"name":"José Mário 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