A Novel Adaptive framework interconnects four pillars for Tethered Robots: Integrating Fuzzy Logic, Genetic Algorithms, and Neural Networks for Robust Dynamic Environment Navigation

A Novel Adaptive framework interconnects four pillars for Tethered Robots: Integrating Fuzzy Logic, Genetic Algorithms, and Neural Networks for Robust Dynamic Environment Navigation | 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 A Novel Adaptive framework interconnects four pillars for Tethered Robots: Integrating Fuzzy Logic, Genetic Algorithms, and Neural Networks for Robust Dynamic Environment Navigation Chandan Sheikder, Weimin Zhang, Xiaopeng Chen, Fangxing Li, Xiaohai He, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6965945/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 Traditional navigation systems for tethered robots often rely on simulated environments, failing to address the unpredictability of real-world dynamic settings—particularly when managing moving obstacles , tether constraints, and sensor noise. To bridge this gap, we propose a novel adaptive hybrid navigation framework that integrates soft computing techniques (fuzzy logic, genetic algorithms, and neural networks) with classical bug algorithms. Our methodology combines sensor fusion to mitigate environmental noise, real-time optimal control for adaptive path planning, and machine learning-driven tether management to dynamically balance efficiency and safety. By enhancing bug algorithms with fuzzy decision-making and genetic optimization, we overcome their inherent limitations in handling non-static obstacles and complex tether configurations. Extensive simulations and real-world trials with a tethered inspection robot demonstrated a 45% reduction in collisions and a 38% improvement in path efficiency compared to conventional methods. Results further show a 28% faster recovery from entanglement scenarios, underscoring the framework’s robustness. This work establishes a foundation for deploying tethered robots in high-stakes applications—from disaster response to industrial inspections—where adaptability and reliability are critical. Physical sciences/Engineering Physical sciences/Mathematics and computing 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. 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-6965945","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475769110,"identity":"10cd6e5e-216a-4616-8e2d-4abce4048b26","order_by":0,"name":"Chandan Sheikder","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chandan","middleName":"","lastName":"Sheikder","suffix":""},{"id":475769111,"identity":"1c04eb78-769b-4e3a-9730-93463ea5a3d3","order_by":1,"name":"Weimin 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