A Multi-Module Perception-Based Robot Dynamic Target Following Method—Collaboration of Improved KCF Tracking, Distance Mapping Function, and Obstacle Avoidance

A Multi-Module Perception-Based Robot Dynamic Target Following Method—Collaboration of Improved KCF Tracking, Distance Mapping Function, and Obstacle Avoidance | 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 A Multi-Module Perception-Based Robot Dynamic Target Following Method—Collaboration of Improved KCF Tracking, Distance Mapping Function, and Obstacle Avoidance Xiaohong Lan, Jian Zhang, Miao Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7581278/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract To address the issues of visual tracking being susceptible to scale variations and occlusion interference, significant monocular ranging errors, and insufficient dynamic obstacle avoidance response in monocular robot dynamic target following, this paper proposes a high-precision dynamic target following method based on multimodal perception. The method enhances tracking robustness in complex scenarios by improving the KCF algorithm with multi-scale feature fusion and occlusion re-detection mechanisms. A distance mapping function integrating geometric distortion correction and pose compensation is designed to significantly improve monocular ranging accuracy. Simultaneously, LiDAR point cloud data is incorporated to construct an obstacle threat model, and an improved trajectory optimization algorithm is employed to achieve collaborative control of following and obstacle avoidance. Experimental results demonstrate that the proposed method maintains a 95.2% following success rate under occlusion, scale variations, and dynamic obstacle scenarios, with a distance estimation error not exceeding 3.5%, showcasing strong practicality and reliability. robot target following multimodal perception KCF algorithm distance estimation dynamic obstacle avoidance path planning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Nov, 2025 Reviews received at journal 05 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 14 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 10 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. 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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-7581278","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515786927,"identity":"d9157ac1-0771-43a0-9de2-4cacd894c0c8","order_by":0,"name":"Xiaohong Lan","email":"","orcid":"","institution":"Chongqing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Lan","suffix":""},{"id":515786928,"identity":"892b7897-b7ef-485c-9f0a-920bff54d802","order_by":1,"name":"Jian 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Avoidance","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-intelligent-and-robotic-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Intelligent \u0026 Robotic Systems](https://link.springer.com/journal/10846)","snPcode":"10846","submissionUrl":"https://submission.springernature.com/new-submission/10846/3","title":"Journal of Intelligent \u0026 Robotic 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