Obstacle Detection and Avoidance Methods for Mobile Robot in Indoor Environment

Obstacle Detection and Avoidance Methods for Mobile Robot in Indoor Environment | 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 Obstacle Detection and Avoidance Methods for Mobile Robot in Indoor Environment Yixiao Du, Zhibin Mo, Xiansheng Yang, Wanquan Liu, Muhammand Imran, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4514042/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 In indoor environments, mobile robots are commonly equipped with depth cameras or 2-D laser rangefinders to detect obstacles and navigate through path planning algorithms. However, numerous challenges persist in achieving comprehensive detection and effective avoidance of collisions with obstacles in real indoor environments. The limitations of the 2-D laser rangefinder in accurately representing obstacles at varying heights, coupled with the inherent drawbacks of the rapidly-exploring random tree and the artificial potential field algorithms in obstacle avoidance. Therefore, in this paper, an obstacle detection and avoidance method for mobile robots in indoor environments is proposed, aiming to significantly enhance the efficiency and safety of robot navigation within indoor environments. Firstly, the depth cameras are fused with the 2-D laser rangefinder to expand the field of view of robot obstacle detection. Then, by adding the offset force to solve the problem that the mobile robot is trapped in the local minima caused by the traditional artificial potential field algorithm. On this basis, a series of path optimization methods are proposed to minimize path length while enhancing path smoothness. Experimental results demonstrate that the proposed method enables the mobile robot to detect obstacles comprehensively and acquire a concise and smooth collision-free path. Obstacle detection Obstacle avoidance Mobile robots 2-D laser rangefinder Depth camera Artificial potential field Path plan 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-4514042","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310385301,"identity":"7fa9de84-2fc2-4474-b42b-99214a63d65c","order_by":0,"name":"Yixiao Du","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yixiao","middleName":"","lastName":"Du","suffix":""},{"id":310385302,"identity":"4875e224-86c8-4673-9185-c5da4781ffb0","order_by":1,"name":"Zhibin Mo","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Mo","suffix":""},{"id":310385303,"identity":"6e627207-d00b-493a-a164-305250afe47e","order_by":2,"name":"Xiansheng Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiansheng","middleName":"","lastName":"Yang","suffix":""},{"id":310385304,"identity":"7773085c-56e1-429a-b96f-83dbc3a0dfa1","order_by":3,"name":"Wanquan Liu","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Wanquan","middleName":"","lastName":"Liu","suffix":""},{"id":310385305,"identity":"c32c0ce7-8a32-43df-97ba-81bf765158f2","order_by":4,"name":"Muhammand Imran","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Muhammand","middleName":"","lastName":"Imran","suffix":""},{"id":310385306,"identity":"9f109fef-53ab-44d3-a556-67283b41f4d6","order_by":5,"name":"Hui-Jie Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHACNgaJChsGBmbmBpiIARFazqQBtTCSooWx5TCQJlaL/IzcYw8sG85H87czNjD+bKtLbGBv3ibBUHMHpxaDG3npBpI7bufOOMzYwMzbdjixgedYmQTDsWe4tUjkmElInrmd2wDSwth2ILEBJMLYcBiPw0Ba2s7lzj8Mc5j8G/xaGG6AtRzI3QDUwsDbxgy0hQe/FoMzQDMlziTnbgRqOcxz7rBxG09asUXCMTwOa88xk5aosMudd/7wwYc/yupk+9kPb7zxoQaPw4CAWQLKOMDIBoomIEjAqwEYhx/gzD8ElI6CUTAKRsGIBADP3VVByVSLCQAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Hui-Jie","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-06-01 14:29:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4514042/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4514042/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63706739,"identity":"0036a7f4-262b-43a6-944c-d3dad7e94b9b","added_by":"auto","created_at":"2024-08-31 20:16:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3980741,"visible":true,"origin":"","legend":"","description":"","filename":"ObstacleDetectionandAvoidanceMethodsforMobileRobotinIndoorEnvironment.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4514042/v1_covered_a8ef436c-a936-4c79-bc99-a25f0f627622.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Obstacle Detection and Avoidance Methods for Mobile Robot in Indoor Environment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obstacle detection, Obstacle avoidance, Mobile robots, 2-D laser rangefinder, Depth camera, Artificial potential field, Path plan","lastPublishedDoi":"10.21203/rs.3.rs-4514042/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4514042/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In indoor environments, mobile robots are commonly equipped with depth cameras or 2-D laser rangefinders to detect obstacles and navigate through path planning algorithms. 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