Research on Indoor Path Planning for Mobile Robots Based on an Improved Ant Colony Algorithm

Research on Indoor Path Planning for Mobile Robots Based on an Improved Ant Colony Algorithm | 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 Research on Indoor Path Planning for Mobile Robots Based on an Improved Ant Colony Algorithm Tianchi ZHANG, Xiaochang NI, Jialin LI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9319475/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract To address the problems of slow convergence, poor path smoothness of traditional ant colony algorithms, and the lack of global vision of the dynamic window approach for mobile robot path planning in complex indoor environments, a global-local collaborative path planning method is proposed. By integrating an improved ant colony algorithm (IAACO) with an optimized dynamic window approach (DWA), efficient, smooth navigation with dynamic obstacle avoidance capability is achieved. IAACO introduces a dynamic adjustment mechanism for pheromone and heuristic factors to balance global exploration and local exploitation, adopts a bidirectional pheromone update strategy with positive and negative feedback to enhance high-quality path memory, and incorporates an improved A* algorithm for differentiated initial pheromone distribution. For local planning, a segmented weighted heading calculation method is introduced into the DWA evaluation function to reduce yaw and velocity change rate constraints, thereby improving motion smoothness. The proposed IAACO-DWA algorithm achieves real-time dynamic obstacle avoidance under optimal global guidance. Gazebo simulation and real-world experimental results based on a ROS-based four-wheel differential mobile robot demonstrate that, compared with traditional methods, IAACO-DWA reduces path length by 11.85% and planning time by 10.92%, generates smoother trajectories, and significantly improves the success rate of dynamic obstacle avoidance. This study provides a feasible solution for autonomous robot navigation in complex indoor environments. Path planning mobile robot ant colony algorithm A* algorithm dynamic window approach ROS navigation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 04 Apr, 2026 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-9319475","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628081805,"identity":"ef923d3d-03d3-4d9f-93b7-0d3c730d1862","order_by":0,"name":"Tianchi ZHANG","email":"","orcid":"","institution":"Tianjin University of Technology and Education","correspondingAuthor":false,"prefix":"","firstName":"Tianchi","middleName":"","lastName":"ZHANG","suffix":""},{"id":628081806,"identity":"61b38551-9a28-43dc-a45c-a2c6f458ca6d","order_by":1,"name":"Xiaochang NI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACZgaGA0AqAYgZHzDwMEDZRGphNjhAlBYoACljkzgAZ+MB5uw8hgd+VNjl8c9uv1b9QeYwAz97jgHDzx24tVg2syUc7DmTXCxx50zZjQM8hxkke94YMPaewa3F4DDzgQO8bQcSG27kpIG1GNzIMWBmbMOnhbHh4F+glvlALQUgLfaEtTAfOAyyZcON9GMMYFskCGphSzgscyY5ceONHGaJMzzpPBJnnhUc7MWn5fwZ449vKuwS591If/ihssdajr89eeODn3i0IAEeAwbGHkj8HyBKAwMD+wMGhh9Eqh0Fo2AUjIIRBQD3CFk3cJQNTgAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin University of Technology and Education","correspondingAuthor":true,"prefix":"","firstName":"Xiaochang","middleName":"","lastName":"NI","suffix":""},{"id":628081807,"identity":"b0f82231-168d-4bd6-bb9f-7485ed760430","order_by":2,"name":"Jialin LI","email":"","orcid":"","institution":"Tianjin University of Technology and Education","correspondingAuthor":false,"prefix":"","firstName":"Jialin","middleName":"","lastName":"LI","suffix":""}],"badges":[],"createdAt":"2026-04-04 09:54:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9319475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9319475/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107993166,"identity":"5c6ff8b7-f738-4e52-8127-b96642307af4","added_by":"auto","created_at":"2026-04-28 10:37:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":624059,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonIndoorPathPlanningforMobileRobotsBasedonanImprovedAntColonyAlgorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9319475/v1_covered_b62d2b36-23f9-4376-ab1a-efa3e628c07a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Indoor Path Planning for Mobile Robots Based on an Improved Ant Colony Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-intelligent-robotics-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jira","sideBox":"Learn more about [International Journal of Intelligent Robotics and Applications](https://www.springer.com/journal/41315)","snPcode":"41315","submissionUrl":"https://submission.springernature.com/new-submission/41315/3","title":"International Journal of Intelligent Robotics and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Path planning, mobile robot, ant colony algorithm, A* algorithm, dynamic window approach, ROS navigation","lastPublishedDoi":"10.21203/rs.3.rs-9319475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9319475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the problems of slow convergence, poor path smoothness of traditional ant colony algorithms, and the lack of global vision of the dynamic window approach for mobile robot path planning in complex indoor environments, a global-local collaborative path planning method is proposed. 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