AI-Assisted Swarm Robotics for Autonomous Exploration Using Ant Colony Algorithms

AI-Assisted Swarm Robotics for Autonomous Exploration Using Ant Colony Algorithms | 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 AI-Assisted Swarm Robotics for Autonomous Exploration Using Ant Colony Algorithms B Sivakumar Reddy, S K Harisha, Jinka Ranganayakulu, M Krishna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7789592/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract This study explores the integration of Artificial Intelligence (AI) techniques with Ant Colony Optimization (ACO) to enhance swarm robotic exploration in dynamic and uncertain environments. While traditional ACO provides a decentralized, pheromone-based mechanism for path planning, it suffers from slow convergence, stagnation, and limited adaptability when confronted with environmental changes. To address these challenges, an AI-assisted ACO framework was developed, incorporating RL principles to adapt pheromone update rules in real time. Robots learn from past interactions, adjust strategies dynamically, and maintain efficient exploration under varying conditions. Python-based simulations were conducted across four environments—50×50, 100×100, 250×250, and 500×500 grids—with swarm sizes ranging from 20 to 200 robots and obstacle densities of 10%, 20%, and 30%. Results show that AI-assisted ACO consistently outperformed traditional ACO, achieving 7–15% higher exploration coverage (≈ 52.1% vs. 44.6% in the baseline case and ≈ 61% vs. 48% in the largest grid), with ≈ 20–25% faster convergence and ≈ 25% higher adaptability under dynamic obstacle scenarios. Swarm Robotics Ant Colony Optimization (ACO) Reinforcement Learning (RL) Autonomous Exploration Multi-Robot Systems Artificial Intelligence (AI) Integration Full Text Additional Declarations Competing interest reported. We need to verify this results to develop hardware swarm robotics for further research work. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviews received at journal 08 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 15 Oct, 2025 Submission checks completed at journal 15 Oct, 2025 First submitted to journal 06 Oct, 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-7789592","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545572985,"identity":"5a615f18-6511-4d47-9ebe-c9fff0247c93","order_by":0,"name":"B Sivakumar Reddy","email":"","orcid":"","institution":"RV College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"B","middleName":"Sivakumar","lastName":"Reddy","suffix":""},{"id":545572986,"identity":"67db3044-8568-4572-8acd-4d74c648b896","order_by":1,"name":"S K Harisha","email":"","orcid":"","institution":"RV College of 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We need to verify this results to develop hardware swarm robotics for further research work.","formattedTitle":"AI-Assisted Swarm Robotics for Autonomous Exploration Using Ant Colony Algorithms","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":"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 Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Swarm Robotics, Ant Colony Optimization (ACO), Reinforcement Learning (RL), Autonomous Exploration, Multi-Robot Systems, Artificial Intelligence (AI) Integration","lastPublishedDoi":"10.21203/rs.3.rs-7789592/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7789592/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the integration of Artificial Intelligence (AI) techniques with Ant Colony Optimization (ACO) to enhance swarm robotic exploration in dynamic and uncertain environments. While traditional ACO provides a decentralized, pheromone-based mechanism for path planning, it suffers from slow convergence, stagnation, and limited adaptability when confronted with environmental changes. To address these challenges, an AI-assisted ACO framework was developed, incorporating RL principles to adapt pheromone update rules in real time. Robots learn from past interactions, adjust strategies dynamically, and maintain efficient exploration under varying conditions. Python-based simulations were conducted across four environments\u0026mdash;50\u0026times;50, 100\u0026times;100, 250\u0026times;250, and 500\u0026times;500 grids\u0026mdash;with swarm sizes ranging from 20 to 200 robots and obstacle densities of 10%, 20%, and 30%. Results show that AI-assisted ACO consistently outperformed traditional ACO, achieving 7\u0026ndash;15% higher exploration coverage (\u0026asymp;\u0026thinsp;52.1% vs. 44.6% in the baseline case and \u0026asymp;\u0026thinsp;61% vs. 48% in the largest grid), with \u0026asymp;\u0026thinsp;20\u0026ndash;25% faster convergence and \u0026asymp;\u0026thinsp;25% higher adaptability under dynamic obstacle scenarios.\u003c/p\u003e","manuscriptTitle":"AI-Assisted Swarm Robotics for Autonomous Exploration Using Ant Colony Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 12:20:17","doi":"10.21203/rs.3.rs-7789592/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T07:31:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T04:02:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153324547790534927802239133883437948057","date":"2026-05-04T07:03:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220811894471103125217119294836733018475","date":"2026-04-30T03:49:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T15:17:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-15T16:56:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-15T09:47:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Intelligent \u0026 Robotic Systems","date":"2025-10-06T08:38:59+00:00","index":"","fulltext":""}],"status":"published","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 Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc90ea6e-3075-4b99-844c-a11f5e148d30","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T07:31:17+00:00","index":20,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T04:02:08+00:00","index":19,"fulltext":""},{"type":"reviewerAgreed","content":"153324547790534927802239133883437948057","date":"2026-05-04T07:03:16+00:00","index":18,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T12:20:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 12:20:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7789592","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7789592","identity":"rs-7789592","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}