A Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym Environment

A Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym 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 A Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym Environment Nessrine Khlif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4822355/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 Purpose: The field of autonomous mobile robots (AMRs) has experienced significant growth in recent years, propelled by advancements in autonomous driving and unmanned aerial vehicles (UAVs). The integration of intelligence into robotic systems necessitates addressing various research challenges, with naviga- tion emerging as a pivotal aspect of mobile robotics. This paper explores the three fundamental questions central to the navigation problem: localization (determin- ing the robot’s position), mapping (creating a representation of the environment), and path planning (determining the optimal route to the destination). The pro- posed solution to the mobile robot navigation problem involves the seamless integration of these three foundational navigation components. Methods: Our comparative analysis between the Q-learning modified method and a deep Q-network (DQN) in simulated gym pathfinding tasks reveals the efficacy of this approach. The modified Q-learning algorithm consistently outperforms DQN, demonstrating its superior ability to navigate complex environments and achieve optimal solutions. The transition from a definite environment to a simulated gym environment serves as a valuable validation of the method’s applicability in real-world scenarios. By rigorously evaluating our algorithm in a controlled setting, we can ensure its robustness and effectiveness across a broader range of applications. Results: In essence, our study establishes the modified Q-learning algorithm as a promising new approach to addressing the exploration-exploitation dilemma in reinforcement learning. Its superior performance in simulated gym environments suggests its potential for real-world applications in various domains, including robotics, autonomous navigation, and game development. Conclusion: The paper furnishes a comprehensive overview of research on autonomous mobile robot navigation. It begins with a succinct introduction to the diverse facets of navigation, followed by an examination of the roles of machine learning and reinforcement learning in the realm of mobile robotics. Subsequently, the paper delves into various path planning techniques. In the end, this paper presents a comparative analysis of two path planning methods for mobile robots: Q-learning with an enhanced exploration strategy and Deep Q-Network (DQN). Through a comprehensive simulation study in a gym environment, the superior performance of the proposed Q-learning approach is firmly established. Artificial Intelligence and Machine Learning Robotics Mobile Robotic Path planing Reinforcement Learning Q-Learning Deep-Q Network (DQN) Gym environment Full Text Additional Declarations The authors declare no competing interests. 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. 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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-4822355","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333223466,"identity":"86941066-1c38-40af-ba17-129751cff81f","order_by":0,"name":"Nessrine Khlif","email":"data:image/png;base64,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","orcid":"","institution":"Laboratory of Robotics, Informatics and Complex Systems (RISC lab - LR16ES07), National engineering school of Tunis, Electrical engineering department, University of Tunis EL Manar, Tunis, Tunisia","correspondingAuthor":true,"prefix":"","firstName":"Nessrine","middleName":"","lastName":"Khlif","suffix":""}],"badges":[],"createdAt":"2024-07-29 13:26:53","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4822355/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4822355/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61421162,"identity":"8bd360bd-7cdd-41e1-bf6b-dfd97f742cc9","added_by":"auto","created_at":"2024-07-30 13:55:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":442198,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleTitle3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4822355/v1_covered_e0abc6c9-ae58-46fe-bf15-007a8d7b2634.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym Environment\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Engineering School of Tunis","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":"Mobile Robotic, Path planing, Reinforcement Learning, Q-Learning, Deep-Q Network (DQN), Gym environment","lastPublishedDoi":"10.21203/rs.3.rs-4822355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4822355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eThe field of autonomous mobile robots (AMRs) has experienced significant growth in recent years, propelled by advancements in autonomous driving and unmanned aerial vehicles (UAVs). The integration of intelligence into robotic systems necessitates addressing various research challenges, with naviga- tion emerging as a pivotal aspect of mobile robotics. This paper explores the three fundamental questions central to the navigation problem: localization (determin- ing the robot’s position), mapping (creating a representation of the environment), and path planning (determining the optimal route to the destination). The pro- posed solution to the mobile robot navigation problem involves the seamless integration of these three foundational navigation components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Our comparative analysis between the Q-learning modified method and a deep Q-network (DQN) in simulated gym pathfinding tasks reveals the efficacy of this approach. The modified Q-learning algorithm consistently outperforms DQN, demonstrating its superior ability to navigate complex environments and achieve optimal solutions. The transition from a definite environment to a simulated gym environment serves as a valuable validation of the method’s applicability in real-world scenarios. By rigorously evaluating our algorithm in a controlled setting, we can ensure its robustness and effectiveness across a broader range of applications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In essence, our study establishes the modified Q-learning algorithm as a promising new approach to addressing the exploration-exploitation dilemma in reinforcement learning. Its superior performance in simulated gym environments suggests its potential for real-world applications in various domains, including robotics, autonomous navigation, and game development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The paper furnishes a comprehensive overview of research on autonomous mobile robot navigation. It begins with a succinct introduction to the diverse facets of navigation, followed by an examination of the roles of machine learning and reinforcement learning in the realm of mobile robotics. Subsequently, the paper delves into various path planning techniques. In the end, this paper presents a comparative analysis of two path planning methods for mobile robots: Q-learning with an enhanced exploration strategy and Deep Q-Network (DQN). Through a comprehensive simulation study in a gym environment, the superior performance of the proposed Q-learning approach is firmly established.\u003c/p\u003e","manuscriptTitle":"A Comprehensive Review of Intelligent Navigation of Mobile Robots Using Reinforcement Learning with A Comparative Analysis of a modified Q-Learning Method and DQN in Simulated Gym Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-30 13:47:14","doi":"10.21203/rs.3.rs-4822355/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"953b3378-c0cf-4fa1-bc1e-864ecf781c30","owner":[],"postedDate":"July 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35283638,"name":"Artificial Intelligence and Machine Learning"},{"id":35283639,"name":"Robotics"}],"tags":[],"updatedAt":"2024-07-30T13:47:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-30 13:47:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4822355","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4822355","identity":"rs-4822355","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}