Deep Reinforcement Learning based Path Planning with Dynamic Trust Region Optimization for Automotive Application

Deep Reinforcement Learning based Path Planning with Dynamic Trust Region Optimization for Automotive Application | 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 Deep Reinforcement Learning based Path Planning with Dynamic Trust Region Optimization for Automotive Application Vengatesan Arumugam, Vasudevan Alagumalai, Venkataramanan Srinivasan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4948392/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Graphical abstract Abstract Multi-robot path planning must adapt to difficult situations, allowing autonomous navigation in both static and dynamic barriers in complicated environments. However, defining the best planning strategies for certain applications remains unsolved. This study focused at three methods for learning complex robotic decision-making principles such as Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), and Deep Reinforcement Learning (DRL). Furthermore, proposed a novel technique for obstacle avoidance and autonomous navigation called Dynamic Improvement Trust Region Policy Optimization with Covariance Grid Adaptation (DITRPO-CGA). Initially, created the Dynamic Improvement Proximal Policy Optimization with Covariance Grid Adaptation (DIPPO-CGA) based on PPO to assure collision-free policies. Next, developed a DRL technique that integrates DIPPO-CGA, resulting in the DITRPO-CGA algorithm, which improved the flexibility of multi-robot systems in different situations. During training process, DIPPO-CGA is utilized to optimize the multi-robot multi-task policies, ensuring least distance obstacle avoidance and target completion. The proposed DIPPO-CGA algorithm reaches the target within minimum distance. The findings showed that when compared to PPO, TRPO, and DIPPO-CGA, the proposed DITRPO-CGA algorithm achieves a higher convergence rate, faster target achievement and reaches the positions more quickly. PPO algorithm DITRPO-CGA algorithm actor-critic network multi-robot deep reinforcement learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 23 Aug, 2024 Submission checks completed at journal 21 Aug, 2024 First submitted to journal 20 Aug, 2024 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-4948392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344366345,"identity":"e8b70321-2f82-41c1-9f94-aef9d7342700","order_by":0,"name":"Vengatesan Arumugam","email":"data:image/png;base64,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","orcid":"","institution":"Saveetha Institute of Medical and Technical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Vengatesan","middleName":"","lastName":"Arumugam","suffix":""},{"id":344366346,"identity":"e10f9f73-add4-4733-b45a-a354abbe50e6","order_by":1,"name":"Vasudevan Alagumalai","email":"","orcid":"","institution":"Saveetha Institute of Medical and Technical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vasudevan","middleName":"","lastName":"Alagumalai","suffix":""},{"id":344366347,"identity":"ae49fa1d-5e68-4e35-b18d-5972ef4d9e2e","order_by":2,"name":"Venkataramanan Srinivasan","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Venkataramanan","middleName":"","lastName":"Srinivasan","suffix":""}],"badges":[],"createdAt":"2024-08-21 03:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4948392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4948392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64826559,"identity":"fde403ca-502f-42f9-9385-ec10f202f7a2","added_by":"auto","created_at":"2024-09-19 08:48:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2292197,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptwithauthordetails.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4948392/v1_covered_7472eb2d-f0c7-4e94-879c-612bd25fea35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Reinforcement Learning based Path Planning with Dynamic Trust Region Optimization for Automotive Application","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":"PPO algorithm, DITRPO-CGA algorithm, actor-critic network, multi-robot, deep reinforcement learning","lastPublishedDoi":"10.21203/rs.3.rs-4948392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4948392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Graphical abstract\nAbstract\nMulti-robot path planning must adapt to difficult situations, allowing autonomous navigation in both static and dynamic barriers in complicated environments. However, defining the best planning strategies for certain applications remains unsolved. This study focused at three methods for learning complex robotic decision-making principles such as Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), and Deep Reinforcement Learning (DRL). Furthermore, proposed a novel technique for obstacle avoidance and autonomous navigation called Dynamic Improvement Trust Region Policy Optimization with Covariance Grid Adaptation (DITRPO-CGA). Initially, created the Dynamic Improvement Proximal Policy Optimization with Covariance Grid Adaptation (DIPPO-CGA) based on PPO to assure collision-free policies. Next, developed a DRL technique that integrates DIPPO-CGA, resulting in the DITRPO-CGA algorithm, which improved the flexibility of multi-robot systems in different situations. During training process, DIPPO-CGA is utilized to optimize the multi-robot multi-task policies, ensuring least distance obstacle avoidance and target completion. The proposed DIPPO-CGA algorithm reaches the target within minimum distance. The findings showed that when compared to PPO, TRPO, and DIPPO-CGA, the proposed DITRPO-CGA algorithm achieves a higher convergence rate, faster target achievement and reaches the positions more quickly.","manuscriptTitle":"Deep Reinforcement Learning based Path Planning with Dynamic Trust Region Optimization for Automotive Application","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-19 08:40:45","doi":"10.21203/rs.3.rs-4948392/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-08-23T21:06:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-21T05:56:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Intelligent Robotics and Applications","date":"2024-08-21T03:52:16+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"bfd5ec09-2042-4390-bfab-d7f8cd789f58","owner":[],"postedDate":"September 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-19T08:40:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-19 08:40:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4948392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4948392","identity":"rs-4948392","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}