Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism

Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism | 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 Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism Jipeng Kang, Zhibin Zhang, Liqiang He, Xu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4254664/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 This paper aims to address the issues that arise when using deep reinforcement learning algorithms for reactive robotic navigation in farmland environments. These challenges include complex network models, a large number of parameters, significant computational requirements, and the possibility of being vulnerable to local optimal solutions. Moreover, the complexity of the farmland environment poses a challenge to the existing indoor reactive navigation methods, which struggle to meet the requirements of tasks such as crop protection and human cooperation.To address these issues, the paper proposes the Field Robot Autonomous Navigation System (FRANS), which utilizes Deep Reinforcement Learning (DRL) to enable farmland robots to autonomously explore unknown environments. The system consists of two modules: Global Navigation (GN) and Local Navigation (LN). The GN module establishes goal points and implements a secure control mechanism to manage the robot's actions. This approach helps to address problems like locally optimal solutions. The LN module uses a lightweight DRL framework to develop a locally navigated motion strategy that guides the robot toward waypoints. FRANS does not require any prior knowledge and is capable of adjusting the navigation strategy in real time based on the current environment. The map is generated in real time as the robot moves through the environment. Experimental results demonstrate that FRANS outperforms similar approaches. Field robotics robot navigation deep reinforcement learning smart agriculture 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. 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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-4254664","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291247876,"identity":"e4fc5981-62a1-42d2-b8d0-01521fb715d8","order_by":0,"name":"Jipeng Kang","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"prefix":"","firstName":"Jipeng","middleName":"","lastName":"Kang","suffix":""},{"id":291247877,"identity":"f9afad53-de85-4099-a6a1-a65eafd66fdb","order_by":1,"name":"Zhibin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYHACNobEBgkGfmYwh5kELZLNJGlhbGBgMDhArBb+/uPPHjzcYZFnfJw7TYKhwjqxgf3sAbxaJG4kpBsknpEoNjvMu02C4Ux6YgNPXgJeLQYSDMckEtskEreBtDC2HQb6i8cAvxb+g21gLZubQVr+EaOFIZkNrGUDM0hLAxFaJG6kAbWckUiccZh3s0XCsXTjNp4c/FpAISb5c0ddYn//2Y03PtRYy/azn8GvBRUkMICiaRSMglEwCkYBxQAABuxAmTfOb0MAAAAASUVORK5CYII=","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":true,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Zhang","suffix":""},{"id":291247878,"identity":"54dbbd94-ada7-4e71-89df-4498f88c1c54","order_by":2,"name":"Liqiang He","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"prefix":"","firstName":"Liqiang","middleName":"","lastName":"He","suffix":""},{"id":291247879,"identity":"bc100c30-ab3f-4162-bbd3-86fc3ed53477","order_by":3,"name":"Xu Chen","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-04-12 00:51:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4254664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4254664/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55263921,"identity":"85d85faf-7289-4c70-b4b0-b003f6d77c37","added_by":"auto","created_at":"2024-04-25 01:31:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2502455,"visible":true,"origin":"","legend":"","description":"","filename":"FieldRobotSelfExplorationBasedonDeepReinforcementLearningandSafetyControlMechanism.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4254664/v1_covered_75e55872-0c1b-4df8-b387-59ab490c59ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism","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":"Field robotics, robot navigation, deep reinforcement learning, smart agriculture","lastPublishedDoi":"10.21203/rs.3.rs-4254664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4254664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper aims to address the issues that arise when using deep reinforcement learning algorithms for reactive robotic navigation in farmland environments. 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