Path planning and real-time control optimization method for unmanned ground robots in complex terrain

Path planning and real-time control optimization method for unmanned ground robots in complex terrain | 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 Path planning and real-time control optimization method for unmanned ground robots in complex terrain Liam Chen, Sofia Martínez, Hiroshi Tanaka This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7993386/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 Unmanned ground robots (UGRs) often work in rough and irregular terrains where traditional path planning and control methods are not reliable. This study develops a combined method using an improved genetic algorithm (IGA) and an enhanced sliding-mode controller (ESMC). The IGA creates smoother and shorter paths by adding terrain slope, curvature, and obstacle risk into the cost function. The ESMC keeps the robot stable under external disturbances with the help of a disturbance observer. Experiments were carried out in a 10 m × 10 m outdoor test field that included uneven surfaces and random obstacles. Compared with the Dijkstra method, the combined system shortened travel time by 18.7%, limited path deviation to ±4.2 cm, and kept attitude error within 1.5°. These results show that linking path planning with real-time control improves tracking accuracy and stability on rough ground. The method can be used for rescue, mapping, and inspection tasks, and future work will test its performance with moving obstacles and soft soil. Theoretical Computer Science Systems Engineering unmanned ground robot path planning genetic algorithm sliding-mode control terrain navigation real-time control obstacle avoidance 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. 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-7993386","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537765115,"identity":"fa6eefdc-1a5d-42a2-938f-be4ad1147cba","order_by":0,"name":"Liam Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYHACZiCW4GFjZmx8kFDxTw4kdOABEVpk+NiZDxt8OHPAGKwlgbAWBhs5frY0yZltBxIbQFx8WszZzwINL7MAOozHQJqH7U76/LDDD4G22MnpNmDXYtmTl5w445wEWIsxD8+z3I230wyAWpKNzQ5g12JwIMf4MG8bREsyjwRz7sbZCSAtBxK34dJy/o3x4b9QLYd5DJjTDWenf8Cv5UaOcTIjWAtbYuOMhMMJ8tI5+G2xnPHG2LAH7BfmwwwfDqQZbpDOKTiQYIDbL+b8OcYSP8rq7OX7D7b/SPxnIy8/O33zhw8VdnI4vQ8m2VACBCFOpBb5BtyqR8EoGAWjYGQCAISyXYEKEERqAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Mechanical and Aerospace Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom","correspondingAuthor":true,"prefix":"","firstName":"Liam","middleName":"","lastName":"Chen","suffix":""},{"id":537765116,"identity":"80469332-6199-40ea-9943-6b327e0d532c","order_by":1,"name":"Sofia Martínez","email":"","orcid":"","institution":"Department of Mechanical and Aerospace Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom","correspondingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"","lastName":"Martínez","suffix":""},{"id":537765117,"identity":"8ced04b6-dd0a-4a1a-8071-4b792104740c","order_by":2,"name":"Hiroshi Tanaka","email":"","orcid":"","institution":"Department of Mechanical and Aerospace Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Tanaka","suffix":""}],"badges":[],"createdAt":"2025-10-31 01:49:11","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7993386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7993386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94987477,"identity":"b7ac77b8-616b-4b07-ab61-ebfb1da83045","added_by":"auto","created_at":"2025-11-03 07:01:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":366838,"visible":true,"origin":"","legend":"","description":"","filename":"10.2303jiqirenPathplanningandrealtimecontroloptimizationmethodforunmannedgroundrobotsincomplexterrain.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7993386/v1_covered_9cb0c11c-a7f9-4ae0-a5fb-772434a399d5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePath planning and real-time control optimization method for unmanned ground robots in complex terrain\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"unmanned ground robot, path planning, genetic algorithm, sliding-mode control, terrain navigation, real-time control, obstacle avoidance","lastPublishedDoi":"10.21203/rs.3.rs-7993386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7993386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnmanned ground robots (UGRs) often work in rough and irregular terrains where traditional path planning and control methods are not reliable. 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