Jabar RRT* Path Planning

Jabar RRT* Path Planning | 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Article Jabar RRT* Path Planning Jabar Yassine, Mao Pengjun, Sun Jianghao, Zhun Miao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9189288/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Aiming at the problems of slow convergence, path redundancy and fixed step size of the traditional rapid expansion random tree (RRT), this paper proposes a Jabar RRT * algorithm, which integrates three strategies: the artificial potential field guided sampling, adaptive expansion step size and greedy path smoothing: the artificial potential field guides the sampling points to the low potential energy region by dynamically adjusting the gravity coefficient and optimizing the repulsion calculation, so as to reduce invalid sampling; The adaptive step size is dynamically adjusted according to the distribution of environmental obstacles, target distance and node density to balance the exploration efficiency and obstacle avoidance safety; Greedy path smoothing eliminates redundant inflection points through piecewise optimization, and adapts to robot motion constraints. The results show that compared with the basic RRT and the standard RRT, the improved algorithm reduces the number of iteration convergence by 57%, the planning time by 36%, the path length by 30%, and the proportion of invalid sampling to 12%. Through the simulation deployment of gazebo, the practicability and superiority of the improved algorithm in the smart factory scenario are verified, which can meet the requirements of efficient and safe path planning of the transport robot in the dynamic environment. Jabar RRT* RRT* AD-RRT* DSSC path planning Artificial potential field Artificial intelligence robot technology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 22 Mar, 2026 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-9189288","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611262271,"identity":"1f931f71-5086-48e8-b481-861ac67103c3","order_by":0,"name":"Jabar Yassine","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYBACxgYeBJOBwUBCDsQ88IB4LQU2xmAtCXjt4UHmfEhLbADR+LQwt589+LmC4bC8fP/htgcfDA6nzw87/BBoi52cbgMOh/XkJUueYThsuOFGYrvhDIPDuRtvpxkAtSQbmx3A5ZccA8kGhsOMGyQY26R5QFpmJ4C0HEjchktL/xvjn0At9vP7D7ZJ/wE6zHB2+gf8WmbkmIFsSWw4kNgmzWCQliAvnUPAlhnv0iwbDNKTgX5pk+wxsDHcIJ1TcCDBALdfDPtzD99sqLC2nd9//JnEjz8S8vKz0zd/+FBhJ4dTSwOINGhGiBiAVRpgVw4C8hCqDkmkAbfqUTAKRsEoGJkAAPI8Zp6ejrl2AAAAAElFTkSuQmCC","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jabar","middleName":"","lastName":"Yassine","suffix":""},{"id":611262272,"identity":"2b0aad4f-e0fc-46e9-88c6-496fd54dba1d","order_by":1,"name":"Mao Pengjun","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mao","middleName":"","lastName":"Pengjun","suffix":""},{"id":611262273,"identity":"9ab00392-d8cf-4045-b535-8d60a8a314b3","order_by":2,"name":"Sun Jianghao","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Jianghao","suffix":""},{"id":611262276,"identity":"2a81ff6e-2ba5-4052-a77b-2ee83cb9a205","order_by":3,"name":"Zhun Miao","email":"","orcid":"","institution":"Henan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhun","middleName":"","lastName":"Miao","suffix":""}],"badges":[],"createdAt":"2026-03-22 05:54:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9189288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9189288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105728868,"identity":"f49ef9fd-243e-406c-8e71-c85b65981fa4","added_by":"auto","created_at":"2026-03-30 11:12:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":881596,"visible":true,"origin":"","legend":"","description":"","filename":"JabarRRTstar.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9189288/v1_covered_50990b0f-ba0a-4a7e-a11f-d143ffbd910c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Jabar RRT* Path Planning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"discover-robotics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Robotics](https://link.springer.com/journal/44430)","snPcode":"44430","submissionUrl":"https://submission.springernature.com/new-submission/44430/3","title":"Discover Robotics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Unsupported Journal","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Jabar RRT*, RRT*, AD-RRT*, DSSC, path planning, Artificial potential field, Artificial intelligence, robot technology","lastPublishedDoi":"10.21203/rs.3.rs-9189288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9189288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming at the problems of slow convergence, path redundancy and fixed step size of the traditional rapid expansion random tree (RRT), this paper proposes a Jabar RRT \u003csup\u003e*\u003c/sup\u003e algorithm, which integrates three strategies: the artificial potential field guided sampling, adaptive expansion step size and greedy path smoothing: the artificial potential field guides the sampling points to the low potential energy region by dynamically adjusting the gravity coefficient and optimizing the repulsion calculation, so as to reduce invalid sampling; The adaptive step size is dynamically adjusted according to the distribution of environmental obstacles, target distance and node density to balance the exploration efficiency and obstacle avoidance safety; Greedy path smoothing eliminates redundant inflection points through piecewise optimization, and adapts to robot motion constraints. The results show that compared with the basic RRT and the standard RRT, the improved algorithm reduces the number of iteration convergence by 57%, the planning time by 36%, the path length by 30%, and the proportion of invalid sampling to 12%. Through the simulation deployment of gazebo, the practicability and superiority of the improved algorithm in the smart factory scenario are verified, which can meet the requirements of efficient and safe path planning of the transport robot in the dynamic environment.\u003c/p\u003e","manuscriptTitle":"Jabar RRT* Path Planning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 03:03:48","doi":"10.21203/rs.3.rs-9189288/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T06:41:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T09:44:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T09:43:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Robotics","date":"2026-03-22T05:43:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-robotics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Robotics](https://link.springer.com/journal/44430)","snPcode":"44430","submissionUrl":"https://submission.springernature.com/new-submission/44430/3","title":"Discover Robotics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Unsupported Journal","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f630172-842a-417a-9dce-66bc74978a52","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T14:38:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 03:03:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9189288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9189288","identity":"rs-9189288","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}