DAPF-BI-RRT: A Bidirectional Rapidly- Exploring Random Tree Algorithm Based on Hierarchical Potential Fields and Multi- Parameter Dynamic Self-Adaptation

DAPF-BI-RRT: A Bidirectional Rapidly- Exploring Random Tree Algorithm Based on Hierarchical Potential Fields and Multi- Parameter Dynamic Self-Adaptation | 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 Article DAPF-BI-RRT: A Bidirectional Rapidly- Exploring Random Tree Algorithm Based on Hierarchical Potential Fields and Multi- Parameter Dynamic Self-Adaptation Tao Yang, Jianqiu Chen, Jianxin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9300881/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Path planning is a core technology that enables autonomous unmanned systems (AUS) to achieve autonomous movement in complex environments. Aiming at inherent limitations of individual path planning algorithms, this paper integrates the Artificial Potential Field (APF) and Bidirectional Rapidly-Exploring Random Tree (BI-RRT) frameworks, proposing a novel Dynamic Adaptive Hierarchical Potential Field Bidirectional Rapidly-Exploring Random Tree algorithm (DAPF-BI-RRT) based on hierarchical potential fields and multi-parameter dynamic self-adaptation. First, in the random point sampling phase, an adaptive dynamic probability bias strategy optimizes the sampling process, improving sampling efficiency and enabling effective configuration space exploration. Second, in the path expansion phase, a hierarchical potential field strategy balancing strong and weak attractive forces and an environment-adaptive step size mechanism synergistically accelerate convergence; a two-stage weighting strategy for new node generation mitigates local optima and goal unreachability. Finally, simulation experiments are conducted in a 2D environment to compare the proposed DAPF-BI-RRT algorithm with the traditional RRT, BI-RRT, APF-RRT and other benchmark algorithms in obstacle-laden environments with different complexities and narrow passage environments. The experimental results demonstrate that the DAPF-BI-RRT algorithm exhibits superior performance and robust stability, and can effectively address the challenges of efficient path planning for AUS in complex environments. Physical sciences/Engineering Physical sciences/Mathematics and computing path planning AUS RRT algorithm APF algorithm hierarchical potential field strategy dynamic self-adaptive strategy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviews received at journal 02 May, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor invited by journal 10 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 02 Apr, 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. 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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-9300881","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":623640595,"identity":"9455a265-9480-440d-aa69-17c28acc9fa1","order_by":0,"name":"Tao Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBADOQjFRqz6AwwMxqRrSWwgWovB8bOHX3+osUnfcLv3AMOHssMM/LMbCGg5k5dmceBYWu6GO+cSGGecO8wgcecAAS0HcswMDrAdzt1wI8eAmbftMIOBRAIBLeffALX8O5xuANLylygtN3KMHxxsO5wA1sJIjBbJG2/MGM72pRnOBGo52HMunUfiBgEtfOdzjD9UfLOR57uRY/jgR5m1HP8MAloUDjCwScA4B4CYB796IJBvYGD+QFDVKBgFo2AUjGwAAP58SlHfb6pXAAAAAElFTkSuQmCC","orcid":"","institution":"Nanning University","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Yang","suffix":""},{"id":623640596,"identity":"f0b96aab-4a4b-49e8-b18a-d1803c05f3db","order_by":1,"name":"Jianqiu Chen","email":"","orcid":"","institution":"Nanning University","correspondingAuthor":false,"prefix":"","firstName":"Jianqiu","middleName":"","lastName":"Chen","suffix":""},{"id":623640597,"identity":"f248fc96-50ef-4141-8aaa-442c5f04c5b9","order_by":2,"name":"Jianxin Wang","email":"","orcid":"","institution":"Nanning University","correspondingAuthor":false,"prefix":"","firstName":"Jianxin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-04-02 09:11:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9300881/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9300881/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481534,"identity":"601672ec-7a4b-4735-b6a5-c3d2ffef55e7","added_by":"auto","created_at":"2026-04-22 02:18:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1678485,"visible":true,"origin":"","legend":"","description":"","filename":"DAPFBIRRTABidirectionalRapidlyExploringRandomTreeAlgorithmBasedonHierarchicalPotentialFieldsandMultiParameterDynamicSelfAdaptation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9300881/v1_covered_9287fb7e-fae4-47d4-b486-2080a0114667.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DAPF-BI-RRT: A Bidirectional Rapidly- Exploring Random Tree Algorithm Based on Hierarchical Potential Fields and Multi- Parameter Dynamic Self-Adaptation","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"path planning; AUS, RRT algorithm, APF algorithm, hierarchical potential field strategy, dynamic self-adaptive strategy","lastPublishedDoi":"10.21203/rs.3.rs-9300881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9300881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePath planning is a core technology that enables autonomous unmanned systems (AUS) to achieve autonomous movement in complex environments. Aiming at inherent limitations of individual path planning algorithms, this paper integrates the Artificial Potential Field (APF) and Bidirectional Rapidly-Exploring Random Tree (BI-RRT) frameworks, proposing a novel Dynamic Adaptive Hierarchical Potential Field Bidirectional Rapidly-Exploring Random Tree algorithm (DAPF-BI-RRT) based on hierarchical potential fields and multi-parameter dynamic self-adaptation. First, in the random point sampling phase, an adaptive dynamic probability bias strategy optimizes the sampling process, improving sampling efficiency and enabling effective configuration space exploration. Second, in the path expansion phase, a hierarchical potential field strategy balancing strong and weak attractive forces and an environment-adaptive step size mechanism synergistically accelerate convergence; a two-stage weighting strategy for new node generation mitigates local optima and goal unreachability. Finally, simulation experiments are conducted in a 2D environment to compare the proposed DAPF-BI-RRT algorithm with the traditional RRT, BI-RRT, APF-RRT and other benchmark algorithms in obstacle-laden environments with different complexities and narrow passage environments. The experimental results demonstrate that the DAPF-BI-RRT algorithm exhibits superior performance and robust stability, and can effectively address the challenges of efficient path planning for AUS in complex environments.\u003c/p\u003e","manuscriptTitle":"DAPF-BI-RRT: A Bidirectional Rapidly- Exploring Random Tree Algorithm Based on Hierarchical Potential Fields and Multi- Parameter Dynamic Self-Adaptation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-17 13:49:13","doi":"10.21203/rs.3.rs-9300881/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T13:09:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103641013395774483571494488916652362150","date":"2026-05-18T10:19:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216845871230331765224949281740869155671","date":"2026-05-09T13:50:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T12:53:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197225013178930145043329533165915213860","date":"2026-04-24T06:35:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303400710434555456879984748618265568828","date":"2026-04-23T12:15:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81742576779229200863560825768481255503","date":"2026-04-15T12:30:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-10T07:45:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-10T07:21:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-04T01:20:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-04T01:20:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-02T08:50:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"769070a6-854f-4264-92d3-c644edda3961","owner":[],"postedDate":"April 17th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T13:09:10+00:00","index":70,"fulltext":""},{"type":"reviewerAgreed","content":"103641013395774483571494488916652362150","date":"2026-05-18T10:19:46+00:00","index":69,"fulltext":""},{"type":"reviewerAgreed","content":"216845871230331765224949281740869155671","date":"2026-05-09T13:50:59+00:00","index":53,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T12:53:17+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66372090,"name":"Physical sciences/Engineering"},{"id":66372091,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-17T13:49:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-17 13:49:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9300881","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9300881","identity":"rs-9300881","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}