Gradient Eigen-decomposition Invariance Biogeography-based Optimization for Mobile Robot Path Planning

Gradient Eigen-decomposition Invariance Biogeography-based Optimization for Mobile Robot 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 Gradient Eigen-decomposition Invariance Biogeography-based Optimization for Mobile Robot Path Planning Jiaqian Wang, Xiaodong Na, Min Han, Deicai Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-885939/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 The path planning for mobile robots has attracted extensive attention, and evolutionary algorithms have been applied to this problem increas-ingly. In this paper, we propose a novel gradient eigen-decomposition invariance biogeography-based optimization (GEI-BBO) for mobile robot path planning, which has the merits of high rotation invariance and excel-lent search performance. In GEI-BBO, we design an eigen-decomposition mechanism for migration operation, which can reduce the dependence of biogeography-based optimization (BBO) on the coordinate system, improve the rotation invariance and share the information between eigen solutions more effectively. Meanwhile, to find the local opti-mal solution better, gradient descent is added, and the system search strategy can reduce the occurrence of local trapping phenomenon. In addition, combining the GEI-BBO with cubic spline interpola-tion will solve the problem of mobile robot path planning through a defined coding method and fitness function. A series of experiments are implemented on benchmark functions, whose results indicated that the optimization performance of GEI-BBO is superior to other algo-rithms. And the successful application of GEI-BBO for path planning in different environments confirms its effectiveness and practicability. Applied Mathematics Mobile robot path planning Biogeography-based optimization Eigen-decomposition Gradient decent strategy System search strategy Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Nov, 2021 Reviewers invited by journal 24 Sep, 2021 First submitted to journal 07 Sep, 2021 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-885939","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":54251704,"identity":"2b9c4474-9d15-4fcc-a2f4-2a26ba0e7082","order_by":0,"name":"Jiaqian Wang","email":"","orcid":"","institution":"Dalian University of Technology Faculty of Electronic Information and Electrical Engineering","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiaqian","middleName":"","lastName":"Wang","suffix":""},{"id":54251705,"identity":"2ae6216f-d2fb-4d7f-a670-78f81187ca94","order_by":1,"name":"Xiaodong Na","email":"","orcid":"","institution":"Dalian University of Technology Faculty of Electronic Information and Electrical Engineering","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaodong","middleName":"","lastName":"Na","suffix":""},{"id":54251706,"identity":"0f3fe9f7-4d64-4d9a-b522-5ac4bb52456c","order_by":2,"name":"Min Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYDACZgY2IGnD2AfmsRGvJY2xDcgiUgtE2WEStOi28x578HPHedk2ifwDDB/KDjPwz27Ar8XsMF+6Ye+Z28ZtEskMjDPOHWaQuHOAkBYeMwnettuJIC3MvG2HGQwkEghrkfzbdg6i5S+xWqR52w5AtDASrUW2Ldm4jeexwcGec+k8EjcIaTl/xkzybZudbD974sMHP8qs5fhnENCCAg4AMQ8J6kfBKBgFo2AU4AIAKdU8qjRCxN8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2964-4884","institution":"Dalian University of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Han","suffix":""},{"id":54251707,"identity":"2c1ba66b-5899-4f49-8c55-6aef2f58597f","order_by":3,"name":"Deicai Li","email":"","orcid":"","institution":"Shenyang Institute of Automation Chinese Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Deicai","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2021-09-08 12:50:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-885939/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-885939/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14037592,"identity":"f3850388-1cf7-4a85-9133-7b183d4595cf","added_by":"auto","created_at":"2021-09-27 20:39:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":711944,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-885939/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Gradient Eigen-decomposition Invariance Biogeography-based Optimization for Mobile Robot Path Planning","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-885939/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mobile robot path planning, Biogeography-based optimization, Eigen-decomposition, Gradient decent strategy, System search strategy","lastPublishedDoi":"10.21203/rs.3.rs-885939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-885939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The path planning for mobile robots has attracted extensive attention, and evolutionary algorithms have been applied to this problem increas-ingly. In this paper, we propose a novel gradient eigen-decomposition invariance biogeography-based optimization (GEI-BBO) for mobile robot path planning, which has the merits of high rotation invariance and excel-lent search performance. In GEI-BBO, we design an eigen-decomposition mechanism for migration operation, which can reduce the dependence of biogeography-based optimization (BBO) on the coordinate system, improve the rotation invariance and share the information between eigen solutions more effectively. Meanwhile, to find the local opti-mal solution better, gradient descent is added, and the system search strategy can reduce the occurrence of local trapping phenomenon. In addition, combining the GEI-BBO with cubic spline interpola-tion will solve the problem of mobile robot path planning through a defined coding method and fitness function. A series of experiments are implemented on benchmark functions, whose results indicated that the optimization performance of GEI-BBO is superior to other algo-rithms. And the successful application of GEI-BBO for path planning in different environments confirms its effectiveness and practicability.","manuscriptTitle":"Gradient Eigen-decomposition Invariance Biogeography-based Optimization for Mobile Robot Path Planning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-09-27 20:39:12","doi":"10.21203/rs.3.rs-885939/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-11-25T07:29:08+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-09-24T14:29:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Soft Computing","date":"2021-09-07T04:04:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0c491d23-c868-4867-a28a-b9ba0b9dd675","owner":[],"postedDate":"September 27th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":7486225,"name":"Applied Mathematics"}],"tags":[],"updatedAt":"2022-03-03T11:54:39+00:00","versionOfRecord":[],"versionCreatedAt":"2021-09-27 20:39:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-885939","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-885939","identity":"rs-885939","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}