SHARP: A Hybrid Metaheuristc Approach forIntelligent Robotic Path Planning

SHARP: A Hybrid Metaheuristc Approach forIntelligent Robotic 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 Article SHARP: A Hybrid Metaheuristc Approach forIntelligent Robotic Path Planning Hussam Fakhouri, Sadi Alawadi, Alexander Galozy, Fahed Alkhabbas, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8515574/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 Robot path planning in crowded environments is a challenging optimizationproblem that requires short and collision-free trajectories under limited compu-tational budgets. Several robot path planning approaches have been proposed,however the majority of them either (1) depend on manually chosen weightsto combine objectives, so their results can be biased and unstable, or (2) canget stuck early in crowded environments, leading to longer paths. To addressthese challenges, we introduce multi-objective planning variants that exploit thePSN hybrid metaheuristic. The novelty of the proposed approach lies in thisdual multi-objective layer: it enables both preference-aware and preference-freeplanning using the same underlying PSN search mechanism, without requiringPareto-archive management or redesigning the optimizer. To improve executabil-ity, the final waypoint sequence is smoothed using cubic spline interpolation. Theproposed approach is evaluated on dynamic and static 2D workspaces with vary-ing obstacle densities and compared against PSO, Grey Wolf Optimizer, and the Sine Cosine Algorithm. Across six scenarios, the PSN-based variants consistentlyreturn the shortest feasible paths, reducing path length by 8–46% relative tothe best competing method and by about 47% on average relative to standardPSO. A dynamic simulation with moving obstacles and a moving target furtherillustrates the approach’s adaptability for online re-planning. Physical sciences/Engineering Physical sciences/Mathematics and computing Swarm intelligence artificial intelligence optimization robot path planning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Editor invited by journal 23 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 22 Jan, 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. 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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-8515574","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":598638971,"identity":"96e6ac3c-54aa-40c8-b61b-d36577796c42","order_by":0,"name":"Hussam Fakhouri","email":"","orcid":"","institution":"Al-Balqa Applied University","correspondingAuthor":false,"prefix":"","firstName":"Hussam","middleName":"","lastName":"Fakhouri","suffix":""},{"id":598638977,"identity":"06ac99a4-5ea7-4d07-b5d3-d030032e02a9","order_by":1,"name":"Sadi Alawadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYHACgwMMDBYQxscGhgRitUiAGQdnNhgQp4UBpoWZlxgt8u2HNx5gqJGQNzh+eONh2x1/8hj4Dx/Ab8WZtIIDDMckDDcAGYdzzxgUM0ik4bfJgCEH6Bc2CcZtB3IMDue2GSQ2SPAY4HdY/xugln8S9tvOvzE4bAnSwn/+A37P3ADawtgmkbgNyDjMCNLCkINfh8GNZwUHEvskkvcDGQd7zxgntkmkEXJY8uYPH77Z2M4EMX7ukEvs5z/8AL81IJCAzGEjrH4UjIJRMApGASEAAI+DT4100wPlAAAAAElFTkSuQmCC","orcid":"","institution":"Blekinge Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Sadi","middleName":"","lastName":"Alawadi","suffix":""},{"id":598638980,"identity":"9685664b-da31-4644-8392-b6a9b26e3ca4","order_by":2,"name":"Alexander Galozy","email":"","orcid":"","institution":"Halmstad University","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Galozy","suffix":""},{"id":598638982,"identity":"733d2f8e-a2c7-4640-8d42-2f8defb89fae","order_by":3,"name":"Fahed Alkhabbas","email":"","orcid":"","institution":"Malmö University","correspondingAuthor":false,"prefix":"","firstName":"Fahed","middleName":"","lastName":"Alkhabbas","suffix":""},{"id":598638987,"identity":"d3044e09-59d3-4286-99af-e782887462fb","order_by":4,"name":"Majed Ayyad","email":"","orcid":"","institution":"Birzeit University","correspondingAuthor":false,"prefix":"","firstName":"Majed","middleName":"","lastName":"Ayyad","suffix":""}],"badges":[],"createdAt":"2026-01-04 22:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8515574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8515574/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103720056,"identity":"9d05022a-b5df-40fe-9c0b-16a4f8cf91fd","added_by":"auto","created_at":"2026-03-02 06:59:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1248689,"visible":true,"origin":"","legend":"","description":"","filename":"revisedversion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8515574/v1_covered_8a447c4a-310b-40e7-acdb-8221d7ccaaee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SHARP: A Hybrid Metaheuristc Approach forIntelligent Robotic Path Planning","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":"Swarm intelligence, artificial intelligence, optimization, robot path planning","lastPublishedDoi":"10.21203/rs.3.rs-8515574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8515574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Robot path planning in crowded environments is a challenging optimizationproblem that requires short and collision-free trajectories under limited compu-tational budgets. Several robot path planning approaches have been proposed,however the majority of them either (1) depend on manually chosen weightsto combine objectives, so their results can be biased and unstable, or (2) canget stuck early in crowded environments, leading to longer paths. To addressthese challenges, we introduce multi-objective planning variants that exploit thePSN hybrid metaheuristic. The novelty of the proposed approach lies in thisdual multi-objective layer: it enables both preference-aware and preference-freeplanning using the same underlying PSN search mechanism, without requiringPareto-archive management or redesigning the optimizer. To improve executabil-ity, the final waypoint sequence is smoothed using cubic spline interpolation. Theproposed approach is evaluated on dynamic and static 2D workspaces with vary-ing obstacle densities and compared against PSO, Grey Wolf Optimizer, and the Sine Cosine Algorithm. Across six scenarios, the PSN-based variants consistentlyreturn the shortest feasible paths, reducing path length by 8–46% relative tothe best competing method and by about 47% on average relative to standardPSO. 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