An Investigation of 6-DOF Robot Path Planning Using Evolutionary Algorithms

An Investigation of 6-DOF Robot Path Planning Using Evolutionary Algorithms | 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 An Investigation of 6-DOF Robot Path Planning Using Evolutionary Algorithms Mohamed Gamal Ebrahem AbdelJawad, Abdel-Kader Abdel-Karim Ibrahim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8693518/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 Multi-objective optimization (MOO) plays a vital role in robotics, where several conflicting objectives—such as minimizing energy consumption, execution time, and motion jerk—must be simultaneously satisfied. Traditional single-objective approaches fail to represent the trade-offs among such competing criteria, motivating the adoption of population-based evolutionary and swarm intelligence algorithms. This study presents a novel comparative investigation of two leading multi-objective optimization methods, MOPSO and NSGA-II, for 6-DOF robotic trajectory planning. The study also lays the foundation for a hybrid framework that sequentially combines both algorithms to leverage their complementary strengths. Both algorithms are applied to the multi-objective trajectory optimization of the AR4 robotic manipulator, aiming to achieve optimal motion performance under kinematic and dynamic constraints. The study evaluates each algorithm’s convergence behavior, diversity preservation, and computational efficiency using standard performance metrics—Generational Distance (GD), Spread (SP), and Hypervolume (HV). Results demonstrate that MOPSO exhibits rapid convergence and superior exploration capabilities, while NSGA-II achieves finer convergence accuracy and better Pareto front uniformity. Building on this complementarity, the paper proposes a future hybrid framework combining MOPSO’s exploratory strength with NSGA-II’s refinement and stability. The envisioned MOPSO→NSGA-II hybrid approach seeks to balance exploration and exploitation dynamically, achieving dense, smooth, and well-converged Pareto fronts. The findings provide a foundation for developing robust hybrid metaheuristics in robotic trajectory optimization, enhancing performance and adaptability in complex multi-objective environments. Multi-objective optimization Trajectory optimization 6-DOF manipulator Evolutionary algorithms MOPSO algorithm NSGA-II algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 May, 2026 Reviews received at journal 30 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 31 Jan, 2026 Submission checks completed at journal 31 Jan, 2026 First submitted to journal 25 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. 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-8693518","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619025469,"identity":"3acbc38a-f41e-47d4-96ce-4434d0b9bf45","order_by":0,"name":"Mohamed Gamal Ebrahem AbdelJawad","email":"data:image/png;base64,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","orcid":"","institution":"Benha University","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"Gamal Ebrahem","lastName":"AbdelJawad","suffix":""},{"id":619025471,"identity":"144218b7-0312-4a9c-b248-7a43c86f0dd8","order_by":1,"name":"Abdel-Kader Abdel-Karim Ibrahim","email":"","orcid":"","institution":"Benha University","correspondingAuthor":false,"prefix":"","firstName":"Abdel-Kader","middleName":"Abdel-Karim","lastName":"Ibrahim","suffix":""},{"id":619025473,"identity":"c0dde748-2225-4486-8e72-d798c59dfda2","order_by":2,"name":"Tarek Mohamed Tawfeek","email":"","orcid":"","institution":"Benha University","correspondingAuthor":false,"prefix":"","firstName":"Tarek","middleName":"Mohamed","lastName":"Tawfeek","suffix":""}],"badges":[],"createdAt":"2026-01-25 15:39:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8693518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8693518/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106724906,"identity":"3ae986c2-33fe-4dbd-98cf-111e5b9e9109","added_by":"auto","created_at":"2026-04-12 18:30:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1601355,"visible":true,"origin":"","legend":"","description":"","filename":"gawad.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8693518/v1_covered_ff620d3a-20f2-4f8a-8d38-4355f4f50db4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Investigation of 6-DOF Robot Path Planning Using Evolutionary Algorithms","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":"international-journal-of-intelligent-robotics-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jira","sideBox":"Learn more about [International Journal of Intelligent Robotics and Applications](https://www.springer.com/journal/41315)","snPcode":"41315","submissionUrl":"https://submission.springernature.com/new-submission/41315/3","title":"International Journal of Intelligent Robotics and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multi-objective optimization, Trajectory optimization, 6-DOF manipulator, Evolutionary algorithms, MOPSO algorithm, NSGA-II algorithm","lastPublishedDoi":"10.21203/rs.3.rs-8693518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8693518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMulti-objective optimization (MOO) plays a vital role in robotics, where several conflicting objectives\u0026mdash;such as minimizing energy consumption, execution time, and motion jerk\u0026mdash;must be simultaneously satisfied. Traditional single-objective approaches fail to represent the trade-offs among such competing criteria, motivating the adoption of population-based evolutionary and swarm intelligence algorithms. This study presents a novel comparative investigation of two leading multi-objective optimization methods, MOPSO and NSGA-II, for 6-DOF robotic trajectory planning. The study also lays the foundation for a hybrid framework that sequentially combines both algorithms to leverage their complementary strengths. Both algorithms are applied to the multi-objective trajectory optimization of the AR4 robotic manipulator, aiming to achieve optimal motion performance under kinematic and dynamic constraints. The study evaluates each algorithm\u0026rsquo;s convergence behavior, diversity preservation, and computational efficiency using standard performance metrics\u0026mdash;Generational Distance (GD), Spread (SP), and Hypervolume (HV). 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