MGAPSO Algorithm Based Inverse Kinematics Solution for 2F2R Macro-Micro Robotic Arm

MGAPSO Algorithm Based Inverse Kinematics Solution for 2F2R Macro-Micro Robotic Arm | 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 MGAPSO Algorithm Based Inverse Kinematics Solution for 2F2R Macro-Micro Robotic Arm Zhiguo Tang, Longqi Li, Zhiwei Ji, Dejun Wang, Jianliang Xu, Yan Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5401517/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The accuracy and precision of the inverse kinematics solution is the key to determine the performance of the robotic arm motion. Aiming at the Particle Swarm Optimization(PSO) algorithm’s low accuracy in solving inverse kinematics of 2-link flexible macro robotic arms with 2-link rigid micro robotic arms(2F2R macro-micro robotic arm), this paper proposes the Multiple Genetic Particle Swarm Optimization algorithm (MGAPSO), which employs a hybrid inertia weight updating strategy and integrates the multiple parallel swarm search mode and the genetic mutation operation to increase the diversity of the populations and the search accuracy. In order to verify the effectiveness of MGAPSO algorithm , two sets of experiments are conducted with six test functions and 2F2R macro-micro robotic arm, respectively. Simulation results show the optimal solution accuracy of improved MGAPSO is 1.19 10-14 , which is better than other algorithms of 3.45 10-7. This paper provides a new algorithm for the inverse kinematics problem. Inverse kinematics Macro-micro robotic arm PSO Hybrid algorithm Optimization problem Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5401517","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375120701,"identity":"c3a0b2cc-fb24-4570-b3cd-b260a11d2581","order_by":0,"name":"Zhiguo Tang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Zhiguo","middleName":"","lastName":"Tang","suffix":""},{"id":375120702,"identity":"979bde31-2684-49e0-8360-33d52078494f","order_by":1,"name":"Longqi Li","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Longqi","middleName":"","lastName":"Li","suffix":""},{"id":375120703,"identity":"7f94cfdb-299b-48fb-b34b-afc84acd70fb","order_by":2,"name":"Zhiwei Ji","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Ji","suffix":""},{"id":375120704,"identity":"23f16c8d-beef-4ac1-92a1-e8528359e221","order_by":3,"name":"Dejun Wang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Dejun","middleName":"","lastName":"Wang","suffix":""},{"id":375120705,"identity":"8877c0c1-694e-4cbe-90e3-3d27c8dcaf5f","order_by":4,"name":"Jianliang Xu","email":"","orcid":"","institution":"Quzhou College of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianliang","middleName":"","lastName":"Xu","suffix":""},{"id":375120706,"identity":"51fdaa43-a18b-46c3-8b08-dfd37a677a72","order_by":5,"name":"Yan Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFADZgbGBwkVNaRpYTZ4cOYYafawST5sYSasTLf9jOHnAoZt8ubtzM8qEhvYGPjbuxPwajE7k2MsPYPhtuGcw2xmNxJ3yDBInDm7Ab+WAzkG0jwMtxlnMDMAtZxhYzCQyCWg5fwb499ALfYzmNm/FSS2MROh5UaOGciWxBnMPGYMRGp5VmYN1JIM1FIskXDmGA9hv5xP3nwbqMV2Bv/xjR9/VNTI8bf34tfCwMBhwMD4D8HlIaAcBNgfEKFoFIyCUTAKRjQAAMR0RKNdU7ZLAAAAAElFTkSuQmCC","orcid":"","institution":"Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-11-06 09:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5401517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5401517/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79411635,"identity":"5c28a06f-0168-46da-9e0e-644f20f785b8","added_by":"auto","created_at":"2025-03-28 06:09:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2606085,"visible":true,"origin":"","legend":"","description":"","filename":"MGAPSOAlgorithmBasedInverseKinematicsSolutionfor2F2RMacroMicroRoboticArm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5401517/v1_covered_ec7d6874-f985-4874-a203-d1968fdba871.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MGAPSO Algorithm Based Inverse Kinematics Solution for 2F2R Macro-Micro Robotic Arm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Inverse kinematics, Macro-micro robotic arm, PSO, Hybrid algorithm, Optimization problem","lastPublishedDoi":"10.21203/rs.3.rs-5401517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5401517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The accuracy and precision of the inverse kinematics solution is the key to determine the performance of the robotic arm motion. Aiming at the Particle Swarm Optimization(PSO) algorithm’s low accuracy in solving inverse kinematics of 2-link flexible macro robotic arms with 2-link rigid micro robotic arms(2F2R macro-micro robotic arm), this paper proposes the Multiple Genetic Particle Swarm Optimization algorithm (MGAPSO), which employs a hybrid inertia weight updating strategy and integrates the multiple parallel swarm search mode and the genetic mutation operation to increase the diversity of the populations and the search accuracy. In order to verify the effectiveness of MGAPSO algorithm , two sets of experiments are conducted with six test functions and 2F2R macro-micro robotic arm, respectively. Simulation results show the optimal solution accuracy of improved MGAPSO is 1.19*10-14 , which is better than other algorithms of 3.45*10-7. 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