Time-optimal trajectory planning algorithm for robotic arms based on ADFMSSA chaotic optimization

Time-optimal trajectory planning algorithm for robotic arms based on ADFMSSA chaotic optimization | 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 Time-optimal trajectory planning algorithm for robotic arms based on ADFMSSA chaotic optimization Guo Yue, Wang Wan-ting, Li Yu-han, Wang Guo-lan, Li Hai-fang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7691443/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Aiming at the problems that the Sparrow Search algorithm (SSA) is prone to fall into local extreme points in the early stage and has low optimization accuracy in the later stage, an adaptive step-size factor mutation sparrow search algorithm (ADFMSSA) is proposed. Firstly, the population is initialized through the improved Tent mapping chaotic sequence to enhance the randomness and ergoability of the initial population and improve the global search ability of the algorithm; The Caucy variation and Tent chaotic perturbation were introduced again to expand the local search ability, enabling the individuals trapped in the local extreme points to break free from the restrictions and continue the search. The algorithm was further improved by combining the adaptive step factor. Finally, an adaptive adjustment strategy for the number of explorers and followers is proposed. The changes in the number of explorers and followers at each stage are utilized to enhance the global search ability in the early stage and the local deep mining ability in the later stage of the algorithm, and improve the optimization accuracy of the algorithm. Through experiments on 23 benchmark functions, the performance of ADFMSSA was tested and its performance was compared with that of other algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and traditional Sparrow Search Algorithm (SSA). The experiment verified the superiority of ADFMSSA, and further optimized the generation time of the robot trajectory through the improved sparrow search algorithm. The experimental results show that this method can not only accelerate the convergence speed and enhance the global search ability, but also ensure the smoothness of the trajectory. The simulation results show that ADFMSSA outperforms GA, PSO and SSA in terms of accuracy, convergence speed, stability and robustness. Physical sciences/Engineering Physical sciences/Mathematics and computing ADFMSSA Caucy variation Tent chaotic perturbation adaptive step factor an adaptive adjustment strategy 23 benchmark functions Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Feb, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 01 Dec, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers invited by journal 02 Nov, 2025 Editor invited by journal 25 Sep, 2025 Editor assigned by journal 25 Sep, 2025 Submission checks completed at journal 24 Sep, 2025 First submitted to journal 23 Sep, 2025 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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Tent chaotic perturbation, adaptive step factor, an adaptive adjustment strategy, 23 benchmark functions","lastPublishedDoi":"10.21203/rs.3.rs-7691443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7691443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming at the problems that the Sparrow Search algorithm (SSA) is prone to fall into local extreme points in the early stage and has low optimization accuracy in the later stage, an adaptive step-size factor mutation sparrow search algorithm (ADFMSSA) is proposed. Firstly, the population is initialized through the improved Tent mapping chaotic sequence to enhance the randomness and ergoability of the initial population and improve the global search ability of the algorithm; The Caucy variation and Tent chaotic perturbation were introduced again to expand the local search ability, enabling the individuals trapped in the local extreme points to break free from the restrictions and continue the search. The algorithm was further improved by combining the adaptive step factor. Finally, an adaptive adjustment strategy for the number of explorers and followers is proposed. The changes in the number of explorers and followers at each stage are utilized to enhance the global search ability in the early stage and the local deep mining ability in the later stage of the algorithm, and improve the optimization accuracy of the algorithm. Through experiments on 23 benchmark functions, the performance of ADFMSSA was tested and its performance was compared with that of other algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and traditional Sparrow Search Algorithm (SSA). The experiment verified the superiority of ADFMSSA, and further optimized the generation time of the robot trajectory through the improved sparrow search algorithm. The experimental results show that this method can not only accelerate the convergence speed and enhance the global search ability, but also ensure the smoothness of the trajectory. The simulation results show that ADFMSSA outperforms GA, PSO and SSA in terms of accuracy, convergence speed, stability and robustness.\u003c/p\u003e","manuscriptTitle":"Time-optimal trajectory planning algorithm for robotic arms based on ADFMSSA chaotic optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 10:34:42","doi":"10.21203/rs.3.rs-7691443/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-16T09:49:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-29T08:33:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177549108866209629313980781234209940495","date":"2026-01-12T11:28:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T11:40:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319646875941550256207245055271681750163","date":"2025-11-24T14:01:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1958180878920396196633539902704494621","date":"2025-11-02T14:13:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-02T14:11:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-25T16:17:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T07:14:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-24T15:09:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-23T08:02:28+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":"737f08e9-6a28-4e65-8c87-bc045e8b358c","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57487498,"name":"Physical sciences/Engineering"},{"id":57487499,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-04T16:20:26+00:00","versionOfRecord":{"articleIdentity":"rs-7691443","link":"https://doi.org/10.1038/s41598-026-45959-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-27 15:58:21","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2025-11-12 10:34:42","video":"","vorDoi":"10.1038/s41598-026-45959-3","vorDoiUrl":"https://doi.org/10.1038/s41598-026-45959-3","workflowStages":[]},"version":"v1","identity":"rs-7691443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7691443","identity":"rs-7691443","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}