GA-Optimized Consensus Control and Formation Transition for Multi-UAV Systems

GA-Optimized Consensus Control and Formation Transition for Multi-UAV Systems | 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 GA-Optimized Consensus Control and Formation Transition for Multi-UAV Systems Dian Rong, Pengfei Zhang, Yawen Li, Zhongliu Wang, Yvhan Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8940350/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Multi-Unmanned Aerial vehicle (Multi-UAV) formations require flexible formation transition capabilities to adapt to dynamic environments and evolving mission requirements during complex operations. This paper proposes a formation structure model based on a virtual leader and consensus control, achieving target tracking, obstacle avoidance, and inter-UAV coordination through a multi-level behavioral control framework. To address the limitations of traditional formation control methods-such as empirical parameter dependency and low convergence efficiency-a multi-objective fitness function is constructed. This function simultaneously optimizes consensus convergence performance, multi-behavior coordination, and formation transition efficiency, utilizing a Genetic Algorithm (GA) for global optimization. Furthermore, an adaptive transition mechanism between V-formation and linear formation is designed, enabling the formation to dynamically reconfigure based on the distribution of environmental obstacles. Simulation results demonstrate that in sparse obstacle environments, the GA-optimized algorithm improves consensus convergence efficiency by 30.8% and reduces transition time by 22.7%. In continuously constrained environments, consensus convergence efficiency is enhanced by 25.6%, with transition time shortened by 29.6%. The results confirm that the proposed method improves both convergence speed and formation transition efficiency for multi-UAV formations in complex environments, validating the superiority of the joint optimization strategy. Multi-UAV Consensus Control Genetic Algorithm Formation Transition Behavioral Control Parameter Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 26 Feb, 2026 Submission checks completed at journal 26 Feb, 2026 First submitted to journal 22 Feb, 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-8940350","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603406173,"identity":"2ffef397-f94a-47ce-82c1-df89f0c1214f","order_by":0,"name":"Dian Rong","email":"","orcid":"","institution":"North University of China","correspondingAuthor":false,"prefix":"","firstName":"Dian","middleName":"","lastName":"Rong","suffix":""},{"id":603406174,"identity":"65f30167-51c8-4ca6-8cfd-9473638e57b9","order_by":1,"name":"Pengfei 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