Multi-UAV collaborative path planning base on CycA-MASAC Reinforcement Learning in GPS-denied Environment

Multi-UAV collaborative path planning base on CycA-MASAC Reinforcement Learning in GPS-denied Environment | 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 Multi-UAV collaborative path planning base on CycA-MASAC Reinforcement Learning in GPS-denied Environment Nan Li, Jiahui JIn, Jialun Xie, Anli Zhang, Meng Xie, Bobo Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8649215/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract This paper addresses the issue of collaborative path planning for UAVs in GPS-denied environments, proposing an improved multi-agent deep reinforcement learning algorithm, Cycloidal Annealing -MASAC (CycA-MASAC). By designing a reward function for UAV collaborative flight and a Cycloidal Annealing learning rate algorithm, incorporating Partially Observable Markov Decision Process (POMDP) theory and UAV dynamics equations, a multi-UAV path planning scenario with obstacle avoidance in airspace was constructed. Performance metrics, including task completion rate, formation retention rate, flight time, flight distance, and energy consumption, were designed to comprehensively assess the algorithm's performance. Comparative tests on reward functions, sensitivity tests on different formation modes, and collaborative strategy tests for UAVs were conducted. Experimental results show that the CycA-MASAC reinforcement learning method outperforms the traditional MASAC algorithm in terms of faster convergence, stronger stability, and a 10.01% increase in task completion rate and a 17.17% improvement in formation retention rate compared to the original algorithm. In addition, flight strategy testing has shown that the CycA-MASAC algorithm proposed in this paper effectively balances flight costs and safety, demonstrating excellent performance in both swarm coordination and flight safety. Multi-agent Deep Reinforcement Learning Path Planning MASAC Algorithm Cycloidal Annealing Algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 27 Jan, 2026 Submission checks completed at journal 22 Jan, 2026 First submitted to journal 20 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-8649215","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608641532,"identity":"5588af02-2feb-4a6c-a712-67c760a822f8","order_by":0,"name":"Nan Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Li","suffix":""},{"id":608641533,"identity":"5978d5af-3226-4072-b0b7-fa7e8262eaf9","order_by":1,"name":"Jiahui 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