Research on intelligent pharmacy unmanned delivery vehicle path planning and drug recognition technology based on JPS and RRT algorithms

Research on intelligent pharmacy unmanned delivery vehicle path planning and drug recognition technology based on JPS and RRT 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 Article Research on intelligent pharmacy unmanned delivery vehicle path planning and drug recognition technology based on JPS and RRT algorithms Hengzhe Yue, Kun Cao, Zhaokun Yang, Quntao An, Lizhi Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7604143/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract This paper is set in the context of an intelligent pharmacy working environment, where we optimize the conventional path planning algorithms for existing drug-dispensing robots. By integrating the Jump Point Search algorithm with the Rapidly-exploring Random Tree Star algorithm, we enhance the accuracy of robot navigation and positioning. Furthermore, deep learning techniques are employed to refine the delivery strategy and improve overall efficiency, thereby increasing system robustness while mitigating issues such as local path optimality and latency. To further enhance the precision of the delivery system, we introduce a drug recognition technology based on contour identification, utilizing the Sum of Absolute Differences model-matching algorithm to accurately identify drug types and their placement locations. This ensures high precision and efficiency in drug recognition during the delivery process, ultimately contributing to a more intelligent and automated pharmacy management system. Physical sciences/Engineering Physical sciences/Mathematics and computing Pharmaceutical Dispensing Robot Robustness Pharmaceutical Identification Model Matching Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 31 Oct, 2025 Reviews received at journal 31 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 27 Oct, 2025 Editor invited by journal 17 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 15 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. 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. 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