Path Planning of Sweeping Robot Based on Improved Particle Swarm Optimization

Path Planning of Sweeping Robot Based on Improved Particle Swarm 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 Path Planning of Sweeping Robot Based on Improved Particle Swarm Optimization Jianxia Zhang, Jinming Kou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7901235/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 In order to solve the problems of low efficiency, long searching time and high path cost of the domestic sweeping robots, a method of improved particle swarm optimization (IPSO) algorithm with symmetrically linearly decreasing inertia weight was used to reasonably plan the path. The symmetric model analyzed the inertia weight of the particle swarm optimization algorithm, the appropriate decreasing rate of inertia weight was found by this paper, and reduced the inertia weight of particle swarm optimization algorithm at a symmetric decreasing rate, so that particle swarm optimization had different optimization ability in different stages, and made its search path effect compare with the search path effect of basic particle swarm optimization algorithm. The experimental results showed that the particle algorithm with symmetrically linearly decreasing inertia weight had the advantages of high accuracy, good stability and not easy to fall into local optimization. The algorithm can effectively carry out global path planning for household sweeping robots, thus providing a theoretical and practical basis for the path planning of household sweeping robots. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics Sweeping robots Path planning Symmetric decline rate Particle swarm optimization Optimization ability 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-7901235","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":541957432,"identity":"7f9b4401-dd56-4c2c-ba96-9a5242924737","order_by":0,"name":"Jianxia 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