Comparison of Stochastic Optimization Strategies in Multi-Robot Multi-Target Tracking Scenarios

Comparison of Stochastic Optimization Strategies in Multi-Robot Multi-Target Tracking Scenarios | 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 Comparison of Stochastic Optimization Strategies in Multi-Robot Multi-Target Tracking Scenarios Pujie Xin, Philip Dames This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3973390/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jun, 2025 Read the published version in Swarm Intelligence → Version 1 posted 11 You are reading this latest preprint version Abstract This paper conducts a comparative study of stochastic optimization strategies to enable multi-robot systems to search for and track an unknown number of targets. Each robot is equipped with a noisy sensor that has a limited Field of View (FoV). The robots use a distributed version of the Probability Hypothesis Density (PHD) filter to estimate both the number and states of the targets. This online target estimate is used by the different search strategies to select actions for each robot. We compare Lloyd's algorithm, a traditional method for distributed search, with four stochastic optimization techniques: Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). Each of the methods is adapted from the traditional case of finding a single global optimum to locate all the local maxima (\ie targets). We demonstrate through extensive simulations that these techniques offer superior coverage of the search area and more accurate target localization compared to the baseline Lloyd's algorithm. We also discuss the strengths and limitations of each method, assisting practitioners in the selection of the most appropriate strategy based on specific operational factors, such as the communication load in a multi-robot system. Multi-target tracking Stochastic optimization Distributed control Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Jun, 2025 Read the published version in Swarm Intelligence → Version 1 posted Editorial decision: Revision requested 09 Jun, 2024 Reviews received at journal 08 Jun, 2024 Reviewers agreed at journal 08 Jun, 2024 Reviews received at journal 18 Apr, 2024 Reviews received at journal 28 Mar, 2024 Reviewers agreed at journal 18 Mar, 2024 Reviewers agreed at journal 18 Mar, 2024 Reviewers invited by journal 16 Mar, 2024 Editor assigned by journal 04 Mar, 2024 Submission checks completed at journal 20 Feb, 2024 First submitted to journal 20 Feb, 2024 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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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-3973390","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274002127,"identity":"f5a6fb25-66d5-4801-b65a-a3e6b8b0a514","order_by":0,"name":"Pujie Xin","email":"","orcid":"","institution":"Temple University","correspondingAuthor":false,"prefix":"","firstName":"Pujie","middleName":"","lastName":"Xin","suffix":""},{"id":274002128,"identity":"be20738e-6aa5-4bd0-813a-fb4c34428da2","order_by":1,"name":"Philip Dames","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYFCCBBBhAyIYD4CIBiK1pEmASJK0HCZBizl78tMNH/6cr+MXO3vgwAcGG9kNBwhosex5ZnZzZtttCcnZeQkHZzCkGRPUYnAjwew2b8NtCYPbOQaHeRgOJxKhJf3b7T9/zknYg7T8YfhPjJYcs9sMbAckDKSBWoAhQFiLZc+bspu9bcmSM24D/dJjkGw8k5AWc/b0bTd+/LHj55+de/DBjwo72T6CDkMweVC5xGoZBaNgFIyCUYAFAADQXkzxb0iw5QAAAABJRU5ErkJggg==","orcid":"","institution":"Temple University","correspondingAuthor":true,"prefix":"","firstName":"Philip","middleName":"","lastName":"Dames","suffix":""}],"badges":[],"createdAt":"2024-02-20 17:06:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3973390/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3973390/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11721-025-00249-y","type":"published","date":"2025-06-05T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84242686,"identity":"712bc08e-a607-474d-96d6-d47ef350cdfb","added_by":"auto","created_at":"2025-06-09 16:11:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1026307,"visible":true,"origin":"","legend":"","description":"","filename":"PSOPHD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3973390/v1_covered_a6bc5976-d851-4871-af9f-d590095aa2f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Stochastic Optimization Strategies in Multi-Robot Multi-Target Tracking Scenarios","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"swarm-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"swrm","sideBox":"Learn more about [Swarm Intelligence](http://link.springer.com/journal/11721)","snPcode":"11721","submissionUrl":"https://submission.nature.com/new-submission/11721/3","title":"Swarm Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multi-target tracking, Stochastic optimization, Distributed control","lastPublishedDoi":"10.21203/rs.3.rs-3973390/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3973390/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper conducts a comparative study of stochastic optimization strategies to enable multi-robot systems to search for and track an unknown number of targets. 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