Heterogeneous Multi-Agent Coverage Control through Adaptive Weighting and Corrective Potential Function | 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 Heterogeneous Multi-Agent Coverage Control through Adaptive Weighting and Corrective Potential Function Adha Imam Cahyadi, Prapto Nugroho, Igi Ardiyanto, Hanung Adi Nugroho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7532772/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Journal of Control, Automation and Electrical Systems → Version 1 posted 9 You are reading this latest preprint version Abstract This paper presents a novel adaptive coverage control strategy for multi-agent systems with dynamic weight learning and obstacle avoidance. The approach combines power diagrams with adaptive weight evolution and an improved artificial potential field (APF) for intelligent agent navigation. Each agent dynamically adjusts its power diagram weight based on real-time sensing quality, local environment complexity, and resource availability, while being attracted to the centroid of its adaptively-sized power cell. The system ensures robust coverage and collision avoidance through distributed learning mechanisms that adapt to changing environments and agent capabilities. Theoretical analysis provides formal convergence guarantees for the adaptive weight dynamics, and the improved APF eliminates local minima that plague traditional potential field methods. Comprehensive validation through extensive simulations demonstrates superior performance in challenging, large-scale scenarios with high obstacle density. Coverage control Distributed algorithms Multi-agent systems Power diagrams Potential field methods Adaptive learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Journal of Control, Automation and Electrical Systems → Version 1 posted Editorial decision: Revision requested 16 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviews received at journal 31 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 09 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 04 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. 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