SUSHI: A Vision System for Reactive, Uninformed ASV Navigation via Multi-Field Path Planning and Visual Exploration

SUSHI: A Vision System for Reactive, Uninformed ASV Navigation via Multi-Field Path Planning and Visual Exploration | 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 SUSHI: A Vision System for Reactive, Uninformed ASV Navigation via Multi-Field Path Planning and Visual Exploration Hamze Hammami, Mohamad Abban, Abdul Maajid Aga, Laith Mohamed, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7490170/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Vision offers richer context than traditional marine sensors (e.g., LiDAR, DVL, sonar) but is harder to interpret on water due to reflections, glare, and dynamic surfaces. SUSHI is a vision-first navigation system for Autonomous Surface Vehicles (ASVs) that fuses detection, water segmentation, and monocular depth to produce camera-centric navigation grids for planning and control. The proposed perception methods improve our existing segmentation model to 90% accuracy from 60% on a previously tested method with only 30 minutes of training, implement a dataset that achieved 91% accuracy for trash and obstacle detection in simulation using YOLO, and benchmark a monocular depth method that solves the issue of reflective surfaces and can work universally. Path planning uses a Multi-Field Synthesis (MFS) approach: a locally reactive artificial-potential-field component blended adaptively with a global wavefront flow field, mitigating local minima while preserving real-time responsiveness. A behavior layer prioritizes target seeking and mask-based visual exploration when explicit goals are absent. Validation was performed in the TOAST simulator and in a pool environment, demonstrating robust goal targeting and exploration using cameras with minimal side sensing for emergency avoidance. Autonomous Surface Vehicles Computer Vision Path Planning Marine Robotics Multi-Field Synthesis Visual Navigation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 19 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 29 Aug, 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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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-7490170","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514911085,"identity":"4ee02230-55c2-46a9-8d68-8d89f3420a80","order_by":0,"name":"Hamze 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