Autonomous Mobile Robot for Industrial Material Handling: A ROS2-Based Implementation with Real-Time Navigation

Autonomous Mobile Robot for Industrial Material Handling: A ROS2-Based Implementation with Real-Time Navigation | 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 Autonomous Mobile Robot for Industrial Material Handling: A ROS2-Based Implementation with Real-Time Navigation Shashwat Mishra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7938184/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 industrial settings, there is a growing demand for flexible automation systems that can dynamically navigate and transport payloads. This paper describes a fully autonomous mobile robot (AMR) system which was built following the Robot Operating System 2 (ROS2) Humble framework. The robot has a modular design for mechanical construction and implementation of a control system in software. The hardware implementation is based on an aluminum frame supporting a custom-built electrical distribution network. The power for the robot is provided by a lithium-ion energy storage system operating at 48 volts. The voltage regulation process utilizes switched-mode power supplies with LM2596 step-down converters for output voltage regulation. The motor actuation employs dual-channel Moov servo controllers and uses optical encoders to measure motor position and speed. The computational architecture employs an Intel Next Unit of Computing (NUC) system running SLAM algorithms in a Gazebo Harmonic simulation environment that is visualized using the Navigation2 stack interface in RViz. Additionally, low-level components communicate with an Arduino Mega 2560 microcontroller since it can generate PWM signals, read encoder values, and manage proximity sensor outputs from in-house digital measurement displays. The environmental perception employed for the robot utilizes RP LIDAR A2 M12 scanner sensor to capture distance in a full 360 -degree range; also, the environmental proximity sensors provide additional information regarding obstacles detected in near-range environments. The electrical system features power sequencing circuits with relays and emergency kill circuits and allows monitoring of operational state parameters in real-time on a 7-inch touch screen user interface. The performance experiments provided evidence for robotic navigation capabilities and required obstacle avoidance characteristics. Robotics Autonomous Navigation Systems ROS2 Framework SLAM Algorithms Nav2 Stack Gazebo Simulation Electrical Power Management Intel NUC Platform Full Text Additional Declarations The authors declare no competing interests. Supplementary Files GraphicalAbstract.pdf Graphical Abstract 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-7938184","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534374865,"identity":"452190cf-e2c8-454f-a03b-a21094820c0a","order_by":0,"name":"Shashwat 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This paper describes a fully autonomous mobile robot (AMR) system which was built following the Robot Operating System 2 (ROS2) Humble framework. The robot has a modular design for mechanical construction and implementation of a control system in software. The hardware implementation is based on an aluminum frame supporting a custom-built electrical distribution network. The power for the robot is provided by a lithium-ion energy storage system operating at 48 volts. The voltage regulation process utilizes switched-mode power supplies with LM2596 step-down converters for output voltage regulation.\u003c/p\u003e\u003cp\u003eThe motor actuation employs dual-channel Moov servo controllers and uses optical encoders to measure motor position and speed. The computational architecture employs an Intel Next Unit of Computing (NUC) system running SLAM algorithms in a Gazebo Harmonic simulation environment that is visualized using the Navigation2 stack interface in RViz. Additionally, low-level components communicate with an Arduino Mega 2560 microcontroller since it can generate PWM signals, read encoder values, and manage proximity sensor outputs from in-house digital measurement displays. The environmental perception employed for the robot utilizes RP LIDAR A2 M12 scanner sensor to capture distance in a full 360 -degree range; also, the environmental proximity sensors provide additional information regarding obstacles detected in near-range environments.\u003c/p\u003e\u003cp\u003eThe electrical system features power sequencing circuits with relays and emergency kill circuits and allows monitoring of operational state parameters in real-time on a 7-inch touch screen user interface. 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