Design and Fabrication of an IoT-Enabled Omnidirectional Automated Guided Vehicle with Dynamic Obstacle Avoidance for Smart Manufacturing

Abstract Automated Guided Vehicles (AGVs) represent a foundational cornerstone of modern industrial automation and Industry 4.0 paradigms, fundamentally transforming internal material handling, raw inventory transport, and intra-facility workflow logistics in contemporary smart warehouses and flexible manufacturing plants. Traditional legacy AGVs have historically relied heavily on rigid, floor-mounted magnetic tape, buried inductive guidance wires, or fixed painted lines, which severely restrict their operational flexibility and adaptability whenever factory layouts, assembly lines, or production cell configurations need to be modified or expanded. This comprehensive research paper presents an in-depth study detailing the systematic design, mechanical fabrication, structural assembly, and integrated electronic control of an innovative, IoT-enabled omnidirectional Automated Guided Vehicle utilizing specialized Mecanum wheels and an Arduino-based microcontroller architecture. Unlike conventional differential-drive mobile platforms that necessitate wide turning arcs, large clearance corridors, and multi-point turning maneuvers, our proposed prototype can translate fluidly and instantly in any planar direction—including pure lateral sideways movement, diagonal translation, and zero-radius rotational spinning—without ever altering the physical orientation or heading of its main chassis body. Equipped with a robust sensory network comprising ultrasonic and infrared proximity sensors, the AGV features a responsive real-time dynamic obstacle avoidance system capable of reacting swiftly to unexpected floor hazards, stray pallet boxes, discarded tooling, and human worker traffic. Furthermore, it integrates a wireless ESP8266 Wi-Fi module for seamless remote monitoring, operational telemetry tracking, battery health diagnosis, and instant emergency override via a centralized Internet of Things cloud dashboard. The experimental results demonstrate exceptional positioning accuracy, vastly improved maneuverability in constricted shop floor spaces, and high cost-effective scalability, making it an ideal capstone project for diploma-level mechanical engineering students bridging traditional machine design with advanced smart automation. Keywords: IoT, Automated Guided Vehicle (AGV), Smart Manufacturing 1. Introduction Modern manufacturing environments face mounting, relentless pressures to achieve high operational agility, reduced lead times, zero-waste material handling, and seamless intra-facility logistics between disparate workstations. Automated Guided Vehicles (AGVs) have emerged as a highly reliable, automated solution for streamlining internal transport, reducing human error, and lowering workplace injury risks associated with manual cart pushing and heavy lifting. However, many legacy AGV systems suffer from severe path inflexibility, high infrastructure modification costs, and vulnerability to system-wide disruptions when production layouts change or temporary shop-floor blockages occur. For diploma mechanical engineering students, designing and building an AGV offers an exceptional, comprehensive multidisciplinary learning experience. It effectively integrates core academic disciplines including machine design, mechanisms, kinematics, structural fabrication, sensor interfacing, and basic automation. This project proposes an advanced yet accessible AGV concept: an omnidirectional mobile robot that breaks completely away from traditional turning radius constraints by utilizing specialized Mecanum wheels. Furthermore, the integration of IoT monitoring introduces students to modern smart manufacturing paradigms, bridging mechanical engineering fundamentals with digital connectivity. By working on such a capstone project, students gain invaluable hands-on experience in solving practical shop-floor problems, managing material bills of rights, and executing collaborative engineering tasks from conceptual sketches to final physical validation. 1.1 The Evolution of Material Handling in Smart Factories The transition from manual material transport to fully automated intelligent conveyance has accelerated dramatically over the past two decades. In traditional workshop settings, workers relied heavily on manual hand trucks, hydraulic pallet jacks, and heavy overhead cranes to move raw materials between machining centers and assembly benches. While these methods offer supreme human flexibility, they are highly prone to human fatigue, inconsistent transit times, and frequent workplace strain injuries. Early industrial automation attempted to solve these bottlenecks through rigid, automated monorails and fixed-path towline carts. However, these systems demanded massive initial capital investments and permanent civil engineering alterations to plant floors. The introduction of magnetic tape and optical line-following AGVs marked a major step forward, offering cleaner automated paths that could be rearranged with moderate effort. Yet, these systems remain fundamentally constrained by fixed guidance tracks. If a temporary obstruction blocks the tape, or if an entire assembly line layout must be reconfigured to accommodate a new product line, the facility must halt operations to lay down new tracks or reprogram fixed optical sensors. Our research addresses this critical limitation by introducing an omnidirectional platform that relies on relative vector control and onboard sensing rather than permanent floor infrastructure, representing the next logical evolutionary step for flexible manufacturing systems. 1.2 Educational Value for Diploma Mechanical Engineering Curricula Diploma-level engineering programs occupy a unique, highly practical niche in technical education. They bridge the gap between theoretical engineering sciences and hands-on fabrication skills. Often, capstone projects in these programs suffer from two extremes: either they are purely theoretical design studies that never result in working hardware, or they are overly simplistic mechanical toys that lack real-world industrial relevance. This project was specifically formulated to strike an optimal balance between academic rigor and practical achievability. By tasking students with the complete design cycle—from structural load calculations and component selection to wiring, firmware integration, and physical testing—the project fosters holistic engineering competence. Students learn how to select appropriate engineering materials, manage dimensional tolerances during fabrication, troubleshoot electrical noise issues between motors and microcontrollers, and evaluate performance using systematic testing protocols. This multifaceted exposure ensures that graduating diploma students possess the precise blend of mechanical dexterity and automation literacy demanded by modern manufacturing employers. 2. Literature Review Previous academic and industrial studies on AGV navigation have primarily focused on traditional line-following algorithms utilizing infrared reflective sensors and magnetic tape guidance strips. While these systems are relatively cost-effective to implement, they lack fundamental adaptability when plant layouts are modified or temporary obstacles block established pathways. Recent technological advancements emphasize highly Autonomous Mobile Robots (AMRs) that rely on complex light detection and ranging mapping, simultaneous localization and mapping, and heavy onboard computing units; however, their extreme computational complexity, expensive sensors, and prohibitive implementation costs typically exceed the scope, budget, and technical timeframe of undergraduate diploma projects. This research successfully bridges the gap between basic educational models and advanced industrial systems by implementing a mechanical omnidirectional chassis powered by Mecanum wheels, combined with decentralized obstacle detection. This configuration offers robust, reliable performance suitable for real-world industrial shop floors without introducing prohibitive complexity or excessive financial burdens for student teams. Reviewing historical milestones in automated material handling reveals a clear industry transition from rigid wire-guided carts of the late twentieth century to flexible optical-guided systems, and finally toward modern autonomous mobile robots. Our project adopts the best mechanical aspects of multi-directional mobility while keeping the electronic architecture approachable and economical for technical institutions. 2.1 Comparative Analysis of Guidance Technologies To contextualize our design choices, it is valuable to examine the operational trade-offs of various historical and contemporary guidance methodologies used in automated material handling equipment: Magnetic Tape and Inductive Wire Systems: These legacy systems operate by embedding magnetic strips or live wire loops beneath the factory floor. Sensor arrays mounted on the underside of the vehicle detect the magnetic field or color contrast to steer the vehicle along the path. While highly reliable in repetitive, high-volume warehousing environments, they offer zero path flexibility without physical floor reconstruction. Natural Feature and Laser Navigation: Advanced AMRs utilize LiDAR scanners to map the surrounding facility architecture, comparing real-time laser scans against pre-loaded digital maps to determine their exact position. While exceptionally sophisticated and flexible, these systems require high-performance onboard computers, expensive laser hardware, and complex software configuration that place them well beyond the budgetary and technical scope of typical student capstone projects. Decentralized Relative Mobility and Sensor Proximity Guidance: Our proposed approach utilizes an open-loop chassis architecture driven by specialized Mecanum wheels, supplemented by immediate infrared and ultrasonic obstacle detection. This methodology allows the AGV to