Design and Development of Autonomous Mobile Robot for Material Handling Application

In the evolving domain of logistics and supply chain management, efficiency, accuracy, and scalability have become essential to meet growing demands. This project introduces a cutting-edge autonomous mobile robot designed for material handling and inventory management in warehouses. Utilizing advanced technologies, the robot integrates Radio Frequency Identification (RFID) for real-time product identification and tracking, RPLIDAR for navigation and mapping using Simultaneous Localization and Mapping (SLAM), and a Raspberry Pi 4 as the central processing unit. With the Robot Operating System (ROS) serving as the control framework, the solution aims to minimize human intervention and automate critical tasks, thereby addressing key inefficiencies in traditional warehouse operations. The robot leverages its RPLIDAR sensor to generate precise 2D maps of the environment, enabling efficient navigation and dynamic obstacle avoidance. RFID-based tagging of items ensures accurate inventory tracking and retrieval, eliminating manual errors and improving overall reliability. As the robot moves autonomously, it scans the environment, identifies specific items, and updates inventory status in real time. Tasks like stocktaking, replenishment, and material transport are seamlessly executed, facilitated by efficient data processing and decision-making via Raspberry Pi 4. This integration provides continuous, uninterrupted operations with minimal manual oversight. By automating labour-intensive and error-prone tasks, the proposed robot enhances warehouse efficiency while significantly reducing operational costs and the risk of workplace injuries. Its scalable design allows deployment in warehouses of varying sizes and complexities, with potential for collaborative operation by multiple robots. The system's ability to operate continuously and integrate with existing warehouse systems makes it a cost-effective and forward-thinking solution for modern inventory management. This project represents a robust step toward advancing autonomous technologies to transform conventional warehousing practices and meet the growing demand for smarter supply chain solutions.