Design and Development of Autonomous Mobile Robot for Material Handling Application

Abstract

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 sizesandcomplexities,withpotentialforcollaborativeoperationbymultiplerobots.Thesystem\'s ability to operate continuously and integrate with existing warehouse systems makes it a cost-effectiveandforward-thinkingsolutionformoderninventorymanagement.Thisproject represents a robust step toward advancing autonomous technologies to transform conventional warehousing practices and meet the growing demand for smarter supply chain solutions.

Introduction

Overview

With the emergence of Industry 4.0, Autonomous Mobile Robots (AMRs) are revolutionizing material handling through smart automation, precision, and flexibility. This project presents the design and development of a cost-effective, modular AMR system using technologies like ROS 2 (Humble), Google Cartographer, RPLiDAR, and Raspberry Pi. It aims to reduce manual labor, increase efficiency, and enable scalable industrial automation.

Key Features & Technologies

• Autonomous Navigation using ROS 2 Humble

• Real-time Mapping with Google Cartographer and RPLiDAR A1M8

• Control Unit: Raspberry Pi (main processor) + Arduino Nano (low-level interfacing)

• Sensor Fusion for improved localization and obstacle detection

• Dynamic Path Planning (no pre-defined tracks like AGVs)

• Material Handling via robotic arms/lift systems

• Wireless Communication with Warehouse Management Systems (WMS)

• Battery Management and automated charging

Objectives

1. Develop autonomous navigation and obstacle avoidance.

2. Enable real-time localization and 2D mapping.

3. Optimize material pickup and delivery workflows.

4. Ensure adaptability to various industrial layouts.

5. Use low-cost hardware for affordability.

6. Prioritize safety and reliability.

7. Offer scalable automation solutions for SMEs.

Comparison with Existing Systems

• Manual Handling: Common in SMEs but labor-intensive.

• Conveyor Systems: Efficient but inflexible due to fixed paths.

• AGVs: Semi-automated, but rely on physical guides like QR codes or magnetic strips.

In contrast, the proposed AMR uses SLAM, real-time decision-making, and AI for adaptability, making it far more suitable for dynamic and evolving environments.

Working Principle

1. SLAM Mapping: RPLiDAR scans the environment; ROS processes data to build/update maps.

2. Sensor Fusion: Combines LiDAR, ultrasonic, and vision systems for accuracy.

3. Path Planning: Real-time route calculation avoiding static and moving obstacles.

4. Motion Control: Precision motor control using encoders and L29 driver.

5. Material Handling: Robotic mechanisms autonomously load/unload items.

6. Safety: Collision avoidance and emergency stop systems.

7. Communication: Wireless updates and task coordination with central systems.

Literature Survey Insights

1. (Shanmugasundaram & Rajalakshmi, 2016)

   • Demonstrated AMRs outperform AGVs in dynamic navigation using SLAM and sensor fusion.

   • AMRs reduced handling time, cost, and human errors in warehouse testing.

2. (Hassan & Vasilenko, 2020)

   • Emphasized IoT integration for AMRs, enabling M2M communication, real-time tracking, predictive maintenance, and scalability.

   • IoT enables cloud connectivity, edge processing, and system-wide optimization.

   • Future trends include 5G, AI-IoT fusion, and swarm robotics.

System Design Elements

• Core Components:

  • RPLiDAR A1M8: 360° environmental scanning.

  • Raspberry Pi: Main processor with ROS 2 support.

  • Arduino Nano: Handles low-level sensors.

  • 12V DC Motor + L29 Driver: Precision motion control.

  • Battery (4400 mAh) with management and charging system.

  • Cooling fan, buck converter, connectors, and wiring.

Applications

• Warehouses, manufacturing, logistics, and healthcare for:

  • Autonomous delivery

  • Inventory management

  • Intralogistics automation

Conclusion

The successful completion of the project titled “Design and Development of Autonomous Mobile Robot for Material Handling Application” has marked a significant milestone in advancing cost-effective and intelligent automation for industrial environments. This project aimed to address the limitations of traditional material handling methods—such as manual labour, conveyor systems, and Automated Guided Vehicles (AGVs)—by introducing a flexible, autonomous, and real-time responsive robotic system. The developed AMR is built on a robust and scalable architecture that integrates key technologies such as Robot Operating System 2 (ROS 2 Humble), Google Cartographer for SLAM-basedmapping,RPLiDARA1M8forenvironmentalperception,andRaspberryPi4B as the core processing unit. These elements collectively enabled the robot to navigate autonomously,detectandavoidobstacles,andcarryoutmaterialhandlingtaskswithminimal human intervention. Component-levelandsystem-leveltestingvalidatedtheperformanceofeachhardware andsoftwaresubsystem.Therobotdemonstratedprecisemapping,smoothpathplanning,and successful navigation in simulated as well as real-world environments. Real-time data from LiDARwasprocessedeffectivelytogenerateaccurate2Dmaps,whiletheNav2stackensured autonomousmovementacrossdynamicpaths.TheimplementationofArduinoNanoandmotor encodersallowedeffectivemotioncontrolandclosed-loopfeedback,enhancingbothaccuracy and responsiveness. This project emphasizes the practical feasibility of deploying autonomous robots in small to medium-sized industries. It highlights a tangible solution to increase productivity, reduceoperationalcosts,andensuresaferworkingconditions.Thelearningsfromthisproject lay the foundation for future research and development in mobile robotics, warehouse automation, and intelligent logistics systems.

References

[1] SLAMTEC, “RPLIDAR A1,” 2013. [2] “slam_toolbox — slam_toolbox 2.6.9 documentation,” Ros.org, 2021. https://docs.ros.org/en/humble/p/slam_toolbox/ [3] “(SLAM) Navigating While Mapping — Nav2 1.0.0 Documentation,” Nav2.org, 2023. https://docs.nav2.org/tutorials/docs/navigation2_with_slam.html [4] “Nav2 — Nav2 1.0.0 Documentation,” docs.nav2.org. https://docs.nav2.org/ [5] “Home,” Articulated Robotics. https://articulatedrobotics.xyz/ [6] “ROS 2 Documentation — ROS 2 Documentation: Humble documentation,” docs.ros.org. https://docs.ros.org/en/humble/index.html

Copyright

Copyright © 2025 Mr. S. Thirumalairajan, M. Dhyanesh, V. Aswaddhaman, M. Jaya Venkata Pudeen, M. Yaswanth. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.