Design and Development of Autonomous Mobile Robot for In-house Logistics

Abstract

The growing demand for automation in warehouses, manufacturing facilities, and logistics centers has increased the need for intelligent Autonomous Mobile Robots (AMRs). Traditional Automated Guided Vehicles (AGVs) rely on fixed paths and infrastructure, limiting flexibility in dynamic environments. This project presents a ROS-based Autonomous Mobile Robot capable of autonomous navigation, real-time obstacle avoidance, and waypoint-based movement using LiDAR sensing. The system employs a differential drive mobile platform powered by DC motors and controlled through ROS. LiDAR data is utilized for environmental perception and obstacle detection, while navigation algorithms enable autonomous movement between predefined locations. Experimental testing demonstrated reliable obstacle avoidance and accurate waypoint navigation in indoor environments. The proposed system provides a low-cost and scalable solution for autonomous material transportation and future industrial automation applications.

Introduction

This project presents the design and development of a ROS-based Autonomous Mobile Robot (AMR) capable of autonomous navigation and obstacle avoidance using LiDAR sensing technology. With the rise of Industry 4.0, automation has become essential for improving productivity, efficiency, and safety in industrial environments. Autonomous Mobile Robots are increasingly used in warehouses, material handling systems, and industrial logistics because they can navigate independently without relying on fixed tracks, markers, or predefined paths.

Unlike traditional Automated Guided Vehicles (AGVs), which depend on fixed infrastructure, the proposed AMR uses sensors, intelligent navigation algorithms, and real-time environmental perception to operate in dynamic environments. The system employs a differential drive mechanism consisting of two driven wheels and a caster wheel for stability. The Robot Operating System (ROS) serves as the primary software framework for sensor integration, navigation, communication, and robot control.

The literature survey highlights key technologies supporting autonomous navigation, including ROS-based navigation systems, LiDAR-based obstacle detection, differential drive control, autonomous warehouse transportation, and Simultaneous Localization and Mapping (SLAM) techniques. These studies provide the theoretical foundation for implementing intelligent mobile robots capable of real-time decision-making and navigation.

The proposed system architecture is organized into three functional layers:

1. Perception Layer – Uses a LiDAR sensor to scan the environment and detect obstacles.
2. Processing Layer – Utilizes ROS running on a laptop and Raspberry Pi for data processing, path planning, and navigation decisions.
3. Execution Layer – Includes an Arduino Uno, motor driver, and DC geared motors responsible for robot movement.

The hardware platform consists of a Raspberry Pi, laptop, LiDAR sensor, Arduino Uno, motor driver, DC motors, differential drive chassis, caster wheel, battery pack, and wheels. Communication between ROS and the Arduino is achieved through serial communication, enabling coordinated control of the robot.

The operational workflow begins with ROS node initialization, followed by continuous LiDAR scanning. Sensor data is processed to identify obstacles, and navigation algorithms generate safe paths toward target waypoints. Velocity commands are transmitted to the Arduino, which converts them into wheel-speed commands and PWM signals to control motor movement. Whenever obstacles are detected, obstacle avoidance behavior is automatically activated.

The ROS software pipeline includes:

• ROS Framework
• LiDAR Driver Package
• Differential Drive Controller
• Navigation Stack
• RViz Visualization Tool
• Waypoint Navigation Module

Real-time communication is maintained through ROS topics such as /scan, /cmd_vel, /odom, and /tf, enabling monitoring, visualization, and debugging through RViz.

Experimental testing was conducted in indoor laboratory environments. The system achieved:

• 98% accuracy in straight-line navigation.
• 100% accuracy in obstacle avoidance.
• 94% accuracy in waypoint navigation.
• 97.3% overall system performance.

Results demonstrate that the robot successfully navigates through predefined waypoints while avoiding obstacles in both static and dynamic environments. The modular architecture supports future enhancements such as dynamic path planning, autonomous pick-and-place operations, and multi-robot coordination. Additionally, the prototype was developed at a relatively low cost of approximately ?21,247, making it an affordable alternative to commercial AMR systems.

Conclusion

This project successfully developed a ROS-based Autonomous Mobile Robot capable of autonomous navigation and obstacle avoidance using LiDAR sensing. The differential drive architecture provided stable movement while ROS enabled efficient integration of sensors, navigation algorithms, and motor control. The system demonstrated reliable waypoint navigation and obstacle avoidance performance, making it suitable for warehouse automation, industrial transportation, and smart factory applications.

References

[1] M. Quigley et al., \"ROS: An Open-Source Robot Operating System,\" ICRA Workshop on Open Source Software, 2009. [2] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, 2005. [3] G. Grisetti, C. Stachniss, and W. Burgard, \"Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters,\" IEEE Transactions on Robotics, vol. 23, no. 1, pp. 34–46, 2007. [4] B. Siciliano and O. Khatib, Springer Handbook of Robotics, Springer, 2016. [5] J. Borenstein and Y. Koren, \"The Vector Field Histogram—Fast Obstacle Avoidance for Mobile Robots,\" IEEE Transactions on Robotics and Automation, vol. 7, no. 3, pp. 278–288, 1991. [6] E. Marder-Eppstein et al., \"The ROS Navigation Stack: Design and Implementation,\" Open Source Robotics Foundation, 2010. [7] J. Chisholm, \"Articulated Robotics – ROS 2 Tutorials and Mobile Robot Navigation,\" Articulated Robotics, 2022–Present. Available: Articulated Robotics

Copyright

Copyright © 2026 Thirumalai Rajan, Karthik , Hari , Mukil Dharshan, Tajeswar . 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.