Design and Development of an Autonomous Mobile Robot for Warehouse Automation Using SLAM Algorithms

Rohan Gupta, Mrudhul Nr, Akileshwarakrishnan A

Department of Artificial Intelligence and Data Science, Nehru Institute of Engineering and Technology

Coimbatore, Tamil Nadu 641105, India

Abstract

The swift expansion of e-commerce and industrial logistics has led to a substantial rise in the need for effective, dependable, and smart warehouse automation systems. Autonomous Mobile Robots (AMRs) have become a crucial solution for boosting operational efficiency, cutting labor expenses, and enhancing safety in contemporary warehouses. The core of these robots’ autonomy lies in their capacity to perceive, localize, and navigate through dynamic settings, a capability effectively realized through Simultaneous Localization and Mapping (SLAM) algorithms. This research article offers an in-depth examination of the design and development of an autonomous mobile robot for warehouse automation utilizing SLAM algorithms. The paper covers hardware architecture, software framework, algorithm selection, system integration, and performance assessment. A modular design strategy is employed, integrating sensor fusion, real-time path planning, and obstacle avoidance methods to facilitate robust autonomous navigation in warehouse settings. The methodology includes creating a differential-drive mobile robot platform equipped with LiDAR, wheel encoders, IMU, and onboard computing units. A hybrid SLAM framework, combining LiDAR-SLAM and visual odometry, is implemented to produce accurate maps and ensure precise localization. The system design incorporates mapping, localization, path

planning, and task execution modules into a cohesive architecture. Experimental results reveal that the developed robot achieves high localization accuracy, dependable navigation, and efficient task completion in both structured and semi-structured warehouse environments. Comparative analysis shows that the proposed system significantly reduces travel time and enhances operational efficiency compared to manual and semi-autonomous methods. The paper concludes that integrating SLAM algorithms with intelligent motion planning and perception systems provides a scalable and adaptable solution for warehouse automation. Future work involves multi-robot coordination, AI-based task optimization, and cloud-connected fleet management systems.

Keywords

Autonomous Systems; Mobile Robotics; SLAM; LiDAR; Path Planning; Robot Navigation; Sensor Fusion; Warehouse Automation; Localization and Mapping; Autonomous Mobile Robot (AMR).