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| Main Authors: | , |
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| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20323490 |
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Table of Contents:
- <p>Manual approaches of carrying out stock auditing and inventory management have been found to be inefficient due to inefficiency and susceptibility to errors and delays caused by the lack of real-time monitoring capacity. This research paper proposes the design and development of an Autonomous Mobile Robot (AMR) for warehouse inventory auditing using NVIDIA Jetson's AI technology. The system comprises sensors, cameras, and mobility units to support its autonomous navigation capacity as well as real-time monitoring of the activities taking place in the warehouse environment. The Deep Learning YOLOv8 detection model will be adopted for the automatic tracking of shelf items in the warehouse. ROS2 will be used for robot control and communication purposes, while SLAM techniques will aid localization and map-making in the process of inventory management. This research proposal outlines how this will be done through simulation tests as well as live experiments on the robot.</p>