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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM67817

AI SMART INDUSTRIAL MATERIAL TRANSPORT ROBOT

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67817

Volume:

10 |

10 |

Oct 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67817

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Shravani Sanjay Raskar

Shravani Sanjay Raskar

Electronics & Telecommunication Engineering

JSPM's Bhivrabai Sawant Polytechnic, Wagholi

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Diksha Uttam Zunjare

Diksha Uttam Zunjare

Electronics & Telecommunication Engineering

JSPM's Bhivrabai Sawant Polytechnic, Wagholi

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Namrata Ankush Pawale

Namrata Ankush Pawale

Electronics & Telecommunication Engineering

JSPM's Bhivrabai Sawant Polytechnic, Wagholi

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Radha Yogesh Mudgal

Radha Yogesh Mudgal

Electronics & Telecommunication Engineering

JSPM's Bhivrabai Sawant Polytechnic, Wagholi

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Mentors

 Vikas Bhiku Takale

Vikas Bhiku Takale

Electronics & Telecommunication Engineering

JSPM's Bhivrabai Sawant Polytechnic, Wagholi

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Abstract

Industrial material handling is an essential activity in manufacturing and storage environments, but conventional transportation methods generally depend on continuous human effort and may require considerable time. This paper presents the design of an AI Smart Industrial Material Transport Robot intended to provide an intelligent and automated approach to internal material movement. The proposed system combines Artificial Intelligence with an ESP32 controller, RFID module, load cell, ultrasonic sensor, camera module, motor driver, DC motors and IoT/Wi-Fi connectivity. RFID is used for material identification, while the load cell provides weight-related information and the ultrasonic sensor supports obstacle detection. Camera information is considered for image processing and AIbased decision making. The ESP32 coordinates the control process and operates the motors through the L298N motor driver. The proposed architecture is aimed at reducing manual handling, improving workplace safety, reducing transportation time and supporting efficient industrial operations. Experimental performance results are not included at this stage because the prototype is under development.

KEYWORDS

Artificial Intelligence ESP32 RFID Industrial Automation Material Handling Obstacle Detection IoT Mobile Robot.

Cite This Article

Shravani Sanjay Raskar, Diksha Uttam Zunjare, Namrata Ankush Pawale, Radha Yogesh Mudgal, Vikas Bhiku Takale, "AI SMART INDUSTRIAL MATERIAL TRANSPORT ROBOT," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-7, Oct 2026. https://doi.org/10.55041/IJSREM67817

Full Article/Conclusion

The Al Smart Industrial Material Transport Robot presents an integrated approach automated material movement in industrial environments. The proposed architecture combines RFID-based material identification, load-cell weight sensing, ultrasonic obstacle detection, camera-based visual information, Al processing, ESP32 control, L298N motor driving, DC motors and IoT/Wi-Fi monitoring. The combination intended to reduce manual handling, support safer movement and improve the efficiency of repetitive internal transportation. The project is currently at the prototype-development stage; therefore, final experimental measurements are not included. Practical testing will be required to establish load capacity, navigation performance, obstacle response, battery endurance and  

References

[1]             K. Ellithy et al., “AGV and Industry 4.0 in Warehouses,” International Journal of Advanced Manufacturing Technology, 2024. [2]             T. Lackner et al., “Autonomous Mobile Robots in Intralogistics,” Procedia CIRP, 2024. [3] W. X. Oh et al., “Autonomous Mobile Robot for Transporting Goods,” 2024. [4]             N. P. Panta et al., “Autonomous Mobile Robot for Material Handling,” 2024. [5]             “Autonomous Industrial Mobile Warehouse,” Applied Sciences, 2024. [6]             Y. Gong and R. B. M. de Koster, “Autonomous Mobile Robot Systems,” Transportation Science, 2024. [7] “Digital Twin-Based Reinforcement Learning for AMR,” International Journal of Computer Integrated Manufacturing, 2024. [8]             N. Demir et al., “Autonomous Mobile Robot Using ROS,” International Journal of 3D Printing Technologies, 2024. [9]             D. Selvaraj et al., “AGV for Material Handling Using RFID,” 2024. [10]          “IoT-Based Robotic Arm for Product Sorting,” Electronics, 2025.

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Publication Date

10/12/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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