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Stream/Area:
-
Subject:
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Paper ID:
IJSREM61994
Volume:
10 |
4 |
April 2026
Pages:
1--
Publication Frequency:
Monthly (12 issues per year)
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.
Department of Electronics and Telecommunication
Sinhgad College Of Engineering Pune, India
Department of Electronics and Telecommunication
Sinhgad College Of Engineering Pune, India
Department of Electronics and Telecommunication
Sinhgad College Of Engineering Pune, India
Department of Electronics and Telecommunication
Sinhgad College Of Engineering Pune, India
Automation in material handling and sorting has become a critical requirement for improving efficiency in manufacturing and packaging industries. This project presents the design and implementation of a low-cost robotic arm system capable of automatically detecting and sorting objects based on their colour and shape. The proposed system employs an ESP32-CAM module to capture real-time images of objects placed in the sorting area. The captured images are processed using Edge Impulse, which enables on-device machine learning for object classification. The system utilizes trained models to identify the dominant colour and geometric shape of objects through image preprocessing and feature extraction. The detection results are then transmitted to a microcontroller, which controls the robotic arm’s servo motors to pick and place objects into the appropriate bins. Unlike conventional industrial solutions, this project focuses on developing a compact, energy-efficient, and cost-effective prototype suitable for small-scale industries, educational institutions, and research applications. The modular design of the system allows easy scalability and adaptation to different object categories, making it a flexible and accessible solution for intelligent automation.
KEYWORDS
This paper presented an AI-driven robotic arm system for automatic colour and object sorting using an ESP32-CAM and Edge Impulse. The system successfully detects and classifies objects in real time and performs accurate pick-and-place operations using a servo-based robotic arm.The results show high classification accuracy and reliable performance, demonstrating the effectiveness of embedded machine learning in automation. Overall, the proposed system offers a low-cost, energy-efficient, and scalable solution suitable for small-scale industrial and educational applications.
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Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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