Design And Development Of Autonomous Robotic Arm Using TinyML

The objective of this research is to develop a cost-effective and autonomous robotic arm system for intelligent object sorting in warehouse-like environments using
TinyML. The proposed system is designed to identify labeled packages in real-time using an embedded vision model and perform corresponding pick-and-place
operations without human intervention. Emphasis is placed on low power consumption, local processing, and minimal hardware complexity to ensure deployability
in constrained settings. The integration of edge-based machine learning with predefined motion control enables responsive and efficient handling of categorized
items. This work contributes toward practical automation solutions for small-scale inventory management and demonstrates the feasibility of TinyML in real-time
robotic applications.

Github: https://github.com/Pranav-s19/Autonomous-robotic-arm-using-Tinyml

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