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.