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All Journal International Journal Science and Technology (IJST)
Jaqlin Tamaka
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : International Journal Science and Technology (IJST)

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Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition Excellsdeo Ndahawali; Jonah Mekel; Jaqlin Tamaka; GheridsDipipi, GheridsDipipi; Rick Resa Wahani; Michael Edward G. Kimbal; Deko Trio Desembara; Motulo, Firmansyah Reskal
International Journal Science and Technology Vol. 4 No. 3 (2025): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i3.2386

Abstract

Robotic-arm deployment beyond specialized facilities is often constrained by time-intensive programming and the need for expert operators, while gesture-based control can lose reliability due to sensor noise, drift, and inter-user variability. Objective: This study develops a low-cost, embedded robotic arm control system that learns from human demonstrations. Methodology: A quantitative experimental prototyping approach was used by building a 3-DOF robotic arm with an MPU6050 IMU and an Arduino Mega 2560. Multi-user gesture trials were collected, and system performance was analyzed through end-to-end evaluation of recognition accuracy, response time, learning efficiency, and motion replication error. Findings: The system achieved 85% gesture recognition accuracy, a 195 ms average response time, and a 4.2° mean absolute joint-angle error (SD = 2.1°), reaching target performance within ≤5 adaptation iterations while operating within microcontroller memory limits. Implications: The results support the feasibility of real-time, gesture-driven robotic arm control on resource-constrained embedded hardware for educational and light industrial use, enabling faster setup and user personalization without extensive pre-training. Originality: This work integrates embedded motion pattern recognition with error-based adaptive learning in a low-cost 3-DOF platform and reports consolidated end-to-end evidence (accuracy–latency–learning convergence–replication fidelity) to demonstrate practical feasibility.
Co-Authors Deko Trio Desembara Excellsdeo Ndahawali GheridsDipipi, GheridsDipipi Jonah Mekel Michael Edward G. Kimbal Motulo, Firmansyah Reskal Rick Resa Wahani