Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition

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.
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How to Cite

Excellsdeo Ndahawali, et al. (2025). Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition. International Journal Science and Technology (IJST), 4(3). https://doi.org/10.56127/ijst.v4i3.2386

Excellsdeo Ndahawali; Jonah Mekel; Jaqlin Tamaka; GheridsDipipi, GheridsDipipi; Rick Resa Wahani; Michael Edward G. Kimbal; Deko Trio Desembara; Motulo, Firmansyah Reskal, "Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition," International Journal Science and Technology (IJST), vol. 4, no. 3, 2025.

Excellsdeo Ndahawali; Jonah Mekel; Jaqlin Tamaka; GheridsDipipi, GheridsDipipi; Rick Resa Wahani; Michael Edward G. Kimbal; Deko Trio Desembara; Motulo, Firmansyah Reskal. "Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition." International Journal Science and Technology (IJST), vol. 4, no. 3, 2025.

Excellsdeo Ndahawali; Jonah Mekel; Jaqlin Tamaka; GheridsDipipi, GheridsDipipi; Rick Resa Wahani; Michael Edward G. Kimbal; Deko Trio Desembara; Motulo, Firmansyah Reskal. "Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition." International Journal Science and Technology (IJST) 4, no. 3 (2025).

Excellsdeo Ndahawali, et al. (2025) 'Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition', International Journal Science and Technology (IJST), 4(3). doi: 10.56127/ijst.v4i3.2386.

Excellsdeo Ndahawali; Jonah Mekel; Jaqlin Tamaka; GheridsDipipi, GheridsDipipi; Rick Resa Wahani; Michael Edward G. Kimbal; Deko Trio Desembara; Motulo, Firmansyah Reskal. Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition. International Journal Science and Technology (IJST). 2025;4(3).

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