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All Journal J-Innovation Emitor: Jurnal Teknik Elektro
Irmawan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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4-DoF Robotic Arm for Picking and Moving RGB Color-Based Objects Using the Support Vector Machine Method Rendyansyah, Rendyansyah; Irmawan; Caroline
Emitor: Jurnal Teknik Elektro Vol 25, No 3: November 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v25i3.13552

Abstract

This study discusses designing and implementing an RGB color pattern recognition system using the Support Vector Machine (SVM) method on a 4-DoF robotic arm to perform autonomous object transfer tasks. This system integrates computer vision, artificial intelligence, and trajectory planning technologies to improve the adaptability and precision of the robot manipulator's movements. The pattern recognition process is done through image acquisition using a camera mounted on a support pole, then extraction and normalizing color values in the R, G, and B channels. These RGB values are input features for color pattern classification using SVM with Radial Basis Function (RBF) kernel and regulation parameter C = 100. The training results show that the SVM model can classify three color classes (red, yellow, and blue) with an accuracy rate of 100%. The classification data is then used to control the movements of three robots with red, orange, and blue arms, each tasked with picking up and moving objects of the corresponding color. The robot trajectory was planned using the Cubic Trajectory method, which produced smooth and coordinated movements between joints, with an average task completion time of ±10 seconds. Based on the results of 30 trials, the system showed a success rate of 96.67%, with only one failure due to gripper position inaccuracy. The results of this study indicate that the combination of the SVM and Cubic Trajectory methods can improve the efficiency and accuracy of robotic arm systems in color-based object recognition and manipulation, which has the potential to be applied to artificial intelligence-based industrial automation systems.
Implementation of the K-Nearest Neighbor Algorithm on a 4-DoF Manipulator Robot for Color-Based Object Retrieval Rendyansyah, Rendyansyah; Irmawan; Hera Hikmarika; Caroline
J-Innovation Vol. 14 No. 2 (2025): Jurnal J Innovation
Publisher : Politeknik Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55600/jipa.v14i2.352

Abstract

This study discusses implementing the K-Nearest Neighbor (K-NN) algorithm on a 4-DoF manipulator robot to pick up objects based on color. The main objective of this study is to design and test a robot control system capable of automatically recognizing, picking up, and moving objects according to color classification. The color detection process is carried out using a camera that extracts color component values in RGB space, which are then classified using the K-NN algorithm with parameter k = 5. The classification results form the basis for the movement of the manipulator robot, which is controlled through cubic trajectory-based trajectory planning, so that the movement of each servo motor joint is smooth and coordinated. Testing was conducted 30 times with three object color categories: red, green, and blue. The experimental results showed that the system could classify colors and pick up objects with a success rate of 96%. Minor failures occurred due to lighting variations that affected color detection results. Overall, the results of this study indicate that the integration of the K-NN algorithm and cubic trajectory is effective in improving the performance of manipulator robots for color-based object recognition and picking tasks and has the potential to be applied in computer vision-based industrial automation systems.
Co-Authors Caroline Hera Hikmarika Rendyansyah