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p-Index From 2021 - 2026
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All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Advances in Intelligent Informatics
Oskar Natan
Universitas Gadjah Mada
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering

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Iron welding spot segmentation using Nested UNet (UNet++) enhanced with diverse convolutional modules Thomas Brian; Oskar Natan; Yohanes Yohanie Fridelin Panduman; Anggarjuna Puncak Pujiputra
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Welding inspection plays an essential role in manufacturing industries to ensure the integrity and quality of weld joints. However, the prevalent manual inspection procedures are inherently subjective, prone to bias, and result in inconsistent quality assessments. Therefore, there is a strong need for an automated, intelligent system capable of objectively detecting welding spots. To address this, we propose an advanced segmentation model based on deep learning and computer vision techniques, specifically utilizing a Nested UNet (UNet++) architecture enhanced by extensive architectural modifications and comprehensive hyperparameter tuning. To further optimize segmentation performance, we systematically compare various convolutional blocks integrated into the bottleneck of the network architecture. Our experimental evaluation demonstrates that employing a VGG convolutional block at the bottleneck of Nested UNet achieves the highest performance, reaching an Intersection over Union (IoU) score of 76.18% and a validation loss of 0.1713 on our collected dataset.
LiDAR-based sensor fusion and navigation for indoor autonomous mobile robots in warehouse environments Rifda Hakima Sari; Jazi Eko Istiyanto; Oskar Natan; Zaidan Hakim; Danang Lelono; Andi Dharmawan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp295-306

Abstract

An indoor navigation system for an autonomous mobile robot was developed using LiDAR-based perception and multi-sensor fusion. The system combines 2D LiDAR, inertial measurement unit (IMU), and wheel encoder measurements within a simultaneous localization and mapping (SLAM) framework to support real-time localization, while the ROS2 Nav2 stack manages global path planning and local obstacle avoidance through A*-based planning and costmap-driven control. Evaluation in a warehouse-like environment showed that the robot maintained stable localization with low drift and completed autonomous navigation missions with a success rate of 93.33%. During operation, the robot was able to avoid static obstacles consistently and adjust its trajectory in response to simple dynamic obstacles through online replanning. These results indicate that the proposed system is suitable for practical indoor logistics scenarios requiring reliable navigation in structured environments. At the same time, the findings suggest the need for further improvement to handle environments with higher dynamics and denser obstacle configurations.
Humanoid robot balance control system during backward walking using linear quadratic regulator Muhammad Arsyi; Andi Dharmawan; Bakhtiar Alldino Ardi Sumbodo; Muhammad Auzan; Jazi Eko Istiyanto; Oskar Natan
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i4.pp472-482

Abstract

Humanoid robots are designed to replicate human activities, including tasks in hazardous environments. However, maintaining balance during backward walking remains a significant challenge due to center of mass (CoM) shifts beyond the support polygon and limited knee joint motion. This study proposes a control strategy that integrates a linear quadratic regulator (LQR) with optimized walking patterns to enhance dynamic stability. The approach combines LQR-based control with CoM trajectory planning to ensure safe and stable backward walking. The methodology includes inverse kinematics for generating walking patterns and the use of Inertial Measurement Unit (IMU) sensors to estimate the CoM trajectory. LQR parameters were tuned through simulation to improve responsiveness to disturbances. Evaluation metrics focused on CoM deviation, rise time, settling time, and overshoot. Experimental results demonstrate that the proposed LQR system effectively maintains the CoM within 5% of the support polygon boundary. The system achieved rise times under one second and settling times below two seconds, while minimizing pitch and roll overshoots. Compared to proportional control, the proposed method significantly improves stability and reduces the risk of falling. This research advances control strategies for humanoid robots, contributing to improved mobility and operational safety. Moreover, it supports Sustainable Development Goal (SDG) 9 by promoting innovation in intelligent robotic systems that can assist in complex or high-risk environments.
Edge-aware distilled segmentation with pseudo-label refinement for autonomous driving perception Novelio Putra Indarto; Oskar Natan; Andi Dharmawan
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i4.pp528-538

Abstract

Achieving precise semantic segmentation is essential for enabling real-time perception in autonomous systems, yet leading approaches typically require substantial annotated data and powerful hardware, restricting their use on devices with limited resources. This work introduces an efficient segmentation framework that integrates pseudo-label refinement, knowledge distillation, and entropy-based confidence filtering to train compact student networks suitable for edge deployment. High-quality pseudo-labels are first produced by a robust teacher network, then further improved using a dense conditional random field to boost spatial consistency. An entropy-based selection mechanism removes unreliable predictions, ensuring that only the most trustworthy labels guide the student model's training. The use of knowledge distillation effectively transfers detailed semantic understanding from the teacher to the student, enhancing accuracy without added computational overhead. Experimental results with multiple EfficientNet backbones reveal that this pipeline improves segmentation accuracy and output clarity, while also supporting real-time or near real-time inference on CPUs with limited processing power. Extensive ablation and qualitative studies further confirm the method's robustness and flexibility for real-world edge applications.
Co-Authors Andi Dharmawan Anggarjuna Puncak Pujiputra Bakhtiar Alldino Ardi Sumbodo Jazi Eko Istiyanto Muhammad Arsyi Muhammad Auzan Novelio Putra Indarto Rifda Hakima Sari Thomas Brian Yohanes Yohanie Fridelin Panduman Zaidan Hakim