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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Bulletin of Electrical Engineering and Informatics
Rendyansyah Rendyansyah
Universitas Sriwijaya
Control & Systems Engineering Education Electrical & Electronics Engineering Energy Social Sciences

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Pengembangan Olfactory Hexapod Robot Untuk Mendeteksi Kebocoran Sumber Aroma Rendyansyah Rendyansyah; Aditya P. P. Prasetyo; Kemahyanto Exaudi; Abdul Wahid Sempurna; Bangun Sudrajat
JTEV (Jurnal Teknik Elektro dan Vokasional) Vol 7, No 2 (2021): JTEV (Jurnal Teknik Elektro dan Vokasional)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.512 KB) | DOI: 10.24036/jtev.v7i2.112851

Abstract

Kebocoran aroma/gas berdampak merugikan dalam dunia Industri, baik dalam skala besar maupun kecil. Gas bersifat menyebar di udara, ada yang mudah terbakar bahkan menyebabkan polusi udara di sekitar wilayah tersebut. Secara teknis diperlukan sebuah sistem untuk membantu meng-investigasi ruangan. Seperti sistem e-nose yang terintegrasi dengan robot. Di dalam penelitian ini telah dikembangkan Hexapod Robot yang terintegrasi dengan sensor pendeteksi aroma, gabungan keduanya disebut Olfactory Hexapod Robot. Motivasi dikembangkan robot ini bertujuan untuk menjelajah di lingkungan pada permukaan kasar. Adapun metode yang digunakan adalah Fuzzy Logic sebagai pengendali untuk navigasi, dan Learning Vector Quantization untuk mengetahui aroma kebocoran gas. Adapun pengujian pada robot dilakukan di dalam Skala Laboratorium, dan arena telah diatur dalam berbagai kondisi. Hasil percobaan menunjukkan bahwa Olfactory Hexapod Robot dapat ber-navigasi di dalam kondisi ruangan dan mencari sumber aroma gas. Percobaan pada robot mencari sumber gas dilakukan sebanyak 30 kali, dan tingkat keberhasilan yang diperoleh mencapai 86%.
Real-time object detection and distance measurement for humanoid robot using you only look once Suci Dwijayanti; Bhakti Yudho Suprapto; Mutiyara Mutiyara; Rendyansyah Rendyansyah
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7476

Abstract

Humanoid robots are designed to mimic human structures and utilize cameras to process visual input to identify surrounding objects. However, previous studies have focused solely on object detection, overlooking both the complexities of real-world implementation and the significance of calculating the distance between objects and the robot. This study proposes a system that employs the you only look once (YOLO) algorithm to detect various objects in the proximity of a robot. Using a dataset of primary data collected in a laboratory, the detected objects are from 12 classes, including humans, chairs, tables, cabinets, computers, books, doors, bottles, eggs, learning modules, cups, and hands, with each class comprising 1500 data points. Two YOLO architectures, namely tiny YOLOv3 and tiny YOLOv4, are assessed for their performance in object detection, with the tiny YOLOv4 demonstrating a superior accuracy of 82.99% compared to tiny YOLOv3. Evaluation under simulated conditions yields an accuracy of 74.16%, while in real-time scenarios, accuracies are 61.66% under bright conditions and 38.33% under dim conditions, affirming tiny YOLOv4’s efficacy. Moreover, this study reveals an average error distance of 31% between an object and the robot in real-time conditions. The developed system enhances human–robot interaction capabilities via data transmission.
Co-Authors Abdul Wahid Sempurna Aditya Putra Perdana Prasetyo Bangun Sudrajat Bhakti Yudho Suprapto Kemahyanto Exaudi Mutiyara Mutiyara Suci Dwijayanti