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All Journal Rotasi Mechatronics, Electrical Power, and Vehicular Technology JURNAL TEKNIK MESIN Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Rekayasa Mesin Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknik Mesin Indonesia Journal of Robotics and Control (JRC)
Joga Dharma Setiawan
Departemen Teknik Mesin, Fakultas Teknik, Universitas Diponegoro, Jl. Prof. Soedarto, SH, Tembalang, Semarang, Jawa Tengah, 50275
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Mechatronics, Electrical Power, and Vehicular Technology

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Design of image classification system for fabric inspection process using Raspberry Pi Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; Munadi, Munadi; Diki, Diki
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.863

Abstract

This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %.
Non-linear model predictive control with single-shooting method for autonomous personal mobility vehicle Pratama, Rakha Rahmadani; Baskoro, Catur Hilman Adritya Haryo Bhakti; Setiawan, Joga Dharma; Dewi, Dyah Kusuma; Paryanto, Paryanto; Ariyanto, Mochammad; Saputra, Roni Permana
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 2 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.1105

Abstract

The advancement of autonomous vehicle technology has markedly evolved during the last decades. Reliable vehicle control is one of the essential technologies in this domain. This study aims to develop a proposed method for controlling an autonomous personal mobility vehicle called SEATER (Single-passenger Electric Autonomous Transporter), using Non-linear Model Predictive Control (NMPC). We propose a single-shooting technique to solve the optimal control problem (OCP) via non-linear programming (NLP). The NMPC is applied to a non-holonomic vehicle with a differential drive setup. The vehicle utilizes odometry data as feedback to help guide it to its target position while complying with constraints, such as vehicle constraints and avoiding obstacles. To evaluate the method's performance, we have developed the SEATER model and testing environment in the Gazebo Simulation and implemented the NMPC via the Robot Operating System (ROS) framework. Several simulations have been done in both obstacle-free and obstacle-filled areas. Based on the simulation results, the NMPC approach effectively directed the vehicle to the desired pose while satisfying the set constraints. In addition, the results from this study have also pointed out the reliability and real-time performance of NMPC with a single-shooting method for controlling SEATER in the various tested scenarios.
Co-Authors AA Sudharmawan, AA Achmad Widodo Agus Mukhtar Anggi Arivian Arifin, Zainal Ariyanto, Mochammad Aulia Syukri Aulia, Dito Rahmat Baskoro, Catur Hilman Adritya Haryo Bhakti Budi Setiyana D. Michael Hendra Damar Yusuf Sutrisno Diki, Diki Dimas Bimo Nugroho Dwiyanto, Yahya Fikri Dyah Kusuma Dewi Eden Wawi Putra Eflita Yohana Emmanuel Agung Nugroho Gunawan Dwi Haryadi Hanif Muhammad Rakha Hujjatul Anam I Made Wicaksana Ekaputra Ilham F, Muhammad Ario Ismoyo Haryanto J. Jamari Khoiri Rozi Kurnianto, Andhieka Rizky La Ode Muhamad Munadi M, Munadi M.Pd S.T. S.Pd. I Gde Wawan Sudatha . Mochammad Ariyanto Mochammad Ariyanto Mochammad Ariyanto Moh. Arozi Muhammad Ritzky Novhar Munadi Munadi Munadi Munadi Nana Supriyana Nugraha, Faryan Nurminda Eka Indra Pramana Paryanto Paryanto Paryanto Petrus Londa Pratama, Rakha Rahmadani Purwiantoro, Arman Rifka Hana Indraswari Rifky Ismail Riza Muhida Roni Permana Saputra RR. Ella Evrita Hestiandari Rusnaldy Rusnaldy Rustiyanti, Alifa Setiawan, Muhammad Farouk Toni Prahasto Wahyu Caesarendra Yuwana, Rizkia Wira Zainal Arifin