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All Journal Rotasi Bulletin of Electrical Engineering and Informatics Journal of Mechanical Engineering Science and Technology INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Journal of Energy, Mechanical, Material and Manufacturing Engineering Journal of Electronics, Electromedical Engineering, and Medical Informatics Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems Mechanical Engineering for Society and Industry Proceeding International Pelita Bangsa Journal of Environment and Sustainability Education Control Systems and Optimization Letters Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes
Wahyu Caesarendra
Dosen Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation Other

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Journal : Journal of Robotics and Control (JRC)

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Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot Umam, Faikul; Fuad, Muhammad; Suwarno, Iswanto; Ma'arif, Alfian; Caesarendra, Wahyu
Journal of Robotics and Control (JRC) Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i2.17977

Abstract

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.
Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi Triwiyanto, Triwiyanto; Caesarendra, Wahyu; Abdullayev, Vugar; Ahmed, Abdussalam Ali; Herianto, Herianto
Journal of Robotics and Control (JRC) Vol 4, No 1 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i1.17364

Abstract

Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well.
Improving Classification Accuracy of Breast Ultrasound Images Using Wasserstein GAN for Synthetic Data Augmentation Mas Diyasa, I Gede Susrama; Humairah, Sayyidah; Puspaningrum, Eva Yulia; Durry, Fara Disa; Lestari, Wahyu Dwi; Caesarendra, Wahyu; Dewi, Deshinta Arrova; Aryananda, Rangga Laksana
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.25075

Abstract

Breast cancer remains one of the most prevalent cancers in Indonesia, and early detection plays a vital role in improving patient outcomes. Ultrasound imaging is a non-invasive and accessible technique used to classify breast conditions into normal, benign, or malignant categories. The advancement of deep learning, particularly Transfer Learning with Convolutional Neural Networks (CNNs), has significantly enhanced the performance of automated image classification. However, the effectiveness of CNNs heavily relies on large, balanced datasets—resources that are often limited and imbalanced in medical domains. To address this issue, this study explores the use of Wasserstein Generative Adversarial Networks (WGAN) for synthetic data augmentation. WGAN is capable of learning the underlying distribution of real ultrasound images and generating high-quality synthetic samples. The inclusion of the Wasserstein distance stabilizes training, with convergence observed around 2500–3000 epochs out of 5000. While synthetic data improves classifier performance, there remains a potential risk of overfitting, particularly when the synthetic images closely mirror the training data. Compared to traditional augmentation techniques such as rotation, flipping, and scaling, WGAN-generated data provides more diverse and realistic representations. Among the tested models, VGG16 achieved the highest accuracy of 83.33% after WGAN augmentation. Nonetheless, computational resource limitations posed challenges in training stability and duration. Furthermore, issues related to model generalizability, as well as ethical and patient privacy considerations in using synthetic medical data, must be addressed to ensure responsible deployment in real-world clinical settings.
Co-Authors Abdullayev, Vugar Achmad Widodo Ade Silvia Handayani Agus Sudarmanto Agus Wantoro Ahmad Rofii Ahmad Taqwa Ahmed, Abdussalam Ali Alfian Ma’arif Anant Athavale, Vijay Andini, Dwi Yana Ayu Ariesma Githa Giovany Ariswati, Her Gumiwang Aryananda, Rangga Laksana Asriyadi Asriyadi Brilliant, Muhammad Zidan Busono Soerowirdjo Dewi, Deshinta Arrova Dian Setioningsih, Endang Dian Setioningsih1 Dwi Kartini Dwi Kartini, Dwi DWI RAMADHANI Edison, Rizki Edmi Endro Yulianto Eva Yulia Puspaningrum Fadillah, Wa Ode Nurul Faikul Umam Faiza, Linda Ziyadatul Fara Disa Durry Faris, Fakhri Al Fatma Indriani Fitriana, Lutfatul Furizal, Furizal Gołdasz, Iwona Gupta, Munish Kumar Herianto Herianto Hidayat, Fathur Rachman Humairah, Sayyidah Ichwan Dwi Nugraha Ikna Awaliyani Irwan Budiman Irwan Budiman Joga Dharma Setiawan Krolczyk, Grzegorz Leni Novianti Luthfiyah, Sari Maharani, Siti Mutia Mahmood, Muhammad Azim Mahmud Mahmud MAJDOUBI, Rania Mas Diyasa, I Gede Susrama Mochammad Ariyanto Mochammad Denny Surindra Muhammad Abdillah Muhammad Fuad Muhammad Reza Faisal, Muhammad Reza Muliadi Nyayu Latifah Husni, Nyayu Latifah Pamanasari, Elta Diah Pranoto, Kirana Astari Putri, Farika Radityo Adi Nugroho Rahardja, Dimas Revindra Rahman, M. Arief Ramadhan, Bahrurrizki Ramadhan, Yogi Reza REKIK, Chokri Rozaq, Hasri Akbar Awal Rudi Irawan Sagita, Muhamad Rian Samudra, Alan Saragih, Triando Hamonangan Seno Darmanto Septiani, Fahira Setiawan, Joga D Setiawan, Nurman Siena, Laifansan Silvian, Fawaida Sitompul, Carlos R Sri Hastuty, Sri Sri Utami Handayani Sumarti, Heni Suwarno, Iswanto Triwiyanto , Triwiyanto Triyanna Widiyaningtyas Utomo, Bedjo V.H, Abdullayev W, Kusnanto Mukti Wahyu Dwi Lestari YILDIZ, Oktay Yuliana, Aviv Fitria Zy, Ahmad Turmudi