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All Journal Jurnal Teknik Mesin Sinkron : Jurnal dan Penelitian Teknik Informatika INVOTEK: Jurnal Inovasi Vokasional dan Teknologi JTERA (Jurnal Teknologi Rekayasa) CYBERNETICS DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering Jurnal Puan Indonesia Jurnal Sains Informatika Terapan (JSIT) SEMNASTERA (Seminar Nasional Teknologi dan Riset Terapan) The Indonesian Journal of Computer Science
Adhitya Sumardi Sunarya
Politeknik Manufaktur Bandung
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Transportation

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ROS2-Based Motion Control Learning Framework for Differential Drive Mobile Robots Danu Jaya Saputro; Adhitya Sumardi Sunarya; Noval Lilansa
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1216

Abstract

This study aims to design and develop a motion control teaching aid for a differential wheel drive mobile robot as a learning medium in robotic courses. The system is implemented using the NVIDIA Jetbot AI platform integrated with the Robot Operating System (ROS2). The research methodology consists of planning, design, and implementation stages, covering wheel control and a preliminary autonomous control scheme. During the design stage, a differential drive control block diagram was developed by integrating trajectory reference input, robot kinematics, motor actuation, and wheel encoder feedback. The system implementation involves the development of multiple ROS2 nodes that communicate through standard topics, along with a web-based interface that supports both manual and autonomous control modes. Initial testing results indicate that the wheel control system provides stable motion response, while the ROS2-based architecture and web interface facilitate real-time monitoring and enhance students’ understanding of motion control concepts. This research is expected to support effective learning of motion control and autonomous mobile robot navigation in higher education.
Predictive Analytics for Energy Consumption of Autonomous Mobile Robot Using Hybrid ARIMA-XGBoost Pipit Anggraeni; Wahyu Adhie Candra; Surya Dharma Jatnika; Noval Lilansa; Adhitya Sumardi Sunarya; Nur Jamiludin Ramadhan; Andri Wiyono
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16484

Abstract

The deployment of autonomous mobile robots in smart manufacturing and intralogistics has grown rapidly, yet current battery management systems can only monitor real-time charge levels without predicting future energy consumption. This reactive limitation risks mid-mission battery depletion and production disruption. The study presents a predictive analytics system for the Polebot autonomous mobile robot integrated with a Robot Operating System 2 data historian pipeline and an InfluxDB time-series database. The objective is to evaluate whether an autoregressive time-series model or a gradient-boosted machine learning model better suits different operational conditions, specifically constant-velocity static operation versus acceleration-heavy dynamic operation. Data were collected from three sensor sources across six operational protocols covering baseline, high-load, stop-and-go, creep, burst acceleration, and mixed conditions, yielding 10,800 synchronized data points at 1 Hz after resampling. Results show that the Autoregressive Integrated Moving Average model with parameters (2,1,3) achieves a Mean Absolute Error of 1.047% and a symmetric Mean Absolute Percentage Error of 1.74% for battery State of Charge prediction under static conditions. Extreme Gradient Boosting achieves a Mean Absolute Error of 0.022 watts for motor power prediction, 136 times more accurate than the time-series model for the same variable. The proposed Condition-Based Temporal Switching framework was validated on 1,803 data points and autonomously produced 265 model transitions during a 30-minute mixed operational test, with static conditions comprising 83.9% of the validation window. Adaptive model selection outperforms single-model strategies for energy prediction in autonomous mobile robot platforms.
Co-Authors Abdur Rohman Harits Martawireja Andri Wiyono Danu Jaya Saputro Dede Sujana Disa Nabila Fitria Suryatini Hendy Rudiansyah Iman, Fachri Ismail Rokhim Ismail Rokhim Jody Jovantio Noval Lilansa Nur Iqbal Hidayatullah Nur Jamiludin Nur Jamiludin Ramadhan Nursyam, M. Nuryanti Nuryanti Permata, Nia Nuryanti Pipit Novita Anggraeni Rifa'i, Afaf Fadhil Sandy Bhawana Mulia Sari, Yunita Indah Sarosa Castrena Abadi Siti Aminah Surya Dharma Jatnika Susetyo Bagas Bhaskoro Suyono . Wahyu Adhie Candra Wahyu Adi Chandra Wening Sukma Saraswati Yuliadi Erdani, Yuliadi