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Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 708 Documents
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A Comprehensive Review of EEGLAB for EEG Signal Processing: Prospects and Limitations Pamungkas, Yuri; Rangkuti, Rahmah Yasinta; Triandini, Evi; Nakkliang, Kanittha; Yunanto, Wawan; Uda, Muhammad Nur Afnan; Hashim, Uda
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.v6i4.27084

Abstract

EEGLAB is a MATLAB-based software that is widely used for EEG signal processing due to its complete features, analysis flexibility, and active open-source community. This review aims to evaluate the use of EEGLAB based on 55 research articles published between 2020 and 2024, and analyze its prospects and limitations in EEG processing. The articles were obtained from reputable databases, namely ScienceDirect, IEEE Xplore, SpringerLink, PubMed, Taylor & Francis, and Emerald Insight, and have gone through a strict study selection stage based on eligibility criteria, topic relevance, and methodological quality. The review results show that EEGLAB is widely used for EEG data preprocessing such as filtering, ICA, artifact removal, and advanced analysis such as ERP, ERSP, brain connectivity, and activity source estimation. EEGLAB has bright prospects in the development of neuroinformatics technology, machine learning integration, multimodal analysis, and large-scale EEG analysis which is increasingly needed. However, EEGLAB still has significant limitations, including a high reliance on manual inspection in preprocessing, low spatial resolution in source modeling, limited multimodal integration, low computational efficiency for large-scale EEG data, and a high learning curve for new users. To overcome these limitations, future research is recommended to focus on developing more accurate automation methods, increasing the spatial resolution of source analysis, more efficient multimodal integration, high computational support, and implementing open science with a standardized EEG data format. This review provides a novel contribution by systematically mapping EEGLAB’s usage trends and pinpointing critical technical and methodological gaps that must be addressed for broader neurotechnology adoption.
Dynamic Clustering of Multi-Mobile Robot System using Gaussian Mixture Model Xuan, Hung Truong; Manh, Thang Pham; Quang, Nha Nguyen; Hong, Hanh Nguyen Thi
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.27184

Abstract

Managing large fleets of mobile robots poses significant challenges to system coordination and workload. An effective grouping strategy is crucial for enhancing operational performance and scalability. This paper introduces a two-stage dynamic clustering method (DCM), a novel framework for organizing robots into manageable groups. The methodology utilizes a Gaussian Mixture Model and the Expectation-Maximization algorithm to cluster robots based on their path intersection points. A unique "cost" parameter, formulated a least squares objective function, is proposed to guide the selection of near-optimal, workload-balanced configurations. The results from extensive simulations demonstrated the framework's effectiveness. On a single dataset, DCM exhibited exceptional reliability, maintaining a stable objective function value even as the number of robots per cluster fluctuated across runs. A sensitivity analysis over multiple unique datasets confirmed the model's adaptive strength, showing its ability to re-configure clusters. This adaptability was highlighted by the mean objective function value varying across different scenarios. Further analysis involving reduced robot populations and obstacle-filled environments validated DCM's generalizability and environment-independent nature. The robot distribution mechanism was consistently equitable and balanced. Statistical validation, including bootstrapping resamples, confirmed the stability and reliability of the performance estimates. The method also steadily maintained a high level of performance by adapting to internal variations. Moreover, every robot was successfully assigned to all clusters across all trials. The research concludes that DCM is a robust, adaptive, and environment-independent framework. It successfully balances performance stability with the flexibility to respond to new operational conditions, proving it is an effective solution for multi-robot coordination.
Lyapunov Truncation for Low-Order Modeling of Linear Time-Invariant Unmanned Rotorcraft Flight Dynamics Le, Ngoc-Hoi; Pham, Van-Cuong; Dang, Dinh-Chung; Nguyen, Thi-Mai-Huong
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.v6i4.27251

Abstract

This study addresses model order reduction for unmanned rotorcraft flight dynamics, specifically focusing on the development of computationally efficient, low-order representations for fourth-order linear time-invariant (LTI) models. The research contribution is a systematic evaluation of the Lyapunov Truncation (LT) algorithm in the context of rotorcraft dynamics, where the need for reduced-order models is motivated by real-time control and simulation requirements in autonomous aerial vehicles. The LT method exploits controllability and observability Gramians to identify dominant state directions, but it inherently relies on the assumptions of linearity and time-invariance. The reduction process yields models of third, second, and first order, which are comparatively assessed using time-domain (RMSE), peak error, frequency-domain (total error), and statistical reliability metrics. Results show that the second-order reduced model achieves a 50% reduction in system complexity, with RMSE as low as 0.0537 rad/s in the lateral-to-pitch channel and relative errors consistently below 200% for all channels. Maximum deviations remain under one unit for most channels, and total frequency-domain error is minimized at this order (1519.48). In contrast, first-order models exhibit RMSEs exceeding 1000% in certain channels and peak deviations above 4 units, highlighting limitations in preserving stability margins and transient behaviors. Overall, the study demonstrates that second-order Lyapunov Truncation achieves the optimal balance between computational efficiency and dynamic fidelity, supporting its adoption for practical control-oriented reduction of LTI unmanned rotorcraft models within their valid operational envelope.
The Emerging Role of Artificial Intelligence in Identifying Epileptogenic Zone: A Systematic Literature Review Pamungkas, Yuri; Radiansyah, Riva Satya; Pratasik, Stralen; Krisnanda, Made; Derek, Natan
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.27281

Abstract

Identifying epileptogenic zones (EZs) is a crucial step in the pre-surgical evaluation of drug-resistant epilepsy patients. Conventional methods, including EEG/SEEG visual inspection and neurofunctional imaging, often face challenges in accuracy, reproducibility, and subjectivity. The rapid development of artificial intelligence (AI) technologies in signal processing and neuroscience has enabled their growing use in detecting epileptogenic zones. This systematic review aims to explore recent developments in AI applications for localizing epileptogenic zones, focusing on algorithm types, dataset characteristics, and performance outcomes. A comprehensive literature search was conducted in 2025 across databases such as ScienceDirect, Springer Nature, and IEEE Xplore using relevant keyword combinations. The study selection followed PRISMA guidelines, resulting in 34 scientific articles published between 2020 and 2024. Extracted data included AI methods, algorithm types, dataset modalities, and performance metrics (accuracy, AUC, sensitivity, and F1-score). Results showed that deep learning was the most used approach (44%), followed by machine learning (35%), multi-methods (18%), and knowledge-based systems (3%). CNN and ANN were the most commonly applied algorithms, particularly in scalp EEG and SEEG-based studies. Datasets ranged from public sources (Bonn, CHB-MIT) to high-resolution clinical SEEG recordings. Multimodal and hybrid models demonstrated superior performance, with several studies achieving accuracy rates above 98%. This review confirms that AI (especially deep learning with SEEG and multimodal integration) has strong potential to improve the precision, efficiency, and scalability of EZ detection. To facilitate clinical adoption, future research should focus on standardizing data pipelines, validating AI models in real-world settings, and developing explainable, ethically responsible AI systems.
Queen Honey Bee Migration-Based Optimization for Battery Management of Internet of Things Devices in High-Risk Emergency Scenarios Widiatmoko, Dekki; Aripriharta, Aripriharta; Sujito, Sujito
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.v6i4.27285

Abstract

Efficient energy management in Internet of Things (IoT) devices is critical in dynamic, resource-constrained operational environments. This study proposes the Queen Honey Bee Migration (QHBM) optimization algorithm for managing Li-ion battery performance in IoT systems, employing the Shepherd battery model to simulate the nonlinear discharge behavior under varying load conditions. Three simulation scenarios of increasing complexity (5, 10, and 20 monitoring points) are used to represent urban operational dynamics. The performance of QHBM is quantitatively compared with four conventional optimization algorithms seperti Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), and Firefly Algorithm (FA). Results show that QHBM maintains a current range of 3.80–5.20 A and a voltage range of 3.65–3.95 V, with State of Charge (SoC) predictions between 75–98%. It also achieves the fastest computation time (0.42–1.20 seconds) and demonstrates more stable performance under high-load dynamic scenarios compared to the other methods. This approach provides an adaptive and efficient optimization framework to support energy-aware decision-making in IoT systems operating in energy-constrained urban environments.
Predicting Occupational Heat Stress in Critical Sectors: A Sector-Based Systematic Review of Wearable Sensing, IoT Platforms, and Machine Learning Models Bonifacio, Roger Fernando Asto; Milla, Blanca Yeraldine Buendia; Rojas, Jezzy James Huaman
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.27377

Abstract

Occupational heat stress is a growing threat to the health and productivity of workers exposed to extreme environmental conditions. This issue is particularly acute in sectors such as construction, mining, agriculture, and heavy industry, where high heat exposure and physical workload are constant. This systematic review analyzes 96 scientific articles published in recent years, aiming to identify emerging technological systems focused on the prediction, monitoring, and mitigation of occupational heat stress. The main contribution of this study lies in the cross-sectoral categorization of recent solutions, providing a comparative framework that highlights knowledge gaps, methodological limitations, and opportunities for innovation. Following PRISMA guidelines, data were extracted on sensor type, predictive models, validation environments, and the sector of application. Technologies were classified into five main categories: wearable sensors, IoT-based monitoring platforms, hybrid thermal indices, predictive models based on environmental and physiological inputs, and decision-support tools. The results reveal a strong presence of wearable systems. Adoption is further constrained by socio-technical barriers such as worker compliance, PPE burden, costs, data privacy, and interoperability gaps. However, only a small fraction of studies conducted in-field validation under real thermal stress conditions, and even fewer included longitudinal ergonomic trials, limiting generalizability, with additional concerns about heterogeneous outcome measures and inconsistent definitions of heat stress across studies. A sectoral imbalance is also observed, with construction and industrial environments receiving more research attention than mining, agriculture, and indoor workplaces. In conclusion, we propose a practical roadmap for the adoption of standardized data schemas and protocols, field trials across complete work cycles, privacy-preserving analytics (federated learning), and integration of ergonomic and organizational controls. In highly humid or high radiation settings, complementing or replacing WBGT with hybrid indices (UTCI) can improve risk estimation and enable more actionable work rest and hydration alerts.
Computer Vision for Food Nutrition Assessment: A Bibliometric Analysis and Technical Review Purwati, Nani; Isnanto, R. Rizal; Kartasurya, Martha Irene
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.27525

Abstract

This study examines the latest trends, challenges, and advances in food image segmentation and computer vision-based nutritional analysis. Traditional nutritional assessment methods such as food diaries and questionnaires are limited by their reliance on participant recall and manual processing, which reduces their accuracy and efficiency. As an alternative, advances in machine learning and deep learning have shown potential in automating food identification and estimating nutrient content, such as calories, protein, carbohydrates, and fat. This study was conducted through bibliometric analysis and technical review of publications from the Scopus database, using a structured search strategy and applying inclusion and exclusion criteria. Articles were selected based on topic relevance, use of machine learning or deep learning methods, publication in English, and publication between 2020 and 2024. The review identified key research trends, key contributors, popular methods such as CNN and YOLO, and the most frequently reported limitations, including lack of dataset diversity, inaccuracy in food volume estimation, and the need for real-time integrated systems. These limitations were analyzed based on the methodology and findings of the reviewed studies. This review is expected to be a comprehensive reference for researchers and practitioners in developing food image segmentation technology for more accurate and applicable nutritional assessment.
Hybrid Path Planning for Wheeled Mobile Robot Based on RRT-star Algorithm and Reinforcement Learning Method Pham, Hoang-Long; Bui, Nhu-Nghia; Dang, Thai-Viet
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.v6i4.27678

Abstract

In the field of wheeled mobile robots (WMRs), path planning is a critical concern. WMRs employ advanced algorithms to find out the feasible path from a starting point to a specific destination. The paper proposes efficient and optimal path planning for WMRs, integrating collision avoidance strategies and smoothed techniques to determine the best route during navigation. The proposed hybrid path planning consists of improved RRTstar algorithm and reinforcement learning method. Therefore, the RRT* algorithm employs random sampling in conjunction with a reinforcement learning model to purposefully guide the sampling process towards areas that demonstrate an increased likelihood of successful navigation completion. The proposed RRTstar-RL algorithm generates significantly shorter trajectories compared to the traditional RRT and RRTstar methods. Specifically, the path length with the proposed algorithm is 11.323 meters, while the lengths for RRT and RRTstar are 15.74 and 14.40 meters, respectively. Moreover, the optimization of computation time, especially when using pre-trained data, greatly speeds up the path-finding calculation process. In particular, the time needed to generate the optimal path with the RRTstar-RL algorithm is 2.02 times faster than that of RRTstar and 1.6 times faster than RRT. Finally, the proposed RRTstar-RL algorithm has been successfully verified for feasibility and effectively meets numerous objectives established during simulations and validation experiments.

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