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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 40 Documents
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Search results for , issue "Vol. 6 No. 4 (2025)" : 40 Documents clear
Design and Evaluation of Secure Software Architectures for 5G-Enabled Vehicular Driving System Saare, Murtaja Ali; Mattar, Ali K.; Sari, Sari Ali; Wong, Seng Yue
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.v6i4.26032

Abstract

Vehicular Ad-hoc Networks (VANETs) represent support for Intelligent Transport Systems (ITS) that allow vehicles and infrastructures to exchange real-time information. Nevertheless, the introduction of the 5G technology for the VANETs poses new security challenges, especially considering the emerging quantum computing threats. In response to this problem, we present a secure software architecture, Lattice Efficient Mutual Authentication (LEMA), designed to improve vehicular communication in 5G supported environments. The research novelty is the construction of LEMA—a lightweight and scalable framework for robust authentication in the fog, based on lattice-based postquantum cryptography, which is also resilient to classical and quantum-based attacks and provides low latency. The framework operates based on three core phases: initialization by a Trusted Authority, secure private key generation, and mutual authentication via LWE-based schemes. A testbed which is built on a Raspberry Pi is used for simulating OBUs to verify LEMA performance in a resource-constrained environment. We compare LEMA with the state of art and get the performance numbers for the computational overhead, communication cost and storage efficiency. Simulation results show that with LEMA, the computational time, the communication amount and the storage consumed can be decreased by at least 25%, 30% and 20% than the benchmark protocols, respectively, and it is secure against the man-in-themiddle and the key-compromise attacks. The authors’ use of fog servers for deployment of the system also significantly boosts real-time responsiveness. Finally, the LEMA model presents a promising quantum-secure authentication technique for 5G-based vehicular networks. In the future we plan to combine it with AIbased anomaly detection and blockchain, for better scalability, privacy and decentralization.
Q-RCR: A Modular Framework for Collision-Free Multi-Package Transfer on Four-Wheeled Omnidirectional Conveyor Systems Kautsar, Syamsiar; Aisjah, Aulia Siti; Arifin, Syamsul; Syai'in, Mat
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.v6i4.26050

Abstract

Modern logistics systems increasingly require high flexibility in handling simultaneous package transfers in compact, dynamic environments without collisions. Improper handling of multi-package transfers in omnidirectional conveyor systems can lead to deadlocks, congestion, or delivery delays, particularly in grid-based environments where routing complexity increases with package variability and layout density. This research addresses these challenges by introducing Q-RCR, a modular Q-Learning-based framework with Rule-Based Conflict Resolution (RCR) for intelligent path planning and collision handling in Four-Wheeled Omnidirectional Cellular Conveyor (FOCC) systems. The research contribution is decoupling path learning and collision handling, enabling independent agent training while minimizing computational burden and improving convergence in multi-agent scenarios. The proposed Q-RCR framework integrates Q-Learning for route optimization with a rule-based conflict resolution module, applying four adaptive strategies: Sequential Transfer, Insert Path, Reroute, and Hybrid. The method is implemented in a grid-based FOCC environment, supporting eight-directional movement and handling various package sizes. Experiments were conducted in four scenarios with grid dimensions ranging from 8×11 to 12×12 and involving up to four simultaneous packages. Results show that Q-RCR consistently outperforms Double Q-Learning, RRT, and A* regarding delivery time, path smoothness, and the number of activated cells. The hybrid mode demonstrated the most effectiveness in handling frequent collisions and maintaining operational flow continuity. The proposed framework demonstrates strong adaptability, scalability, and responsiveness, offering a practical and intelligent solution for real-time multi-package coordination in flexible manufacturing and warehouse automation environments.
Ensemble Voting Regressor for Enhanced Prediction in EMG-Based Prosthetic Wrist Control Karis, Mohd Safirin; Kasdirin, Hyreil Anuar; Abas, Norafizah; Zainudin, Muhammad Noorazlan Shah; Ali, Nursabilillah Mohd; Saad, Wira Hidayat Mohd; Razlan, Zarina
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.v6i4.26222

Abstract

Accurately capturing user motion intention is crucial for effective wrist control in myelectronic prosthetic hands. While various regression models have been explored to improve prediction performance, each presents specific limitations when used independently. This study proposes a novel ensemble learning approach that utilizes a Voting Regressor to combine the strengths of several regression models ANN, ANFIS, fuzzy logic, and their combinations (ANN-ANFIS, ANN-Fuzzy, ANFIS-Fuzzy, and ANN-ANFIS-Fuzzy) to improve predictive performance. Surface EMG signals were collected from the FCR and ECRL muscles at five contraction levels: 20%, 40%, 60%, 80%, and 100% MVC. These signals were used to predict wrist velocity, which was then validated using a SimMechanics based prosthetic hand model in MATLAB 2017a. The ensemble model outperformed all individual and combination models at four MVC levels; 20%, 40%, 60%, and 100%. However, at 80% MVC, a single model achieved superior performance. Based on the average performance gain at the four winning MVC levels, the ensemble method achieved an overall improvement of 11.38%. When applied to the prosthetic hand simulation, the ensemble model showed slight additional improvements in RMSE at each MVC level, highlighting the practical applicability of the approach. To assign optimal and objective weights to the contributing models, MCDM-WSM approach was applied. This method combined multiple evaluation metrics (RMSE, %NRMSE, MAE, R², and p-value) into a single composite score, leading to the final weighted regression equation: YVR-HG-wrist = (0.5163)YANN + (0.2367)YANFIS + (0.2470)YFuzzy. Furthermore, the ensemble model reduced reliance on additional control strategies such as PID tuning, as its improvements in RMSE were comparable to those typically achieved through PID-based compensation. These findings highlight the potential of a performance-weighted ensemble approach to provide more accurate, robust, and practical EMG-based prosthetic wrist control especially in real-time applications.
Unveiling the Predictive Power of Machine Learning and Deep Learning: A Comparative Study on Disease Diagnosis, Detection, and Mortality Risk in Healthcare Santoso, Daniel; Firdaus, Asno Azzawagama; Yunus, Muhajir; Pangri, Muzakkir
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.v6i4.26223

Abstract

This study compares the roles of machine learning (ML) and deep learning (DL) in healthcare, focusing on their applications, challenges, and prospects. It addresses the increasing relevance of AI in public health systems and contributes a structured analysis of how ML and DL process different healthcare data types. A systematic literature review was conducted using sources from Google Scholar, Elsevier, Springer, IEEE, and MDPI, applying inclusion criteria based on relevance, publication quality, and recency (2018–2024). Article selection and synthesis using meta-analysis followed the PRISMA framework. The review identified four key application areas: (1) disease outbreak prediction, (2) disease forecasting, (3) disease diagnosis and detection, and (4) disease hotspot monitoring and mapping. ML techniques such as Random Forest and ensemble methods show high performance in handling structured data like patient records, whereas DL architectures like convolutional neural network (CNN) and long-short term memory (LSTM) are superior for unstructured data, including medical imaging and bio signals. Challenges common to both approaches include data quality issues, dataset bias, privacy concerns, and integration into existing healthcare infrastructures. Looking forward, promising directions include explainable AI (XAI), transfer learning, federated learning, and real-time data use from wearable and internet of things (IoT) devices. The study concludes that while ML and DL can significantly improve diagnosis, response to health threats, and resource allocation, maximizing their impact requires continuous cross-sector collaboration, transparency, and ethical governance.
The Effect of Eye Shape and the Use of Corrective Glasses on the Spatial Accuracy of Eye-Gaze-Based Robot Control with a Static Head Pose Suryadarma, Engelbert Harsandi Erik; Laksono, Pringgo Widyo; Priadythama, Ilham; Herdiman, Lobes; Suhaimi, Muhammad Syaiful Amri Bin
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.v6i4.26229

Abstract

The integration of eye-gaze technology into robotic control systems has shown considerable promise in enhancing human–robot interaction, particularly for individuals with physical disabilities. This study investigates the influence of eye morphology and the use of corrective eyewear on the spatial accuracy of gaze-based robot control under static head pose conditions. Experiments were conducted using advanced eye-tracking systems and multiple machine learning algorithms—decision tree, support vector machine, discriminant analysis, naïve bayes, and K-nearest neighbor—on a participant pool with varied eye shapes and eyewear usage. The experimental design accounted for potential sources of bias, including lighting variability, participant fatigue, and calibration procedures. Statistical analyses revealed no significant differences in gaze estimation accuracy across eye shapes or eyewear status. However, a consistent pattern emerged: participants with non-monolid eye shapes achieved, on average, approximately 1% higher accuracy than those with monolid eye shapes—a difference that, while statistically insignificant, warrants further exploration. The findings suggest that gaze-based robotic control systems can operate reliably across diverse user groups and hold strong potential for use in assistive technologies targeting individuals with limited mobility, including those with severe motor impairments such as head paralysis. To further enhance the inclusiveness and robustness of such systems, future research should explore additional anatomical variations and environmental conditions that may influence gaze estimation accuracy.
Adaptive Strategies for Dynamic Obstacle Avoidance and Formation Control in Multi-Agent Drone Systems: A Review Argiliana, Shania; Ekawati, Estiyanti; Mukhlish, Faqihza
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.v6i4.26243

Abstract

Obstacle avoidance in multi-agent systems is a critical area of research driven by advancements in autonomous technology and artificial intelligence. This review examines various approaches to path planning, formation control, and communication architectures, focusing on their effectiveness in static and dynamic environments. The research contribution is a comprehensive analysis of current techniques based on a structured selection process evaluating peer-reviewed studies through computational efficiency, real-time adaptivity, and scalability. The findings highlight the strengths and limitations of classical methods, such as the Improved Artificial Potential Field (IAPF), and modern techniques like Reinforcement Learning (RL) and Model Predictive Control (MPC). Comparative analysis reveals that while these approaches improve adaptivity, they also introduce challenges such as high computational loads, difficulties in large-scale multi-agent coordination, and sensitivity of parameter tuning. Additionally, existing formation control strategies depend highly on stable inter-agent communication, making them vulnerable to delays and failures in decentralized networks. This review identifies key research gaps and suggests future directions, including hybrid RL-MPC formation control, adaptive path planning algorithms, and scalable communication protocols to enhance multi-agent system performance in real-world applications.
AI-Enhanced High-Speed Data Encryption System for Unmanned Aerial Vehicles in Fire Detection Applications Moldamurat, Khuralay; Spada, Luigi La; Zeeshan, Nida; Bakyt, Makhabbat; Kuanysh, Absalyam; Zhanibek, Kazybek bi; Tilenbayev, Alzhan
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.v6i4.26275

Abstract

Small unmanned aerial vehicles (UAVs) are increasingly used for wildfire detection, where they must not only identify fire events rapidly but also transmit large volumes of sensor data securely to ground stations. Achieving both fast on-board analysis and high-speed encrypted data transmission within the size, weight, and power limits of UAV platforms remain a major technical challenge. In this study, we introduce a compact, FPGA-based system that simultaneously performs real-time fire detection and high-throughput data encryption. Our system integrates a programmable logic chip (FPGA), deep-learning models for visual recognition, and AES-256 cryptographic cores onto a single hardware module. A key innovation is a shared scheduling mechanism that coordinates these two functions efficiently. Furthermore, we demonstrate how artificial intelligence contributes beyond image classification: a lightweight neural network monitors input data streams and dynamically adjusts encryption key parameters, thereby improving security without compromising performance. The hardware supports encrypted data transfer rates of 800 megabits per second at a latency of just 2 microseconds, while identifying fire signatures at 30 frames per second. Extensive testing, including cross-validation on a 50,000-frame dataset and environmental stress testing from –20 °C to 55 °C, confirms robust performance under real-world conditions. While the current memory footprint limits multi-camera input, this work offers a foundational design for future systems that aim to combine edge computing, secure communications, and AI-driven perception in autonomous aerial platforms.
Artificial Intelligence-Driven and Secure 5G-VANET Architectures for Future Transportation Systems Saare, Murtaja Ali; Abdulhamed, Mohamed Abdulrahman; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; Shehab, Rami
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.v6i4.26295

Abstract

The advent of 5G has opened a new era of intelligent, adaptive and secure VANETs that is envisaged to serve as the backbone network architecture for next generation of vehicular transportation systems. In this work, we present a connected 5G VANETs-to-Edge Computing systems with Artificial Intelligence (AI) infrastructure to improve system adaptability, anomaly detection, trust management, and real-time decisionmaking. Crucial enabling technologies like Software-Defined Networking (SDN). Mobile Edge Computing (MEC), and millimeterwave communication are investigated in detail. We examine key security threats such as identity forgery, data interception, and denial-of-service attacks, and assess the AI-enhanced defense measures such as intrusion detection systems and blockchainbased trust models. Applications, like autonomous platooning, and collaborative vehicle authentication provide additional examples of AI technologies’ added value in the context of vehicular communications and safety. The paper concludes by providing open issues and future directions, including quantum-resistant protocols, lightweight AI models and cognitive networking in the context AI-driven 5G-VANET ecosystems.
Robust Velocity Control for a Launch Vehicle Erection System Saber, Ahmed K.; Maged, Shady A.; Abdelaziz, M.; Mohamed, Mostafa S.
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.v6i4.26385

Abstract

The design of a launch vehicle erection system requires careful consideration of factors such as load capacity, pressure requirements, actuator type, safety mechanisms, and control strategy. Ensuring precise velocity control is critical, as the system’s changing geometry and dynamic behavior influence its loading conditions, stability, and overall performance. This study investigates the velocity control of a hydraulic erection beam using a proportional directional control valve (PDCV). Four control techniques are examined: a classical PID controller, a sliding mode controller (SMC), a model predictive controller (MPC), and a PID controller optimized using the Particle Swarm Optimization (PSO) method. The controllers are evaluated through MATLAB/SIMULINK simulations under both undisturbed and disturbed conditions. Simulation results indicate that the classical PID controller struggles with stability under disturbances, while the MPC exhibits slow response times and fails to reach the desired position. The integration of PSO further degrades performance by introducing instability. In contrast, the SMC demonstrates superior robustness, achieving minimal response variation across all conditions. Comparative experiments validate these findings, confirming that SMC offers the best balance of precision, reliability, and disturbance rejection. These results highlight that SMC is the most effective control technique for real-world hydraulic erection systems, ensuring high stability, accuracy, and operational reliability.
Non-Intrusive Real-Time Tourist Crowd Monitoring for Overtourism Mitigation using YOLOv8-Based Head Detection and Tracking Wijayanti, Kurnia; Mutiara, Giva Andriana; Suryawardani, Bethani; Ervina, Ersy; Kusuma, Guntur Prabawa
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.v6i4.26396

Abstract

Overtourism has emerged as a critical issue in popular tourist destinations, often leading to environmental strain, reduced visitor satisfaction, and safety concerns. Traditional methods such as ticket counts, or vehicle estimation fail to provide real-time insights or adapt effectively to dynamic outdoor environments. This study proposes a privacy-aware, real-time visitor capacity monitoring system for smart tourism, utilizing YOLOv8-based head detection and Centroid Tracking to ensure accurate, non-intrusive people counting in dense and complex crowd scenarios. Head detection is employed specifically to preserve personal privacy without compromising on detection performance. The system was trained on a custom dataset comprising over 3,000 annotated frames with diverse lighting conditions, occlusion levels, and viewing angles. Deployment at Wana Wisata Kawah Putih, an open-air tourist destination in Indonesia, demonstrated strong performance with 94.2% accuracy, 95.1% precision, and 90.6% recall, while sustaining >60 FPS for real-time execution. The integration of Centroid Tracking enables lightweight, frame-to-frame identity association with minimal computational overhead, making the system suitable for deployment on moderate-performance hardware. Despite its robustness, the system's performance slightly degrades under extreme weather (e.g., fog, direct glare) and rapid lighting transitions, which remain challenges for visual models. Moreover, the current model requires further evaluation for cross-location generalizability. Future research will explore the integration of predictive analytics for visitor flow forecasting, and further optimization of energy efficiency and adaptive detection under environmental uncertainty. This work contributes a scalable, ethical solution for real-time crowd monitoring to support informed, sustainable tourism management.

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