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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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Hybrid SVD and SURF-Based Framework for Robust Image Forgery Detection and Object Localization Najjar, Fallah H.; AbdulAmeer, Ansam Ali; Kadum, Salman
Journal of Robotics and Control (JRC) Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i2.25567

Abstract

This paper presents a highly effective and reliable approach for detecting image forgery and identifying manipulated regions in digital images. The proposed method uses a combination of Singular Value Decomposition (SVD) and the Speeded-Up Robust Features (SURF) algorithm, achieving a high degree accuracy of 99.1% for revealed tampering. After an input image is initially divided parallel to partition, then is performed by SVD to extract features with remarkable discriminability, the method is valued based on independent experiments. The norms are calculated, and pixels with the same norm begin to group to identify potentially tampered areas. In order to simplify the detection process, we conduct a weighted comparison among subgroups to distinguish real structures from false ones. Once we discover a suspicious forgery area, the SURF algorithm comes into play to accurately identify the manipulated items. This process uses a keypoint detector, descriptor calculations, the match between points, and geometric checking to improve the accuracy and reliability of forgery localization. Experimental results on different image databases show that this method is effective. It exhibits advanced ability in detecting forgeries, finding objects and locating where they are in an image. Eventually, we hope this work will produce a sturdy forgery detection system and improve the accuracy of recognizing tampered regions. The proposed method is useful in digital forensics and image verification.
Optimizing Mobile Robot Path Planning with a Hybrid Crocodile Hunting and Falcon Optimization Algorithm Hashim, Wassan Adnan; Ahmed, Saadaldeen Rashid; Mahmood, Mohammed Thakir; Almaiah, Mohammed Amin; Shehab, Rami; AlAli, Rommel
Journal of Robotics and Control (JRC) Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i2.25586

Abstract

Thorough path planning is critical in unmanned ground vehicle control to reduce path length, computational time, and the number of collisions. This paper aims to introduce a new metaheuristic method called the Hybrid Crocodile Hunting-SearcH and Falcon Optimization (CHS-FO) algorithm. This method combines CHS's exploration and exploitation abilities with FO's rapid convergence rate. In this way, the use of both metaheuristic techniques limits the disadvantage of the individual approach, guaranteeing a high level of both global and local search. We conduct several simulations to compare the performance of the CHS-FO algorithm with conventional algorithms such as A* and Genetic Algorithms (GA). It is found The results show that the CHS-FO algorithm performs 30–50% better in terms of computation time, involves shorter path planning, and improves obstacle avoidance. Eristic also suggests that the path generation algorithm can adapt to environmental constraints and be used in real-world scenarios, such as automating product movement in a warehouse or conducting search and rescue operations for lost vehicles. The primary The proposed CHS-FO architecture makes the robot more independent and better at making choices, which makes it a good choice for developing the next generation of mobile robotic platforms. Goals will encompass the improvement of the algorithm's scalability for use in multiple robots, as well as the integration of the algorithm in a real environment in real time.
Design of a Robust Component-wise Sliding Mode Controller for a Two-Link Manipulator Qasim, Mohammed; Abdulla, Abdulla Ibrahim; Ayoub, Abdurahman Basil
Journal of Robotics and Control (JRC) Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i2.25632

Abstract

Compared to conventional Multiple-Input Multiple-Output (MIMO) Sliding Mode Control (SMC) techniques, the component-wise SMC approach offers several advantages, including improved decoupling of system dynamics, enhanced robustness, and greater flexibility in controller design. This paper proposes a novel trajectory tracking controller for a two-link manipulator based on the component-wise sliding mode control approach. The design methodology involves determining controller gains by solving a set of inequalities. This analysis results in conditions on the system parameter uncertainties that guarantee the existence of a feasible solution to the set of inequalities. Furthermore, an algorithm is presented to determine the maximum allowable uncertainties that ensure the feasibility of the controller gains. To evaluate the performance and robustness of the proposed tracking controller, the manipulator is subjected to a series of challenging trajectories, including circular and figure-8 ones, under both nominal and maximum allowable uncertainty conditions. The proposed controller demonstrates superior performance across both circular and figure-8 trajectories, exhibiting excellent transient response and minimal steady-state error even under the maximum permissible uncertainties, which extend up to 27% in link masses. This performance is validated through a quantitative analysis that incorporates a comparative evaluation against two conventional MIMO SMC techniques. The comparison is conducted using the Integral Norm of Error (INE) to assess tracking accuracy and the Integral Norm of Control Action (INU) to evaluate the energy efficiency of the controllers. These metrics provide a comprehensive basis for analyzing both the precision and the energy consumption of the proposed control strategy in relation to established methods.
An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization Suwoyo, Heru; Adriansyah, Andi; Andika, Julpri; Shamsudin, Abu Ubaidah; Tian, Yingzhong
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.22062

Abstract

Conceptually, Fast-RRT applies fast sampling and random steering which makes the initial path quickly obtained. Referring to the initial path, the optimality of the path is improved by applying path fusion and path optimization. Theoretically, path fusion will only be optimal if there is always a unique/different path to be fused with the previously obtained path. However, in the conditions of solving path planning problems in narrow corridors, the potential for obtaining a different path from the previous one is very small. So that fusion does not run properly, but checking the relationship between nodes to nodes still occurs. Instead of getting an optimal path in conditions like this, the computation will increase, the solution time will be long, and the resulting path will still be sub-optimal. As an effort to solve this problem, Grey Wolf Optimization (GWO) is involved through this study. While an initial path is found, the beacons are repositioned. From the path, the number of nodes is unpredictable, causing the decision variables in optimization to become large. For this reason, the GWO is chosen because it is independent of population representation and is not affected by the number of decision variables. This proposed method is claimed to be more effective in solving path planning problems in terms of convergence rate and optimality. Therefore, the proposed method is evaluated and compared with previous methods and gives the result that the average working speed of Fast-RRT is improved by 90.25% and the optimality average increased by 5.67%.
Advancements in Artificial Intelligence Techniques for Diabetes Prediction: A Comprehensive Literature Review Hameed, Emad Majeed; Joshi, Hardik; Kadhim, Qusay Kanaan
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.22258

Abstract

Diabetes mellitus (DM) is a chronic condition requiring lifelong management due to inadequate insulin secretion or inefficacy of insulin. Its global prevalence has led to extensive research focusing on diagnosis, prevention, and treatment. The developments in artificial intelligence (AI) have improved diabetes management and prediction. This paper provides a comprehensive review of the contributions of machine learning (ML) algorithms in predicting and classifying diabetes. The review examines research on artificial intelligence techniques used to predict diabetes over the past six years, intending to identify the latest innovations and trends in this field. This time frame reflects recent methodological advances and new applications that exemplify the current state of artificial intelligence in diabetes prediction. It covers dataset selection, preprocessing, AI algorithms application, and evaluation methodologies. The results of this review show that the most predominant methods used in diabetes prediction are Random Forest, Logistic Regression, Decision Trees, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbors, each with distinct advantages and limitations. The review also shows through its examination that the highest accuracy provided by the hybrid approach was 99.4%, the ensemble approach (ada boost) was 98.8%, deep learning (DNN) was 98.04%, and traditional machine learning (decision tree_ ID3) was 99%. Most studies conducted for diabetes prediction trained the models on specific datasets, which makes their generalizability to diverse populations and healthcare settings limited. The future directions must address ensuring the robustness and generalizability of predictive models through comprehensive external validation across various populations, settings, and geographic areas.
Two-Level Feature Selection for Enhanced Accuracy and Reduced Computational Overhead in Intrusion Detection Systems Using Rough Set Theory and Binary Particle Swarm Optimization Almania, Moaad; Zainal, Anazida; Ghaleb, Fuad A; Alnawasrah, Ahmad; Al Qerom, Mahmoud
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Intrusion Detection Systems (IDS) are essential for safeguarding network infrastructures by detecting and mitigating malicious activities. This study introduces a two-level feature selection approach (TLFSA) designed to enhance classification accuracy and reduce computational overhead. The first phase employs Rough Set Theory (RST) to filter out irrelevant features, while the second phase uses Binary Particle Swarm Optimization (BPSO) to refine the feature subset based on their discriminative power. Experiments conducted on the NSL-KDD dataset show that the TLFSA approach outperforms traditional algorithms such as Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA), achieving a notable improvement of 0.99% in classification accuracy. Furthermore, class-specific feature subsets produced by the method demonstrate superior detection rates across all network traffic classes, with an average accuracy of 97.22%, compared to 91.11% for alternative methods. The proposed method effectively reduces the feature set to approximately 15% of the original features, streamlining the IDS model and improving both operational efficiency and real-time applicability.
Autonomous Nutrient Controller System for Hydroponic Honey Melon Based on the Integration of Artificial Intelligence Algorithms According to Planting Time Herdiana, Budi; Utama, Jana; Sutono, Sutono; Adhari, Febryan Rizky; Henrikus, Yansen
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The honeydew melon cultivation model using the hydroponic greenhouse method has been widely applied due to its ease in controlling nutrients and the environment. However, complaints from farmers regarding the inaccuracy of nutrient levels and the dynamic environmental changes, that hinder plant growth and fruit quality, have surfaced. The development of autonomous control technology is crucial as a strategic solution to this issue since the quality of honeydew melon management lies in achieving precise and accurate nutrient levels. On the other hand, managing standardized nutrient composition often becomes a challenge for farmers as the needs constantly change over time. Conventional systems are not yet capable of accurately measuring nutrient levels in line with the plant’s growth stages. According to the objectives of this study, which is to improve the productivity and quality of honeydew melons based on the increase in the sweetness index, the development of an autonomous nutrient control system is proposed. This system integrates artificial intelligence algorithms, namely CNN and Fuzzy Logic, to process plant height image data and multisensor data for system control processes. The research findings that applying this integrated technique has resulted in a sweetness increase of 11.7%, or from the previous value of 15 brix to 17 brix. Even a one-point increase in the brix value leads to a sugar increase of 1 gram per 100 gram of liquid content in the fruit, contributing significantly to the market value. These results indicate that AI-supported agricultural management can be realized in future modern farming practices.
Enhanced Precision Control of a 4-DOF Robotic Arm Using Numerical Code Recognition for Automated Object Handling Sukri, Hanifudin; Ibadillah, Achmad Fiqhi; Thinakaran, Rajermani; Umam, Faikul; Dafid, Ach.; Kurniawan, Adi; Morshed, Md. Monzur; Kurniawan, Denni
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research develops a 4-DOF robotic arm system that utilizes numerical codes for accurate, automated object handling, supporting advancements in sustainable industrial automation aligned with the UN Sustainable Development Goals (SDGs), particularly Industry, Innovation, and Infrastructure (SDG 9). Key contributions include the integration of EasyOCR for reliable code recognition and a control mechanism that enables precise positioning. The robotic system combines a webcam for visual sensing, servo motors for movement, and a gripper for object manipulation. EasyOCR effectively recognizes numerical codes on randomly positioned objects against a uniform background while the microcontroller calculates servo angles to guide the arm accurately to target positions. Testing results show a success rate exceeding 94% for detecting codes 1 to 4, with minor servo angle errors requiring adjustments in arm extension by 30 mm to 50 mm. Positional error analysis reveals an average error of less than 1.5 degrees. Although environmental factors like lighting can influence code visibility, this approach outperforms traditional methods in adaptability and precision. Future research will focus on enhancing code recognition under variable lighting and expanding the system's adaptability for diverse object types, broadening its applications in industries demanding high efficiency.
Analysis of Design Considerations for a 6 DoF Mobile Manipulator Based on Manipulability Measure Mathew, Shyju Susan; R, Jisha V
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.24411

Abstract

Mobile manipulators are highly versatile and are used across various fields due to their flexibility, reach, and adaptability. Hence it finds applications that involve complex environments or require high precision. The mobile manipulation tasks require the manipulators to retain good manipulation capability, which calls for reasonable motion planning. Manipulability, a crucial metric indicating the robot’s ability to perform effective and efficient manipulation tasks, serves as the central criterion for the design of redundant mobile manipulators (MM). In addition to this, for applications where the mobile base and manipulator are moving simultaneously, a design configuration with good manipulability measure is preferred. This study fills a significant gap in the literature by offering an analysis of the design considerations for a redundant MM for improved manipulability measure. In this paper, the end effector of a 6 DoF MM is made to move through a predefined trajectory, and the manipulability measure and manipulability ellipsoid are computed at various points in the workspace. The analysis is done based on various link length ratios, mounting positions of the arm, and mobile base speeds. The manipulability ellipsoids at various locations in the task space were analyzed which is indicative of maximum and minimum velocities achievable by the end effector. Based on the analysis, the best configuration is identified and a kinematic controller is designed for this configuration which traces the reference trajectory with high manipulability. An exhaustive simulation study shows the benefits of the suggested design principles and control techniques, reaffirming the significance of optimized link lengths, mounting positions, and mobile base speeds in enhancing manipulability. Although this study is carried out in a 6 DoF MM, the novelty of this research lies in its emphasis on enabling design of redundant MM for better manipulability which lays a strong foundation for future applications.
A Customized Temporal Federated Learning Through Adversarial Networks for Cyber Attack Detection in IoT Vemulapalli, Lavanya; Sekhar, P. Chandra
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.24529

Abstract

The exponential growth of the Internet of Things (IoT) has heightened the need for secure, privacy-preserving, and efficient cyber-attack detection mechanisms. This study introduces the Customized Temporal Federated Learning through Adversarial Networks (CusTFL-AN) framework, which combines Temporal Convolutional Networks (TCNs) and Generative Adversarial Networks (GANs) for robust and personalized attack detection. CusTFL-AN enables clients to train local models while maintaining data privacy by generating synthetic datasets using GANs and aggregating these at a central server, thereby mitigating risks associated with direct data sharing. The framework's effectiveness is demonstrated on three benchmark datasets—UNSW-NB15, BoT-IoT, and Edge-IIoT—achieving detection accuracies of 99.2%, 99.5%, and 99.25%, respectively, significantly outperforming state-of-the-art methods. Key enhancements include addressing data heterogeneity through federated aggregation, minimizing overfitting using GAN validation and cross-validation techniques, and ensuring interpretability to support practical adoption in real-world IoT scenarios. Privacy mechanisms are strengthened to prevent potential data leakage during aggregation, and ethical considerations surrounding the use of synthetic datasets are acknowledged. Furthermore, the impact of computational constraints, network latency, and communication overhead on resource-constrained IoT devices has been carefully analyzed. While the results affirm the robustness and scalability of CusTFL-AN, future work will focus on extending evaluations to more diverse datasets and addressing the challenges of adversarial attacks. CusTFL-AN represents a significant step forward in privacy-preserving federated learning, offering practical solutions to real-world IoT cybersecurity challenges.

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