Journal of Robotics and Control (JRC)
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.
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<![CDATA[ROS-based Multi-Robot System for Efficient Indoor Exploration Using a Combined Path Planning Technique ]]>
<![CDATA[Sandanika, Wanni Arachchige Heshani]]>;
<![CDATA[Wishvajith, Supun Hansaka]]>;
<![CDATA[Randika, Sahan]]>;
<![CDATA[Thennakoon, Deshitha Adeeshan]]>;
<![CDATA[Rajapaksha, Samantha Kumara]]>;
<![CDATA[Jayasinghearachchi, Vishan]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.22494
<![CDATA[This study introduces an innovative combined system utilizing the Robot Operating System (ROS) to enhance multi-robot systems for comprehensive coverage in indoor settings. The research emphasizes integrating diverse robotics technologies, such as map partitioning, path planning, and adaptive task allocation, to boost deployment and coordination for localization and navigation. The system uses occupancy grid maps for effective map partitioning and employs a market-based algorithm for adaptive task distribution. A hybrid path planning approach, merging Boustrophedon Traversing Coverage (BTC) and Spiral Traversing Coverage (STC), ensures complete area coverage while reducing redundancy. During thorough testing, our system showed coverage efficiencies between 94% and 98% in different layouts and conditions, with task completion rates as high as 19.6% per minute, highlighting its ability to effectively handle and adjust to various indoor environments. Additionally, dynamic robot deployment in response to environmental changes has led to enhanced operational efficiency and flexibility. The initial results are promising, though future research will focus on incorporating dynamic obstacle management and path planning to boost the system's robustness and adaptability. This study paves the way for further exploration and development of advanced path-planning algorithms to enhance the performance and usability of multi-robot systems in dynamic environment applications.]]>
<![CDATA[Three-Dimensional Coordination Control of Multi-UAV for Partially Observable Multi-Target Tracking ]]>
<![CDATA[Maynad, Vincentius Charles]]>;
<![CDATA[Nugraha, Yurid Eka]]>;
<![CDATA[Alkaff, Abdullah]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.22560
<![CDATA[This research deals with multi-UAV systems to track partially observable multi-targets in noisy three-dimensional environments, which are commonly encountered in defense and surveillance systems. It is a far extension from previous research which focused mainly on two-dimensional, fully observable, and/or perfect measurement settings. The targets are modeled as linear time-invariant systems with Gaussian noise and the pursuers UAV are represented in a standard six-degree-of-freedom model. Necessary equations to describe the relationship between observations regarding the target and the pursuers states are derived and represented as the Gauss-Markov model. Partially observable targets require the pursuers to maintain belief values for target positions. In the presence of a noisy environment, an extended Kalman filter is used to estimate and update those beliefs. A Decentralized Multi-Agent Reinforcement Learning (MARL) algorithm known as soft Double Q-Learning is proposed to learn the coordination control among the pursuers. The algorithm is enriched with an entropy regulation to train a certain stochastic policy and enable interactions among pursuers to foster cooperative behavior. The enrichment encourages the algorithm to explore wider and unknown search areas which is important for multi-target tracking systems. The algorithm was trained before it was deployed to complete several scenarios. The experiments using various sensor capabilities showed that the proposed algorithm had higher success rates compared to the baseline algorithm. A description of the many distinctions between two-dimensional and three-dimensional settings is also provided.]]>
<![CDATA[Enhanced Transformer Protection Using Fuzzy-Logic-Integrated Differential Relays: A Comparative Study with Rule-based Methods ]]>
<![CDATA[Hussein, Raad Ibrahim Hussein]]>;
<![CDATA[Gökşenli, Nurettin]]>;
<![CDATA[Bektaş, Enes]]>;
<![CDATA[Teke, Mustafa]]>;
<![CDATA[Tümay, Mehmet]]>;
<![CDATA[Yaseen, Ethar Sulaiman Yaseen]]>;
<![CDATA[Bektaş, Yasin]]>;
<![CDATA[Taha, Taha A.]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.21937
<![CDATA[The power transformers are the important part of electrical networks where transformer reliability and operational lifetime depends on sufficiently accurate and reliable protective means. Other traditional forms of differential protection that were developed initially also suffer from the inability to distinguish between a fault and normal operation such as inrush currents in transformers and CT saturation. This paper presents the development of an improved differential relay augmented by Fuzzy-Logic Control System (FLC), to improve (a) dependability, (b) performance of the existing transformer protection systems, and (c) accuracy in fault identification possible due to uncertainty and non-linearity in transformer operation. They include the proposed methodology compared to the traditional Rule-based current differential method in outlining the protection settings. MATLAB/Simulink model of the power transformer and protection methods suggested in the study form a part of the investigation. Computer simulations show that the presented scheme provides a substantial increase in the speed and resolution of fault detection and fault types identification relating to current differential method based on the Rule. The system’s accuracy rate is the average of 98% for internal faults and 95% for external faults while its response time is 25ms for internal faults and 30ms for external faults. Furthermore, the Fuzzy-Logic-based system has an 90% efficiency in detect the defect and 85% efficiency in identify the inrush currents. The findings of this research prove that the differential relay based on Fuzzy-Logic enhances the flexibility and reliability of transformer protection and opens the road to the introduction of further improvements in the intelligent protection systems in the future.]]>
<![CDATA[Vicinity Monitoring of Military Vehicle Cabin to Improve Passenger Comfort with Fusion Sensors and LoRa RFM95W ]]>
<![CDATA[Fadillah, Wildan Muhammad Yasin]]>;
<![CDATA[Mutiara, Giva Andriana]]>;
<![CDATA[Periyadi, Periyadi]]>;
<![CDATA[Alfarisi, Muhammad Rizqy]]>;
<![CDATA[Meisaroh, Lisda]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.21600
<![CDATA[The application and utilization of technology to measure the level of comfort in mass-produced vehicles, including military vehicles, is constantly evolving. Currently, the testing of comfort parameters is carried out manually through human-driven test drives. Thus, the range of variability in measurements is extensive as it depends on the subjective experiential indications of experts. This research utilizes KY-037 sensor to measure noise level and BME280 sensor fusion to detect temperature, air pressure, humidity, and altitude. These parameters have a significant impact on passenger comfort inside the passenger cabin of military vehicles. This project included involves the development of LoRa-based communication medium using RFM95W technology. The system has extensive performance testing inside the passenger cabin of a military vehicle on various test area tracks. The test results indicate that the system is capable of accurately reading the KY-037 sensor, with a range of 80 - 141 dB depending on the tracks. The BME280 sensor consistently measures a temperature of 36,98°C, altitude readings ranging from 667-677 meter above sea level, maintaining a stable air pressure of 955.35 hPa, and measuring the lowest humidity level in the vehicle cabin at 24.34%. The LoRa technology possesses remarkable to extend the communication range, even in challenging environments, reaching distances over 2 kilometers. The response time for data sent in web-based applications consistently remains below 1 second. Thus, this system can assist experts in enhancing cabin passenger comfort standards by narrowing the range and making it more measurable.]]>
<![CDATA[Cancer Treatment Precision Strategies Through Optimal Control Theory ]]>
<![CDATA[Abougarair, Ahmed J.]]>;
<![CDATA[Oun, Abdulhamid A.]]>;
<![CDATA[Sawan, Salah I.]]>;
<![CDATA[Abougard, T.]]>;
<![CDATA[Maghfiroh, H.]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.22378
<![CDATA[Lung cancer is a highly heterogeneous disease, with diverse genetic, molecular, and cellular drivers that can vary significantly between individual patients and even within a single tumor. Though combination therapy is becoming more common in the treatment of cancer, it can be challenging to predict how various treatment modalities will interact and what negative effects they may have on a patient's health, such as increased gastrointestinal toxicities, or neurological problems. This paper aims to regulate immunity to tumor therapy by utilizing optimal control theory (OCT). This research suggests a malignant tumor model that can be regulated with a combination of immunological, vaccine, and chemotherapeutic therapy. The optimal control variables are employed to support the best possible treatment plan with the fewest potential side effects by reducing the production of new tumor cells and keeping the number of normal cells above the average carrying capacity. Also, the study addresses patient heterogeneity, individual variations in tumor biology, and immune responses for both young and old cancer patients. Finding the right doses for a treatment that works is the main goal. To do this, we conducted a comparative analysis of two optimum control approaches: the Single Network Adaptive Critic (SNAC) approach, which directly applies the notion of reinforcement learning to the essential conditions for optimality and the Linear Quadratic Regulator (LQR) methodology. Although the study's results show the promise of precision treatment plans, a number of significant obstacles must be overcome before these tactics can be successfully applied in clinical settings. It will be necessary to make considerable adjustments to the healthcare system's infrastructure in order to successfully offer personalized treatment regimens. This includes enhanced interdisciplinary care coordination methods, safe data management systems.]]>
<![CDATA[The Role of Occasional Assessment of Sensor Performance for Improved Subsea Search Efficiency ]]>
<![CDATA[Yetkin, Harun]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.22298
<![CDATA[This study addresses the subsea search performance of an autonomous underwater vehicle equipped with a search sensor and an environment characterization sensor. The performance of the search sensor is assumed to be dependent on characteristics of the local environment, and thus sensor performance in some locations can be different than in other locations. For the case that the agent is able to occasionally characterize the environment, and therefore estimate the performance of its search sensor, we describe a method for selecting when and where to characterize the environment and when and where to search in order to maximize overall search effectiveness. Our work accounts for false positives, false negatives and uncertainty in the performance of the search sensor that varies geographically. We show that effort applied to characterizing the environment, and therefore the performance of the search sensor, can improve search performance. We derive a utility function that is used to compute the best path and when to switch between the tasks of search and environmental characterization. The objective of the subsea search mission is to maximize the probability of attaining a desired level of risk reduction, and we terminate the search mission as soon as it is found that the desired risk reduction cannot be attained. To the best of our knowledge, this is the first study that addresses the problem of attaining a desired level of risk and stopping the mission when the desired risk is found to be unachievable. Through numerical illustrations, we show realistic scenarios where the findings of this study can be useful to improve search effectiveness and attain the desired level of risk where the standard exhaustive search techniques will fail to achieve.]]>
<![CDATA[LICA-CS: Efficient Lossless Image Compression Algorithm via Column Subtraction Model ]]>
<![CDATA[Al Qerom, Mahmoud]]>;
<![CDATA[Otair, Mohammad]]>;
<![CDATA[Meziane, Farid]]>;
<![CDATA[AbdulRahman, Sawsan]]>;
<![CDATA[Alzubi, Maen]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.21834
<![CDATA[Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICA-CS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies.]]>
<![CDATA[Techno-Economic and Environmental Analysis of an On-Grid and Off-Grid Renewable Energy Hybrid System in an Energy-Rich Rural Area: A Case in Indonesia ]]>
<![CDATA[Umam, Faikul]]>;
<![CDATA[Wahyu, Fiki Milatul]]>;
<![CDATA[Efendi, Mochamad Yusuf]]>;
<![CDATA[Amir, Nizar]]>;
<![CDATA[Gozan, Misri]]>;
<![CDATA[Asmara, Yuli Panca]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.22633
<![CDATA[Developing a dedicated renewable energy hybrid system is a viable option for extending access to electrical energy in energy-rich rural areas. This study conducted a feasibility analysis of using a hybrid energy system, combining solar photovoltaic, wind, and biogas, to generate electricity and meet the energy needs of the rural area. West Waru Village is selected as the case study area for this research because it has abundant renewable energy sources. The Hybrid Optimization of Multiple Energy Resources (HOMER) tools is employed for modeling and optimizing the hybrid energy system, offering a comprehensive analysis encompassing technical, economic, and environmental aspects. Furthermore, the study's findings were further analyzed through a sensitivity analysis, considering unpredictable factors such as village load consumption, solar radiation, wind speed, and biomass availability. Additionally, the study’s results reveals that the renewable energy hybrid system can meet nearly 80% of the rural area's electrical energy requirements at a cost of $0.16 per kWh, resulting in the reduction of 8.4 million kg of carbon dioxide emissions. These findings can serve as a baseline for stakeholders in developing renewable energy systems in rural areas.]]>
<![CDATA[Robust DeepFake Face Detection Leveraging Xception Model and Novel Snake Optimization Technique ]]>
<![CDATA[Al-Qazzaz, Ahmed SAAD]]>;
<![CDATA[Salehpour, Pedram]]>;
<![CDATA[Aghdasi, Hadi S.]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.22473
<![CDATA[DeepFake technology has created an existential crisis around authenticity in digital media with the ability to create nearly imperceptible forgeries on a massive scale, such as impersonating public figures for nefarious reasons like misinformation campaigns, harassment, and fraud. In this thesis, a model Xception is combined with the Snake optimization technique to ensure efficient and accurate detection of ADOR in practice. The former is deep CNN architecture Xception which exploits depthwise separable convolutions to perform efficient feature extraction, and the latter is a novel snake optimization that borrows lessons from real-life predatory snakes to dynamically adapt parameters for better exploration of search space while avoiding local optima. The combined modality is systematically evaluated using multiple challenging DeepFake video datasets and shows significant improvement. A comparison of performance with other methods showed that a mean accuracy, precision, recall, and F1-score was 98.53% for the Snake-optimized Xception model while outperformed some state-of-the-art approaches and traditional Xception itself. This helps in reducing missing of misdetection and reduction of false positives, helping achieve a tool that is highly effective for digital media forensics. Such discoveries open the door for this method to unlock new levels of digital content integrity, necessary in media verification and legal evidence authentication, as well as assist individuals dealing with fake news or videos attempting identity theft online. This research highlights the strong efficacy of coupling the Xception model with Snake optimization for DeepFake detection; thus, establishes a new state-of-the-art and will inspire future studies and applications to protect genuineness in digital media.]]>
<![CDATA[Design and Simulation of an Analog Robust Control for a Realistic Buck Converter Model ]]>
<![CDATA[Mohammed, Ibrahim Khalaf]]>;
<![CDATA[Khalaf, Laith Abduljabbar]]>
<![CDATA[ Journal of Robotics and Control (JRC) Vol 5, No 5 (2024) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/jrc.v5i5.22408
<![CDATA[The simplicity and cost of the control systems used in power converters are an urgent aspect. In this research, a simple and low cost voltage regulation system for a Buck converter system operating in uncertain conditions is provided. Using an electronic PID controller technique, the feedback control scheme of the presented Buck converter is carried out. Matlab software used a simulation environment for the proposed analog PID-based Buck converter scheme. The PID controller is easily implementable since it is built with basic and conventional electronic components like a resistor, capacitor and op-amp. The system simulation has high reliability as it is implemented using the Simscape package. The Simscape components used to build the converter system are modeled effectively taking into consideration including the practical factors such as internal resistance, tolerance and parasitic elements. This procedure certainly enhances the reliability of the simulation findings as the working conditions of the simulated system become more closer to the real-world conditions. Particle Swarm Optimization (PSO) is employed to properly optimize tune the PID gains. The regulation process of the PID control scheme is assessed under voltage and load disturbances in order to explore the robustness of the Buck converter performance. The findings from the system simulation, under the uncertainties, show largest rise time and settling time of 20 ms and 25 ms respectively, zero overshoot and minimum steady state error response, except at load disturbance case there is a fluctuation of 1 V. Consequently, It can be said that the proposed Buck converter based on analog PID controller can be used efficiently in the industrial and power applications.]]>
