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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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Optimizing Latent Space Representation for Tourism Insights: A Metaheuristic Approach Win, Thinzar Aung; Sunat, Khamron
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.21419

Abstract

In the modern digital era, social media platforms with travel reviews significantly influence the tourism industry by providing a wealth of information on consumer preferences and behaviors. However, these textual reviews' complex and varied nature poses analytical challenges. This research employs advanced Natural Language Processing (NLP) techniques to process and analyze vast amounts of travel data efficiently, tackling the challenges posed by the diverse and detailed content in the tourism field. We have developed an innovative text clustering methodology that combines BERT's deep linguistic analysis capabilities (Bidirectional Encoder Representations from Transformers) with the thematic organization strengths of LDA (Latent Dirichlet Allocation). This hybrid model, further refined with the dimensionality reduction capabilities of ELM-AE and the optimization precision of PPSO (Phasor Particle Swarm Optimization), yields concise, contextually enriched text representations. Such refined data representations enhance the accuracy of K-means clustering, facilitating nuanced topic identification within the complex domain of travel reviews. This approach streamlines feature extraction and ensures rapid training and minimal loss, underscoring the model's effectiveness in distilling and reconstructing textual features. Our application of this hybrid LDA-BERT model to analyze TripAdvisor reviews of Thailand's shopping destinations reveals meaningful insights, significantly aiding in understanding customer experiences. Despite its contributions, this study acknowledges limitations, including biases in user-generated content and the intricacies of accurately interpreting sentiments and contexts within reviews. This research marks a significant step forward in utilizing NLP for tourism industry analysis, providing a pathway for future investigations to build upon.
PID Controller for A Bearing Angle Control in Self-Driving Vehicles Khather, Salam Ibrahim; Ibrahim, Muhammed A.; Ibrahim, Mustafa Hussein
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21612

Abstract

The enhancement of self-driving vehicles has the potential to disrupt traditional transportation systems, Utilizing progress in secure and intelligent mobility. However, control of movement in self-driving vehicles is still difficult to carry out driving duties in a constantly changing road environment. The regulation of bearing angle is an essential component in self-driving vehicles navigation systems, facilitating the secure and efficient operation of vehicles across a range of environments, including urban streets, highways, and off-road terrain. It employs algorithms and sensor fusion to perceive surroundings, compute trajectories, and execute precise steering commands. The bearing angle represents the angle between the vehicle's current and desired directions. By consistently monitoring this angle and implementing appropriate steering inputs, the self-driving vehicle can accurately stay on track and proactively adapt to obstacles or adhere to a designated route. In this context, we explore the advancements in bearing angle control methods for self-driving vehicles. By conducting simulations of a simplified block diagram for a self-guiding vehicle's bearing angle control techniques, the efficacy of the steering system of self-driving cars has been briefly examined. We provide various methods of control, which are considered approaches for controlling the angle of bearings through lag lead compensation and PID auto-tuned controllers. The results show that the auto-tuned PID controller outperforms all other controllers in terms of transient and steady-state responses.
Enhancing Humanoid Robot Soccer Ball Tracking, Goal Alignment, and Robot Avoidance Using YOLO-NAS Jati, Handaru; Ilyasa, Nur Alif; Dominic, Dhanapal Durai
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21839

Abstract

This research aims to enhance humanoid robot soccer Ball Tracking, Goal Alignment, and Robot avoidance tasks using YOLO-NAS. The study followed a three-stage approach involving model engineering, which involves model training, code integration, and testing by comparing it with YOLO-v8 and YOLOv7. We measured the mAP (Mean Avegara Precision) and the speed of the detection of each model. Descriptive and Friedman techniques were employed to interpret testing results. In the ball tracking task, YOLO-NAS achieved a success rate of 53.3% compared to YOLOv7 with 68.3%. In the goal alignment task, YOLO-NAS achieved the highest success rate of 91.7%. In the Robot Avoidance task, YOLO-NAS, the same as YOLOv8, 100% nailed the test. These findings suggest that YOLO-NAS performs exceptionally well in the goal-alignment task but does not excel in two other tasks related to humanoid robot soccer.
Unlocking Solar Potential: Advancements in Automated Solar Tracking Systems for Enhanced Energy Utilization Hussain, Abadal-Salam T.; Hakim, Baqer A; Ahmed, Saadaldeen Rashid; Abed, Tareq Hamad; Taha, Taha A.; Hasan, Taif. S.; Hasan, Raed Abdulkareem; Hashim, Abdulghafor Mohammed; Tawfeq, Jamal Fadhil
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.19931

Abstract

The use of solar tracking systems has become vital and has established itself as a vital element in the generation of solar energy by enhancing the collection efficiency. This paper seeks to understand the necessity of shifting from conventional energy sources and why issues like scarcity of fossil fuel, and pollution are some of the hurdles toward achieving sustainable energy. Solar power, in particular, is one of the lights at the end of this tunnel since it pioneers a shift towards the usage of clean energy in the world. The subject of interests of the study is on how tracking systems help in maximizing energy collection from solar systems by interchanging it with the movement of sun’s path. It discusses the method that was followed, which involves selecting component, designing circuit and developing software together with presenting empirical data that was obtained from a three-day, Twenty-four-hour experiment. Outcomes show that there is an improvement on voltage stability, the level of solar irradiation and temperature regulation when the system is applied as compared to static system and its applicability for the enhancement of the renewable energy harnessing methods by using the solar tracking technology. Finally, it outlines the future research directions to continue exploring the proposed methods and its wider impact on renewable energy generation.
Vicinity Monitoring of Military Vehicle Cabin to Improve Passenger Comfort with Fusion Sensors and LoRa RFM95W Fadillah, Wildan Muhammad Yasin; Mutiara, Giva Andriana; Periyadi, Periyadi; Alfarisi, Muhammad Rizqy; Meisaroh, Lisda
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.21600

Abstract

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.
Handling Four DOF Robot to Move Objects Based on Color and Weight using Fuzzy Logic Control Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; Munadi, M.
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20087

Abstract

Manipulators are increasingly used in industry to improve efficiency in jobs that require precision, long duration, and repetitive work. This research was conducted on a laboratory scale to control manipulators on a pick-and-place system in the product storage and packing area. The object of this research is a four-degree-of-freedom (4-DOF) manipulator controlled using a fuzzy logic system. The hardware used is a conveyor machine to model the product delivery process, Dobot Magician as a 4-DOF manipulator, HX711 load cell serves as a weight sensor, TCS-3200 serves as a color sensor, and Arduino Mega 2560 as a controller. The software used is Dobot Studio as the main program to control the movement of the robot and Matlab to develop the Fuzzy Logic Control (FLC) function, which is embedded in the Arduino. Fuzzy logic control processes weight variables and color variables read by sensors as information data to control the movement of the manipulator. The results showed that the manipulator was able to pick up and place objects according to the path-planning coordinates. The testing data states that the precision and accuracy of the average coordinates of product pick and place against the path planning has an error deviation of 1.8%.
Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification Saputra, Dimas Chaerul Ekty; Ma'arif, Alfian; Sunat, Khamron
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20898

Abstract

This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Forests (RFs) to leverage their diversity and complementary features. The SVM component implements multiple kernels to identify unique data patterns, while the RF component consists of an ensemble of decision trees to ensure reliable predictions. Integrating these models into a stacked architecture allows SMKSVM-RF to enhance the overall predictive performance for classification or regression tasks by optimizing their strengths. A significant finding of this study is the introduction of SMKSVM-RF, which displays an impressive 73.37% accuracy rate in the confusion matrix. Additionally, its recall is 71.62%, its precision is 70.13%, and it has a noteworthy F1-Score of 71.34%. This innovative technique shows potential for enhancing current methods and developing into an ideal healthcare system, signifying a noteworthy step forward in diabetes detection. The results emphasize the importance of sophisticated machine learning methods, highlighting how SMKSVM-RF can improve diagnostic precision and aid in the continual advancement of healthcare systems for more effective diabetes management.
Enhanced Trajectory Tracking of 3D Overhead Crane Using Adaptive Sliding-Mode Control and Particle Swarm Optimization Alyazidi, Nezar M.; Hassanine, Abdalrahman M.; Mahmoud, Magdi S.; Ma'arif, Alfian
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.18746

Abstract

Cranes hold a prominent position as one of the most extensively employed systems across global industries. Given their critical role in various sectors, a comprehensive examination was necessary to enhance their operational efficiency, performance, and facilitate the control of transporting loads. Furthermore, due to the complexities involved in disassembling and reinstalling cranes, as well as the challenges associated with precisely determining system parameters, it became essential to implement adaptive control methods capable of efficiently managing the system with minimal resource requirements. This work proposes a trajectory tracking control using adaptive sliding-mode control (SMC) with particle swarm optimization (PSO) to control the position and rope length of a 3D overhead crane system with unknown parameters. The PSO is mainly used to identify the model and estimate the uncertain parameters. Then, sliding-mode control is adapted using the PSO algorithm to minimize the tracking error and ensure robustness against model uncertainties. A model of the systems is derived assuming changing rope length. The model is nonlinear of second order with five states, three actuated states: position x and y, and rope length l, and two unactuated states, which are the rope angles θx and θy. The system has uncertain parameters, which are the system’s masses Mx, My and Mz, and viscous damping coefficients Dx, Dy and Dy. A simulation study is established to illustrate the influence and robustness of the developed controller and it can enhance the tracking trajectory under different scenarios to test the scheme.
Sorting Line Assisted by A Robotic Manipulator and Artificial Vision with Active Safety Mogro, María F.; Jácome, Fausto A.; Cruz, Guillermo M.; Zurita, Jonathan R.
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20327

Abstract

This article presents the design, implementation and evaluation of an object classification and manipulation system in industrial environments by integrating artificial vision and a MELFA RV-2SDB robotic manipulator. The central problem lies in the need to achieve rapid and accurate classification of objects for palletizing, while ensuring the safety of operators. To address this challenge, a machine vision system based on Logitech C920 HD Pro cameras and force and torque sensors was used on the robotic manipulator. The methodology focused on the use of object and person detection algorithms, as well as direct and inverse kinematics to calculate adaptive movements of the manipulator. The experiments covered evaluation of the system's accuracy and efficiency under various lighting and environmental conditions, as well as testing people detection and geometric shape classification. The results indicated that the system allowed precise and efficient manipulation, adapting in real time to the position and characteristics of the detected objects. The conclusions highlighted the effectiveness of the system in improving productivity and safety in collaborative industrial environments, highlighting the importance of integrating cutting-edge technologies to address automation challenges in the industry.
Reliable Wireless Sensor Network Planning with Multipath Topology through Relay Placement Optimization Amron, Kasyful; Kusumawinahyu, Wuryansari M; Anam, Syaiful; Mahmudy, Wayan F
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.19921

Abstract

Recent developments in Wireless Sensor Networks (WSN) focus on scalability and reliability. This research addresses the challenge of improving reliability in WSNs through optimal relay placement and multipath topology design. A heuristic method with a Multi-Objective Optimization (MOO) approach is proposed to solve this problem. The proposed method integrates a modified Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) characteristics. The hybrid approach aims to minimize the number of relays and associated communication costs while maintaining network reliability. The method encodes relay positions and quantities into GA chromosomes that are updated by mutation, crossover, and PSO-inspired particle motion. Simulations are performed in a simplified square area with twenty randomly placed sensors, a hundred and thirty-two arranged relays, and a single sink node. As a result, the simulation generated two multipath topologies that offer unique advantages. The first emphasizes relay efficiency (61 relays, with 2096 costs), while the second ensures lower communication costs (64 relays, 1832 costs). Comparisons with alternative algorithms, including Dijkstra, A-star, GA, and PSO, prove the superiority of the proposed approach. The optimum results obtained with a composition of 95% GA and 5% PSO, outperform alternative algorithms in terms of relay efficiency and communication cost. This research contributes to the field by providing a robust solution for designing reliable multipath WSNs with a minimum number of relays.

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