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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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ROS-based Multi-Robot System for Efficient Indoor Exploration Using a Combined Path Planning Technique Sandanika, Wanni Arachchige Heshani; Wishvajith, Supun Hansaka; Randika, Sahan; Thennakoon, Deshitha Adeeshan; Rajapaksha, Samantha Kumara; Jayasinghearachchi, Vishan
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.v5i5.22494

Abstract

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.
Design and Development of swarm AGV’s alliance for Search and Rescue operations Pyla, Ratan; Pandalaneni, Vikranth; Raju, P Jeevan Narayana; G, Guga Priya
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.v4i6.18392

Abstract

Rapid response is essential for saving lives in search and rescue operations since the amount of time is critical. In this project, a swarm of autonomous ground vehicles (AGVs) equipped with ROS-based software architecture will be designed and built for rapid search and rescue missions. The swarm of AGVs will function autonomously to navigate through challenging areas and be outfitted with a variety of sensors, including cameras, and LIDAR. The proposed system will be capable of performing 2D mapping, live video surveillance, autonomous navigation, victim/object detection, and two-way audio communication. The goal of the project is to reduce the risk to human life in dangerous areas by providing a quick and efficient response system for search and rescue operations. A centralized management system with a de-centralized module will be created as part of the project to keep an eye on and manage the AGV horde. However, there will be a functionality to take control of a specific robot in the swarm network when needed. In difficult areas where it might not be safe for humans to operate, the suggested method will enable quick and efficient search and rescue operations.
The Classification of Aflatoxin Contamination Level in Cocoa Beans using Fluorescence Imaging and Deep learning Sadimantara, Muhammad Syukri; Argo, Bambang Dwi; Sucipto, Sucipto; Al Riza, Dimas Firmanda; Hendrawan, Yusuf
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.v5i1.19081

Abstract

Aflatoxin contamination in cacao is a significant problem in terms of trade losses and health effects. This calls for the need for a non-invasive, precise, and effective detection strategy. This research contribution is to determine the best deep-learning model to classify the aflatoxin contamination level in cocoa beans based on fluorescence images and deep learning to improve performance in the classification. The process involved inoculating and incubating Aspergillus flavus (6mL/100g) to obtain aflatoxin-contaminated cocoa beans for 7 days during the incubation period. Liquid Mass Chromatography (LCMS) was used to quantify the aflatoxin in order to categorize the images into different levels including “free of aflatoxin”, “contaminated below the limit”, and “contaminated above the limit”.  300 images were acquired through a mini studio equipped with UV lamps.  The aflatoxin level was classified using several pre-trained CNN approaches which has high accuracy such as GoogLeNet, SqueezeNet, AlexNet, and ResNet50. The sensitivity analysis showed that the highest classification accuracy was found in the GoogLeNet model with optimizer: Adam and learning rate: 0.0001 by 96.42%. The model was tested using a testing dataset and obtain accuracy of 96% based on the confusion matrix. The findings indicate that combining CNN with fluorescence images improved the ability to classify the amount of aflatoxin contamination in cacao beans. This method has the potential to be more accurate and economical than the current approach, which could be adapted to reduce aflatoxin's negative effects on food safety and cacao trade losses.
Development of Microclimate Data Recorder on Coffee-Pine Agroforestry Using LoRaWAN and IoT Technology Nurwarsito, Heru; Suprayogo, Didik; Sakti, Setyawan P.; Prayogo, Cahyo; Oakley, Simon; Wibawa, Aji Prasetya; Adaby, Resnu Wahyu
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.v5i1.20991

Abstract

Microclimate monitoring in agroforestry is very important to understand the complex interactions between vegetation, soil, and the environment. Microclimate parameters include air and soil temperature, air humidity, soil moisture, and light intensity. This research aims to develop a new microclimate data recording system for coffee-pine agroforestry, utilizing LoRaWAN and IoT technology to capture real-time microclimate parameters. Unlike traditional data loggers that require manual download on-site, this innovative system enables instant data download from IoT servers, thereby increasing data efficiency and accessibility. The system proved effective, significantly improving the precision of air temperature and humidity, as well as soil temperature measurements, with an average accuracy of 100%. However, soil moisture and light intensity recorded lower accuracies of 81.23% and 82.56%, respectively, indicating potential areas for future research and system refinement. The system maintains a 15-minute sampling period, aligning with conventional datalogger intervals. This represents an advancement in precision agriculture for microclimate monitoring, enabling the data to be utilized in decision-making for agroforestry management, which involves complex interactions between the local microclimate and the broader ecological system. It underscores the significance of sustainable land use as a response to global climate change.
Advancements, Challenges and Safety Implications of AI in Autonomous Vehicles: A Comparative Analysis of Urban vs. Highway Environments Abu, N. S.; Bukhari, W. M.; Adli, M. H.; Maghfiroh, Hari; Ma’arif, Alfian
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.v5i3.21114

Abstract

This research reviews AI integration in AVs, evaluating its effectiveness in urban and highway settings. Analyzing over 161 studies, it explores advancements like machine learning perception, sensor technology, V2X communication, and adaptive cruise control. It also examines challenges like traffic congestion, pedestrian and cyclist safety, regulations, and technology limitations. Safety considerations include human-AI interaction, cybersecurity, and liability/ethics. The study contributes valuable insights into the latest developments and challenges of AI in AVs, specifically in urban and highway contexts, which will guide future transportation research and decision-making. In urban settings, AI-powered sensor fusion technology helps AVs navigate dynamic traffic safely. On highways, adaptive cruise control systems maintain safe distances, reducing accidents. These findings suggest AI facilitates safer navigation in urban areas and enhances safety and efficiency on highways. While AI integration in AVs holds immense potential, innovative solutions like advanced perception systems and optimized long-range communication are needed to create safer and more sustainable transportation systems.
Enhance Deep Reinforcement Learning with Denoising Autoencoder for Self-Driving Mobile Robot Pratama, Gilang Nugraha Putu; Hidayatulloh, Indra; Surjono, Herman Dwi; Sukardiyono, Totok
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.v5i3.21713

Abstract

Over the past years, self-driving mobile robots have captured the interest of researchers, prompting exploration into their multifaceted implementation. They have the potential to revolutionize transportation by mitigating human error and reducing traffic accidents. The process of deploying self-driving mobile robots can be divided into several steps, such as algorithm design, simulation, and real-world application. This research paper presents a simulation using DonkeyCar on the Mini Monaco track, employing a Soft Actor-Critic (SAC) alongside a denoising autoencoder. At this point, it is limited to the simulation, serving as a proof of concept for further research with hardware implementation. The simulation verifies that relying solely on SAC for the convergence of policy is not sufficient; it yields a mean episode length of only 28.82 steps and a mean episode reward of 0.7815. The simulation ended after 3557 steps due to the inability of SAC alone to converge, without completing a single lap. Later, by integrating the denoising autoencoder, convergence of policy can be achieved. It enables DonkeyCar to adeptly track the lane of the circuit. The denoising autoencoder plays an important role in accelerating the convergence of transfer learning. Notably, the mean reward per episode reached 2380.4387, with an average episode length of 771.71 and a total of 114357 steps taken. DonkeyCar manages to complete several laps. These results affirm the effectiveness of SAC with a denoising autoencoder in enhancing the performance of self-driving mobile robots.
Accuracy Improvement for Indoor Positioning Using Decawave on ESP32 UWB Pro with Display and Regression Hapsari, Gita Indah; Munadi, Rendy; Erfianto, Bayu; Irawati, Indrarini Dyah
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.v5i3.20825

Abstract

In UWB-based indoor positioning, it is important to observe the ranging performance of the UWB module to prevent positioning errors. Ranging is the initial process in computing positioning. This research aims to observe the ranging accuracy and precision of the ESP32 UWB Pro with a Display module and analyze its performance in indoor positioning using TDoA and Trilateration. The ranging method was held using the SS-TWR which is the basic ranging used generally in UWB. ESP32 Pro is a module consisting of ESP32 and OLED display which is integrated with Decawave DW 1000. Analysis of 6750 ranging error data is carried out to determine the appropriate method to increase accuracy. The convergence of error ranges that occur leads to the use of regression as an error mitigation method for Decawave on the ESP32 UWB Pro with Display module. Increasing the accuracy of ranging regression can reduce the error from MAE of 79.98cm to only 5.05cm. It’s applied to positioning to obtain the accuracy and precision performance of the TDoA and Trilateration positioning.  The resulting MAE values are 7.47cm for X and 10.49cm for Y in TDoA Positioning. Meanwhile, in Trilateration, the MAE was 8.15cm for X and 8.47cm for Y. Our findings indicate that an increase in ranging accuracy with regression had an impact on positioning accuracy. However, the spread of error positioning shows that it’s still weak in precision.
Implementation of IoT of an Electric Infant Warmer to Prevent Hypothermia in Newborns Fauzi, Ekha Rifki; Maharesi, Angger; Setiyadi, Noor Alis
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18539

Abstract

Hypothermia is a drop in body temperature below 36.5°C in newborns. It results in an internal distribution of body heat from the nucleus to the periphery, followed by heat loss greater than metabolic production. Hypothermia is one of the factors predisposing to metabolic disorders, intracranial hemorrhage, respiratory distress, and Necrotizing enterocolitis. Hypothermia problems can be treated with infant warmers. Thus, the need for a infant warmer is considered to improve survival in newborns. This study aims to improve the accuracy of temperature monitoring, increase security, and enable remote monitoring. The temperature sensor of the device is calibrated with comparable devices such as Incubator Analyzer and Thermo hygrometer while the SpO2 sensor is calibrated with Spotlight SpO2 Functional Tester and Thermo hygrometer. Achievement and validation of temperature and oxygen saturation use a calibration comparison tool. The results of the temperature sensor measurements, including air temperature and skin sensor temperature, namely: air temperature error tolerance ≤2°C and skin sensor temperature error tolerance ± 0.5 ° C. All two indicators have the same standard deviation value of ±0.49. The SpO2 indicator reached an error tolerance value of ± 1% O2 with a standard deviation value of ± 0.6-0.9 from six trials. Then the pulse rate indicator obtained an error tolerance of ±5% with a standard deviation value of ±0.6. The smart infant warmer tool provides benefits to avoid excessive heat from the heater and minimize low temperatures that cause hypothermia through the Internet of Things technology. Furthermore, this research can be improved with machine learning technology to increase efficiency and effectiveness in patient treatment.
Smartphone Sensor-based Development and Implementation of a Remotely Controlled Robot Arm Salah, Wael A.; Sneineh, Anees Abu; Shabaneh, Arafat A. A.
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.v5i4.21987

Abstract

As a result of advances in both technology and science, it is now possible to carry out essential processes such as lifting objects and moving them by remote control of an arm. In this sense, it is much easier for a person to engage in potentially dangerous activities without running the risk of getting hurt. This article presents the development and design of a robot arm that is controlled by a smartphone device using gyroscope sensors integrated inside. Smartphones with built-in gyroscope sensors are used to operate robot arms in a flexible and affordable manner. The robot arm's movement is effectively controlled by the gyroscope sensors, which include proximity, orientation, and accelerometer sensors, to get it to the required position. The developed prototype found to capable of handling a variety of objects with a smooth movement and transporting them based on the movement of a mobile phone. The control of the arm imitates the movements of a human being, which results in the reduction of the amount of time and effort required by a person to carry out a certain process.
A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks Msala, Youssef; Hamed, Oussama; Talea, Mohamed; Aboulfatah, Mohamed
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.v4i6.18650

Abstract

This paper presents an innovative task allocation method for multi-robot systems that aims to optimize task distribution while taking into account various performance metrics such as efficiency, speed, and cost. Contrary to conventional approaches, the proposed method takes a comprehensive approach to initialization by integrating the K-means clustering algorithm, the Hungarian method for solving the assignment problem, and a genetic algorithm specifically adapted for Open Loop Travel Sales Man Problem (OLTSP). This synergistic combination allows for a more robust initialization, effectively grouping similar tasks and robots, and laying a strong foundation for the subsequent optimization process. The suggested method is flexible enough to handle a variety of situations, including Multi-Robot System (MRS) with robots that have unique capabilities and tasks of varying difficulty. The method provides a more adaptable and flexible solution than traditional algorithms, which might not be able to adequately address these variations because of the heterogeneity of the robots and the complexity of the tasks. Additionally, ensuring optimal task allocation is a key component of the suggested method. The method efficiently determines the best task assignments for robots through the use of a systematic optimization approach, thereby reducing the overall cost and time needed to complete all tasks. This contrasts with some existing methods that might not ensure optimality or might have limitations in their ability to handle a variety of scenarios. Extensive simulation experiments and numerical evaluations are carried out to validate the method's efficiency. The extensive validation process verifies the suggested approach's dependability and efficiency, giving confidence in its practical applicability.

Page 40 of 71 | Total Record : 708

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