IAES International Journal of Robotics and Automation (IJRA)
Robots are becoming part of people's everyday social lives and will increasingly become so. In future years, robots may become caretaker assistants for the elderly, or academic tutors for our children, or medical assistants, day care assistants, or psychological counselors. Robots may become our co-workers in factories and offices, or maids in our homes. The IAES International Journal of Robotics and Automation (IJRA) is providing a platform to researchers, scientists, engineers and practitioners throughout the world to publish the latest achievement, future challenges and exciting applications of intelligent and autonomous robots. IJRA is aiming to push the frontier of robotics into a new dimension, in which motion and intelligence play equally important roles. Its scope includes (but not limited) to the following: automation control, automation engineering, autonomous robots, biotechnology and robotics, emergence of the thinking machine, forward kinematics, household robots and automation, inverse kinematics, Jacobian and singularities, methods for teaching robots, nanotechnology and robotics (nanobots), orientation matrices, robot controller, robot structure and workspace, robotic and automation software development, robotic exploration, robotic surgery, robotic surgical procedures, robotic welding, robotics applications, robotics programming, robotics technologies, robots society and ethics, software and hardware designing for robots, spatial transformations, trajectory generation, unmanned (robotic) vehicles, etc.
Articles
470 Documents
<![CDATA[Analyzing the effectiveness of waiting times at the pharmacy of the King Hamad University Hospital ]]>
<![CDATA[Al-Mofleh, Anwar]]>;
<![CDATA[Alseddiqi, Mohamed]]>;
<![CDATA[Najam, Osama]]>;
<![CDATA[AlMannaei, Budoor]]>;
<![CDATA[Albalooshi, Leena]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp121-128
<![CDATA[This study investigates the efficiency of waiting times at the pharmacy of King Hamad University Hospital, with a primary focus on optimizing patient experiences. Efficient pharmacy services are vital for ensuring the timely provision of medications to patients. Prolonged waiting times not only affect patient satisfaction but may also have implications for overall healthcare outcomes. To assess the effectiveness of waiting times, we conducted comprehensive analysis, including data collection, surveys, and observations. Our findings reveal valuable insights into the current state of pharmacy operations and the patient experience. We explore factors contributing to waiting times, such as prescription processing, queue management, and staff allocation. Through this analysis, we aim to provide actionable recommendations to enhance pharmacy efficiency, reduce waiting times, and improve patient satisfaction. Our study underscores the importance of optimizing pharmacy operations to ensure that patients receive timely and high-quality healthcare services. By addressing these issues, King Hamad University Hospital can not only enhance the overall patient experience but also contribute to better healthcare outcomes and increased operational efficiency. This research serves as a valuable resource for healthcare administrators, policymakers, and practitioners seeking to improve pharmacy services and patient satisfaction in hospital settings.]]>
<![CDATA[Optimizing robot anomaly detection through stochastic differential approximation and Brownian motion ]]>
<![CDATA[Pillai, Branesh M.]]>;
<![CDATA[Mishra, Arush]]>;
<![CDATA[Thomas, Rijo Jacob]]>;
<![CDATA[Suthakorn, Jackrit]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp19-30
<![CDATA[This paper presents an adaptive approximation method for detecting anomalous patterns in extensive data streams gathered by mobile robots operating in rough terrain. Detecting anomalies in such dynamic environments poses a significant challenge, as it requires continuous monitoring and adjustment of robot movement, which can be resource intensive. To address this, a cost-effective solution is proposed that incorporates a threshold mechanism to track transitions between different regions of the data stream. The approach utilizes stochastic differential approximation (SDA) and optimistic optimization of Brownian motion to determine optimal parameter values and thresholds, ensuring efficient anomaly detection. This method focuses on minimizing the movement cost of the robots while maintaining accuracy in anomaly identification. By applying this technique, robots can dynamically adjust their movements in response to changes in the data stream, reducing operational expenses. Moreover, the temporal performance of the data stream is prioritized, a key factor often overlooked by conventional search engines. This paper demonstrates how the approach enhances the precision of anomaly detection in resource-constrained environments, making it particularly beneficial for real-time applications in rugged terrains.]]>
<![CDATA[Enhancing efficiency and reliability in high-power microwave amplifiers: a novel circuit driver approach ]]>
<![CDATA[Jebelli, Ali]]>;
<![CDATA[Lotfi, Nafiseh]]>;
<![CDATA[Partovibakhsh, Maral]]>;
<![CDATA[Mahabadi, Arezoo]]>;
<![CDATA[Zare, Mohammad Saeid]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp82-92
<![CDATA[This paper introduces an innovative circuit driver engineered to significantly enhance the efficiency and longevity of high-power microwave amplifiers, addressing critical limitations of traditional drivers in handling high-power systems. The proposed design features advanced voltage sequencing, which is crucial for extending component life and ensuring safe operation within the safe operating area (SOA). By integrating a sophisticated circuit board with real-time feedback sensors, controlled by a microcontroller, the system ensures continuous monitoring and rapid response to potential operational hazards. The driver automatically engages a fail-safe mode when thresholds are breached, prioritizing efficiency optimization and minimizing energy waste. Rigorous testing has confirmed the circuit driver’s capability to meet and exceed the stringent demands of high-power microwave amplifier applications. This work offers a robust, reliable solution that not only overcomes existing challenges but also sets a new standard for the performance and safety of microwave amplification systems, making it a valuable contribution to the field of power electronics.]]>
<![CDATA[Position tracking of DC motor with PID controller utilizing particle swarm optimization algorithm with Lévy flight and Doppler effect ]]>
<![CDATA[Mohamed Azmi, Nur Iffah]]>;
<![CDATA[Mat Yahya, Nafrizuan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp67-73
<![CDATA[This paper presents the implementation of the particle swarm optimization with the Lévy flight Doppler effect (PSO-LFDE) algorithm for optimizing proportional-integral-derivative (PID) controller parameters in a direct current (DC) motor system. Traditional optimization algorithms like particle swarm optimization, whale optimization algorithm, grey wolf optimizer, and moth flame optimization often face challenges in balancing exploration and exploitation, leading to suboptimal performance. The proposed PSO-LFDE algorithm addresses these issues by incorporating Lévy flight for enhanced exploration and the Doppler effect for refined exploitation. The algorithm is validated using MATLAB/Simulink for position control in a DC motor system with step inputs of 10, 30, and 60 cm. Key performance metrics, including rise time, settling time, peak time, and steady-state error, were compared against other optimization methods. PSO-LFDE demonstrated superior performance, achieving a 41.63% improvement in rise time and a 70.20% reduction in peak time compared to other methods. These results highlight PSO-LFDE's effectiveness in optimizing PID controller parameters and improving the dynamic response of DC motor systems, offering a robust solution for real-world control applications.]]>
<![CDATA[Design of a prototype firefighting robot based on an Arduino microcontroller using machine learning technique ]]>
<![CDATA[Chenchireddy, Kalagotla]]>;
<![CDATA[Dora, Radhika]]>;
<![CDATA[Jagan, Vadthya]]>;
<![CDATA[Mulla, Gouse Basha]]>;
<![CDATA[Jegathesan, Varghese]]>;
<![CDATA[Sydu, Shabbier Ahmed]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp31-37
<![CDATA[The design and implementation of this paper are mainly based on control of the autonomous firefighting robot. In recent years, robotics has turned out to be an ingredient in which many people have shown their interest. Robotics has gained popularity due to the advancement of many technologies of computing and nanotechnologies. The output of the fire sensor is connected to the Arduino controller that controls the movement of the vehicle and the operation of spraying water. An infrared sensing circuit is designed with the infrared sensors placed in front of the vehicle to avoid collision with the obstacles. A total of two inbuilt reduction geared direct current motors are used in the paper for the robot movement in all the directions forward, backward, right, and left directions. For more practicality, a small water tank with a pumping motor is also arranged over the chassis and the water sprinkler pipe that is firmly fixed over the plate where the sensor is arranged can deliver water with some force. When the sensor detects the fire, the sprinkler is positioned toward fire flames; the pumping motor will be energized automatically to extinguish the fire. The main advantage of the proposed system automatically controls the fire by using advanced control techniques.]]>
<![CDATA[Integration of natural language processing methods and machine learning model for malicious webpage detection based on web contents ]]>
<![CDATA[Liaquathali, Shaheetha]]>;
<![CDATA[Kadirvelu, Vadivazhagan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp47-57
<![CDATA[Malicious actors continually exploit vulnerabilities in web systems to distribute malware, launch phishing attacks, steal sensitive information, and perpetrate various forms of cybercrime. Traditional signature-based methods for detecting malicious webpages often struggle to keep pace with the rapid evolution of malware and cyber threats. As a result, there is a growing demand for more advanced and proactive approaches that can effectively identify malicious web content based on its characteristics and behavior. Detection based on web content is crucial because malicious webpages can be designed to mimic legitimate ones, making them difficult to identify through traditional means. By analyzing the content of webpages, it becomes possible to uncover patterns, anomalies, and malicious intent that may not be evident from surface-level inspection. The proposed approach integrates a pretrained Word2Vec model with seven distinct machine learning classifiers to enhance malicious webpage detection. Initially, web contents (documents) are encoded using the Word2Vec model, followed by the computation of average Word2Vec embeddings for each document. Subsequently, each classifier is trained on the extracted average Word2Vec embedding features. The results demonstrate that the Word2Vec model significantly enhances the detection accuracy, achieving an accuracy of 94.8% and an F1-score of 94.9% with the random forest classifier, and an accuracy of 94.6% and an F1-score of 94.7% with the extreme gradient boosting classifier.]]>
<![CDATA[Performance comparison of optical flow and background subtraction and discrete wavelet transform methods for moving objects ]]>
<![CDATA[Sharma, Monika]]>;
<![CDATA[Kaswan, Kuldeep Singh]]>;
<![CDATA[Yadav, Dileep Kumar]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp93-102
<![CDATA[Self-driving cars and other autonomous vehicles rely on systems that can recognize and follow objects. The ways help people make safe decisions and navigate by showing things like people, cars, obstacles, and traffic lights. Computer vision algorithms encompass both object detection and tracking. Different methods are specifically developed for picture or video analysis not only to identify items within the visual content but also to accurately determine their precise locations. This can operate independently as an algorithm or as a constituent of an item-tracking system. Object tracking algorithms can be used to follow objects over video frames, providing a contrasting approach. The research article focuses on the mathematical model simulation of optical flow, background subtraction, and discrete wavelet transform (DWT) methods for moving objects. The performance evaluation of the methods is done based on simulation response time, accuracy, sensitivity, and specificity doe several images in different environments. The DWT has shown optimal behavior in terms of the response time of 0.27 seconds, accuracy of 95.34 %, selectivity of 95.96 %, and specificity of 94.68 %.]]>
<![CDATA[Design and development of an Arduino-based oxygen saturation, heart rate variability, and blood glucose measurement device ]]>
<![CDATA[Putra, Yehezkiel P.]]>;
<![CDATA[Wibowo, Wahyu K.]]>;
<![CDATA[Prayogi, Soni]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp74-81
<![CDATA[This study presents the design and development of an Arduino-based device for measuring oxygen saturation, heart rate variability (HRV), and blood glucose levels. The primary goal is to create an affordable, portable, and accurate health monitoring solution suitable for home health care and remote medical services. The device integrates a pulse oximeter sensor to measure oxygen saturation and heart rate, an electrocardiogram (ECG) sensor for capturing HRV data, and a non-invasive sensor for regular blood glucose monitoring, all managed by an Arduino microcontroller. Data collected by the sensors is processed and displayed on a user-friendly interface, enabling real-time tracking of health metrics. The device's performance was rigorously tested and validated against standard medical equipment, demonstrating comparable accuracy in measuring the targeted health parameters. This innovative solution aims to enhance personal health monitoring, reduce the burden on healthcare systems, and promote early detection and better management of health conditions. The affordability and ease of use make this device accessible to a wider population, potentially improving health outcomes. Future developments will focus on refining sensor accuracy and expanding the device's capabilities to monitor additional health metrics.]]>
<![CDATA[Long-range radio and Internet of things-inspired smart road reflectors for smart highways ]]>
<![CDATA[Singh, Rajesh]]>;
<![CDATA[Gehlot, Anita]]>;
<![CDATA[Akram, Shaik Vaseem]]>;
<![CDATA[Singh, Vivek Kumar]]>;
<![CDATA[Iqbal, Mohammed Ismail]]>;
<![CDATA[Mahala, Rahul]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp113-120
<![CDATA[The Internet of things (IoT) has been proven as an efficient technology for real-time monitoring of physical things through the Internet from any location. With the advancement in sensors and communication technologies, the implementation of IoT is adopted in wide extensions. Road reflectors on highway roads need to be automated and also powered with intelligence. With this motivation, we have proposed and implemented IoT and long range (LoRa) based architecture for the realization of smart road reflectors on the highway. To realize the proposed architecture, the hardware of the smart reflector and gateway is implemented on the university campus. During our implementation of the hardware, we observed the light intensity values that are sensed by smart reflectors on the server through LoRa and internet connectivity. In the future, we will be integrating additional sensors and also power the smart reflector with artificial intelligence to predict the fog status of a particular road.]]>
<![CDATA[Advanced cardiovascular disease classification using multi-modal imaging and deep learning ]]>
<![CDATA[Thankappan, Benila Christabel]]>;
<![CDATA[Krishnammal, Thanammal Kakkumperumal]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i1.pp58-66
<![CDATA[Cardiovascular disease (CVD) is a disorder of the heart and blood vessels that causes significant morbidity and mortality. They also represent a global public health burden and the primary cause of death worldwide. In this research, a novel deep learning-based multi-model image (DL-MMI) has been proposed for detecting CVD. Initially, the input Kaggle datasets images like magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and chest X-ray are fed into wavelet transform-based Multiscale Retinex in the pre-processing phase to enhance the quality of the images. Then the enhanced images are given to GLCM for extracting features in the images. Finally, the dilated convolutional neural network (D-CNN) is used to classify healthy and CVD images. The experimental findings use the specific measures of accuracy, recall, precision, specificity, and F1-score to demonstrate the durability of the DL-MMI approach. Using the Kaggle dataset the proposed DL-MMI method achieves an accuracy rate of 98.89%. The proposed DL-MMI model increases the overall accuracy by 28.62%, 7.51%, and 17.57% than the existing methods such as convolutional auto encoder, CNN, and deep learning, respectively.]]>
