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[A fuzzy inference system for hand injury level classification using surface electromyography signals ]]>
<![CDATA[Enojas, Mark Joseph Bullo]]>
<![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.pp103-112
<![CDATA[The surface electromyography (SEMG) is extensively used in assessing injuries in the musculoskeletal parts of the body. Integrating intelligence in such applications impacted the development of intelligent medical devices. The conventional way of assessing hand injury level is manually and subjectively done by experts to identify the type of rehabilitation program recommended to the patient. This work uses SEMG data to classify hand injury levels through a fuzzy inference system (FIS). Three of the many features of the SEMG signal were selected based on its high distinction levels, namely, the root-mean-square, enhanced mean-absolute value, and the waveform length. Segmentation through a sliding window method is used for feature extraction. The FIS rules were designed based on the assessment guide of the experts. A Mamdani-type FIS classifier was used with membership functions which are a combination of trapezoidal and triangular types. A MATLAB Simulink model was also designed to test the FIS system. The setup effectively identified injury levels through tests with a healthy subject, wherein no muscle activation means an injury, while the full fist, as a full muscle activation or healthy. In between signal values vary with different injury levels. In the future, this setup will be tested on patients in a rehabilitation clinic for validation.]]>
<![CDATA[Position and orientation analysis of Jupiter robot arm for navigation stability ]]>
<![CDATA[Shalash, Omar]]>;
<![CDATA[Sakr, Adham]]>;
<![CDATA[Salem, Yasser]]>;
<![CDATA[Abdelhadi, Ahmed]]>;
<![CDATA[Elsayed, HossamEldin]]>;
<![CDATA[El-Shaer, 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.pp1-10
<![CDATA[Jupiter robot has made a great impact in the educational field with its support for autonomous navigation, visual perception, and many other features from its artificial intelligence platform's learning box. This study undertakes a kinematic model design of Jupiter's arm to aid the robot's motion stability. This process involved the determination of a homogeneous transformation matrix, followed by the determination of orientation, position, and Euler angles. Ultimately, the homogeneous transformation matrix was successfully derived, and the simplification of direct kinematic matrices was achieved. Consequently, the kinematic analysis for Jupiter's arm was established using the position Denavit–Hartenberg method, orientation, and Euler angles, proving to be valuable in the context of this research.]]>
<![CDATA[Design and development of humanoid robotic arm ]]>
<![CDATA[Bhatlawande, Shripad]]>;
<![CDATA[Kulkarni, Sakshi]]>;
<![CDATA[Shaikh, Shajjad]]>;
<![CDATA[Kurian, Sachi]]>;
<![CDATA[Shilaskar, Swati]]>
<![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.pp11-18
<![CDATA[This paper presents the design, development, and evaluation of a 5-degrees of freedom (5-DoF) humanoid robotic arm featuring a sophisticated 5-finger gripper. The five degrees of freedom include the base, shoulder, elbow, wrist, and gripper, all controlled by MG996R servo motors to enhance grasping, positioning, flexibility, and mobility. The arm is constructed from laser-cut aluminum sheets. It effectively picks and places objects such as bottles and bags. A high-speed portable computing system is used to control robotics hand operations. A webcam is used for object detection and to acquire information about the surroundings. The system uses a convolutional neural network-based MobileNet architecture for object detection. The robotic hand is used as an assistive aid for amputees. It mimics finger movements based on detected objects. The system achieved a precision of 0.97 for bags and 0.93 for bottles, with accuracies of 96.83% and 92.42%, respectively. The system employs advanced computer vision algorithms and real-time strategies, ensuring adaptability across various tasks. It integrates advanced visual systems and improved feedback to enhance user interaction and overall usability. It addresses trade-offs between detection precision and processing time.]]>
<![CDATA[Faraid distribution calculation using AI-based Quranic chatbot ]]>
<![CDATA[Md Zin, Iman Hafizi]]>;
<![CDATA[Mansor, Nur Farraliza]]>;
<![CDATA[Mat Diah, Norizan]]>;
<![CDATA[Hashim, Shakirah]]>;
<![CDATA[Mansor, Mastura]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp393-406
<![CDATA[Faraid, Islamic inheritance law, refers to that aspect of Shariah law which is not properly understood and has created issues and impediments in the distribution of estates. This paper discusses the development of an AI-based Quranic chatbot to be used by the public to learn the Faraid rules and automate calculations of inheritance distribution. The chatbot has been developed using natural language processing and a rule-based algorithm, which intends to search and get an accurate interpretation from the user queries, retrieve relevant verses of the Quran, and compute the share of inheritance according to the established Islamic jurisprudence. Fuzzy match identifies and corrects variation in queries, enhancing user interaction, ensuring that it appears more intuitive and accessible. The system processes user input regarding heirs of the deceased, estate value, and debts, and applies Faraid rules to generate accurate distribution results that happen to be web-based platforms of this chatbot. It intends to link traditional Islamic knowledge with modern digital solutions, bringing Faraid calculations closer, more comfortable, faster, and transparent. Through rigorous tests and user feedback will prove above, revealing the chatbot’s potential in understanding the application of Islamic inheritance law and promoting digital engagement in all these through Quranic teachings.]]>
<![CDATA[Robotic mist bath wheelchair: innovations in automated body drying and sanitization for improved patient hygiene ]]>
<![CDATA[Mane, Vijay Mahadeo]]>;
<![CDATA[Durge, Harshal Ambadas]]>;
<![CDATA[Shieh, Chin-Shiuh]]>;
<![CDATA[Dey, Rajesh]]>;
<![CDATA[Mahajan, Rupali Atul]]>;
<![CDATA[Bhorge, Siddharth]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp301-310
<![CDATA[This paper presents the development and evaluation of the robotic mist bath wheelchair (MBWC), a multifunctional assistive device designed to enhance hygiene and comfort for individuals with limited mobility. The MBWC integrates mist-based bathing, automated sanitization, and warm air-drying into a compact, wheelchair-mounted system suitable for home and clinical settings. Experimental evaluations demonstrated effective temperature maintenance and a 30% reduction in bathing time compared to conventional methods. User trials with 20 participants indicated a 92% satisfaction rate, reflecting improvements in hygiene, comfort, and operational ease. MBWC provides a cost-effective, hygienic alternative to traditional bathing methods, addressing critical challenges in eldercare and rehabilitation environments.]]>
<![CDATA[CP_SDUNet: road extraction using SDUNet and centerline preserving dice loss ]]>
<![CDATA[Persada, Bayu Satria]]>;
<![CDATA[Susanto, Muhammad Rifqi Priyo]]>;
<![CDATA[Rahadianti, Laksmita]]>;
<![CDATA[Arymurthy, Aniati Murni]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i2.pp260-272
<![CDATA[Existing automatic road map extraction approaches on remote sensing images often fail because they cannot understand the spatial context of an image. Mainly because they could not learn the spatial context of the image and only knew the structure or texture of the image. These approaches only focus on regional accuracy instead of connectivity. Therefore, most approaches produce discontinuous outputs caused by buildings, shadows, and similarity to rivers. This study addresses the challenge of automatic road extraction, focusing on enhancing road connectivity and segmentation accuracy by proposing a network-based road extraction that uses a spatial intensifier module (DULR) and densely connected U-Net architecture (SDUNet) with a connectivity-preserving loss function (CP_clDice) called CP_SDUNet. This study analyzes the CP_clDice loss function for the road extraction task compared to the BCE Loss function to train the SDUNet model. The result shows that CP_SDUNet, performs best using an image size of 128×128 pixels and trained with the whole dataset with a combination of 20% clDice and 80% dice loss. The proposed method obtains a clDice score of 0.85 and an Interest over Union (IoU) score of 0.65 for the testing data. These findings demonstrate the potential of CP_SDUNet for reliable road extraction.]]>
<![CDATA[IntelliDrive autonomous robot powered by large language model ]]>
<![CDATA[Khan, Imran Ulla]]>;
<![CDATA[Raja, D. R. Kumar]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp339-347
<![CDATA[The rapid advancements in artificial intelligence (AI) and robotics have paved the way for innovative autonomous systems capable of performing complex tasks. This project integrates robotics with Large Language Models (LLMs) to develop an intelligent, versatile and user-friendly robotic system. The robot is designed to interpret structured commands, make real-time decisions, and navigate autonomously in dynamic environments, addressing key challenges faced by traditional autonomous systems. Central to the system is a Raspberry Pi 4, which serves as the main processing unit, integrating components such as a webcam for visual data capture, an L298N motor driver for motor control, and a Bluetooth speaker for real-time feedback. The LLM API enables the robot to process natural language commands, providing context-aware task execution and adaptability to changing scenarios. Testing has demonstrated the system’s ability to perform autonomous navigation, detect obstacles, and execute tasks effectively. This research offers a foundation for various industries, including logistics, healthcare, education, and hazardous environment operations. By incorporating LLMs the robot overcomes limitations of traditional rule-based systems, enhancing dynamic decision-making and user interaction. With its modular design and scalability, it bridges the gap between human-like intelligence and mechanical precision, setting the stage for future advancements in AI-driven robotics.]]>
<![CDATA[IoT-based cricket environment system to maximize egg production and reduce mortality rate ]]>
<![CDATA[Tjandrata, Dominic Miracle]]>;
<![CDATA[Liawatimena, Suryadiputra]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i2.pp281-289
<![CDATA[The deployment of Internet of things (IoT) technologies presents an opportunity to improve efficiency in cricket farming. This study investigates the implementation of an IoT-based system utilizing an ESP32 microcontroller, a suite of environmental sensors, and actuators. The system is supported by a ThingsBoard dashboard for data visualization and a Telegram bot for notifications. The setup was tested on a single cricket cage over a 28-day period and compared against a control group. Each cage contained 20 male and 100 female Cliring crickets. Key parameters analyzed included temperature, humidity, soil moisture, egg yield, food conversion ratio (FCR), and mortality rate. Findings show that the IoT-enabled cage consistently maintained optimal environmental conditions—temperature (20 to 32 °C), humidity (65% to 85%), and soil moisture (60% to 80%)—unlike the control, which experienced greater variability. The IoT cage yielded 87.28 grams of eggs, a 33.33% improvement over the control's 65.46 grams. Additionally, FCR improved from 2.53 to 2.01 grams per egg, and mortality rate dropped from 0.816 to 0.708. These results underscore the effectiveness of IoT systems in enhancing environmental stability, productivity, and survival rates in small- to medium-scale cricket farming operations.]]>
<![CDATA[Understanding golfers’ acceptance behavior toward robotic golf caddies by merging the task technology fits theory and the perceived risk theory ]]>
<![CDATA[Joo, Kyuhyeon]]>;
<![CDATA[Kim, Heather Markham]]>;
<![CDATA[Hwang, Jinsoo]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i2.pp173-180
<![CDATA[The current paper was designed to understand how to form the acceptance behavior of golfers toward robotic golf caddies, which conducted a hypothetico-deductive approach. The study focused on two questions: i) Can the TTF theory explain the acceptance behavior of golfers toward robotic golf caddies? ii) Do perceived risks negatively affect the acceptance behavior of golfers toward robotic golf caddies? Thus, the study postulated the impacts of task/technology characteristics and the five perceived risks (i.e. financial, time, privacy, performance, and psychological) on task technology fit, and the link between task technology fit and behavioral intentions. The data was collected from 387 golfers in South Korea, and the hypotheses tests were conducted by structural equation modeling. The results of the data analysis indicate that both task and technology characteristics increase task technology fit, and the four dimensions of perceived risks, which include time, privacy, performance, and psychology, have a negative influence on task technology fit. In addition, task technology fit also increases behavioral intentions. The study provides theoretical contributions by filling the acknowledged research gaps, and it also presents managerial implications in regard to commercializing robotic caddies in the golf industry.]]>
<![CDATA[Disease detection on coconut tree using golden jackal optimization algorithm ]]>
<![CDATA[Ramaiah, Arun]]>;
<![CDATA[Shunmugathammal, Muthusamy]]>;
<![CDATA[Kalidindi, Hari Krishna]]>;
<![CDATA[Kumareson, Anish Pon Yamini]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp407-417
<![CDATA[Millions of people depend on coconut palms for their food and livelihoods, making them one of the most essential crops in tropical countries. However, Diseases may significantly reduce the output of coconut trees and possibly result in their death. To overcome this, a novel golden jackal optimized disease detection in COCOnut tree (GOD-COCO) has been proposed for detecting diseases in coconut trees. First, the input dataset images are pre-processed in pre-processing image rotation, image rescaling, and image resizing, and the enhanced images are gathered. The enhanced images are segmented using the PSP-Net. From the segmented images, the features are extracted using the Dense-Net. Then the features needed are selected using the golden jackal optimization algorithm (GJOA). Finally, the deep belief network (DBN) classifier classifies whether it is normal or abnormal. The experimental analysis of the proposed GOD-COC has been evaluated using the Plant Pathology datasets based on the accuracy, precision, and recall standards. By this, the proposed GOD-COCO achieves an accuracy rate of 99.31% and it achieves an overall accuracy rate of 0.77%, 0.31% and 1.17% by the existing methods such as AIE-CTDDC, DL-WDM, and CLS. Similarly, the proposed GOD-COCO model takes less time, 1.13 milliseconds to detect the disease, than the existing methods, which take 3.04, 2.5, and 2.67 milliseconds, respectively.]]>
