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IAES International Journal of Robotics and Automation (IJRA)
Published by Institute of Advanced Engineering and Science
ISSN : 20894856     EISSN : 27222586     DOI : -
Core Subject : Engineering,
Automotive Engineering Electrical & Electronics Engineering
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 15 Documents
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Search results for , issue "Vol 14, No 1: March 2025" : 15 Documents clear
Long-range radio and Internet of things-inspired smart road reflectors for smart highways Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Singh, Vivek Kumar; Iqbal, Mohammed Ismail; Mahala, Rahul
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i1.pp113-120

Abstract

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.
Advanced cardiovascular disease classification using multi-modal imaging and deep learning Thankappan, Benila Christabel; Krishnammal, Thanammal Kakkumperumal
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i1.pp58-66

Abstract

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.
A fuzzy inference system for hand injury level classification using surface electromyography signals Enojas, Mark Joseph Bullo
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i1.pp103-112

Abstract

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.
Position and orientation analysis of Jupiter robot arm for navigation stability Shalash, Omar; Sakr, Adham; Salem, Yasser; Abdelhadi, Ahmed; Elsayed, HossamEldin; El-Shaer, Ahmed
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i1.pp1-10

Abstract

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.
Design and development of humanoid robotic arm Bhatlawande, Shripad; Kulkarni, Sakshi; Shaikh, Shajjad; Kurian, Sachi; Shilaskar, Swati
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i1.pp11-18

Abstract

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.

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