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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 23 Documents
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Search results for , issue "Vol 15, No 1: March 2026" : 23 Documents clear
An improved black-winged kite algorithm optimized back-propagation neural network for biceps curl classification Liu, Chunqing; Geok Soh, Kim; Abu Saad, Hazizi; Ma, Haohao
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp247-256

Abstract

Accurately identifying and classifying biceps curl types is of vital importance for sports training and upper limb joint rehabilitation training. It can improve the effect and reduce the risk of injury caused by incorrect training. In this study, a dataset of biceps curl training was obtained by measuring wearable sensors. After data preprocessing, 340 samples of 35-dimensional feature data were obtained. The classification labels of the dataset were marked as 1-5 according to the five types of biceps curl. This study proposed a black-winged kite algorithm (IBKA) that uses the good point set (GPS) method and the adaptive spiral search rule, a multi-strategy. IBKA optimized the initial weights, biases, and hidden layer numbers and provided them to the back-propagation neural network (BPNN) to establish the IBKA-BPNN model. The constructed IBKA-BPNN model improved the classification accuracy of the training set from 79.83% to 94.54%, and the accuracy of the test set from 69.61% to 88.33%. The IBKA-BPNN model proposed in this study provides a reliable decision-making basis for real-time coaching, athlete performance analysis, and upper limb rehabilitation. Future work will expand the dataset, integrate more bio signals, and explore lightweight deployment on wearable hardware.
Emotion recognition and classification using Inception EfficientNet based on electroencephalography signals J, Jananee; F, Emerson Solomon; M, Sundar Raj
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp190-199

Abstract

Emotions are intricate psychological phenomena arising from the interaction of internal cognitive states and external environmental inputs. The manual extraction of electroencephalography (EEG) signals results in less optimal performance of learning models. To overcome this, a novel EEG-based emotion recognition and classification (EEG-EMRE) model has been proposed for the detection and classification of emotions. Initially, the input EEG-Signals are pre-processed using quantum signal processing (QSP) to enhance the quality by removing the noise from the signal. The enhanced signals are fed into an improved Inception EfficientNet for extracting the relevant features. The Penta types of emotions, such as happy, sad, anger, scared, and anxiety, are classified using a bidirectional-k nearest neighbors (KNN) classification network. The performance of the proposed EEG-EMRE approach is evaluated using the F1-Score, recall, specificity, accuracy, and precision. The proposed Inception EfficientNet for feature extraction network improves the overall accuracy by 0.41%, 1.52%, 0.63%, 1.55% better than ResNet, AlexNet, GoogleNet, and DenseNet. The proposed EEG-EMRE method achieves an overall accuracy by 0.68%, 1.77%, and 0.52% better than the linear formulation of differential entropy (LF-DfE), extreme learning machine wavelet auto encoder (ELM-W-AE), and attention-based convolutional transformer neural network (ACTNN), respectively.
Experimental validation of a trajectory tracking controller for a two-wheeled mobile robot Kazed, Boualem; Guessoum, Abderrezak
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp33-42

Abstract

One of the most important and challenging problems of any kind of autonomous mobile robot is the ability to accurately control its onboard actuators, enabling it to fulfill a specified task. In the case of a two-wheeled mobile robot, this can only be achieved through a pair of adequate steering control signals. The main goal of this paper is to design a nonlinear multivariable controller allowing a self-made mobile robot prototype to track a prescribed trajectory. The basic principle of this control approach uses the Lyapunov theory as a primary tool to derive two steering control laws, making a three-state error vector converge to zero. Tuning the proposed controller parameters is carried out using an equivalent dynamic simulated model. This controller is then applied to generate the resulting command signals to the actual robot. This is achieved through a real-time high-speed serial communication between a stationary personal computer (PC), on which a MATLAB/Simulink version of this controller is performing, and an onboard Microchip 16 bits dsPIC33FJ64MC802 microcontroller running a firmware that takes care of all the data exchange with the connected PC and a set of two proportional integral derivative (PID) controllers ensuring that the rotational speeds of the robot wheels are kept very close to those required by the main controller, running on this PC. The performance of the proposed controller is evaluated using two different shaped trajectories. These tests show that the robot is able to gradually follow the required path with minimal lateral error. The robustness of this controller is demonstrated through its capability to reject external disturbances triggered during these experimental tests.
Modeling and simulation of an active quarter-car suspension system using a synergetic controller Dung, Dao Trong; Le, Trong Nghia; Lukyanov, Alexandr D.; Chiem, Nguyen Xuan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp210-221

Abstract

This paper presents the modeling and simulation of an active quarter-car suspension system (AQCSS) designed to enhance operational performance and ride comfort across various road conditions. First, a dynamic quarter-car model was developed, incorporating all the components of AQCSS and road-induced stimuli, based on the Euler–Lagrange method. Subsequently, a synergetic controller is designed by selecting a manifold that meets the system’s technical requirements. The proposed controller ensures a balance between ride comfort and road-holding performance by leveraging this manifold design. This control framework enables flexible adjustment of the damping force in real time according to the system states and external excitations. The stability of the closed-loop system is rigorously established through Lyapunov analysis. Numerical simulations are carried out in MATLAB to assess the proposed control law by benchmarking it against a passive suspension configuration and a sliding mode control approach, thereby demonstrating its effectiveness.
EdgeRetina: Hybrid multimedia architecture for diabetic retinopathy screening on low-cost mobiles Amina, Guidoum; Soltana, Achour; Bougherara, Maamar; Rafik, Amara; Tayeb, Mhamed
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp234-246

Abstract

Diabetic retinopathy (DR) is a major cause of preventable blindness, particularly in areas with limited medical resources where access to ophthalmologists is critical. Existing automated solutions struggle to balance clinical performance, cost-effectiveness, and robustness in the face of fundus image variability—including lighting differences, artifacts, and uneven capture quality. To address this challenge, we propose EdgeRetina, an integrated solution for diabetic retinopathy screening on low-cost mobiles. Our approach combines lightweight preprocessing (128×128 resizing, intensity normalization, and targeted augmentations simulating real-world conditions) with a hybrid SqueezeNet-MobileViT architecture (1.4 million parameters), optimized by dynamic threshold calibration (median: 0.3), maximizing clinical utility. Clinically calibrated INT8 quantization reduces the model to 8.27 MB (-92%) without altering diagnostic performance (sensitivity of 90.7% for referable diabetic retinopathies), while preserving compatibility with floating point 32 (FP32)-based gradient-weighted class activation mapping (Grad-CAM) visualizations. Evaluated on the APTOS 2019 dataset, this solution achieves an AUC of 0.96 with a latency (inference time) of 15.43 ms, reducing CPU consumption by 43% compared to FP32. The dynamic threshold/INT8 coupling decreases false positives by 71.4%. This pipeline thus enables accurate, accessible, and early screening of diabetic retinopathy on low-cost mobile devices, combining operational efficiency and diagnostic reliability in constrained environments, which is crucial to prevent avoidable blindness.
Cascading automata to improve efficiency of large language models agents with GraphRAG for error analysis Haritas, Hrishikesh K.; Sadarangani, Vineet H.; Shidaganti, Ganeshayya Ishwarayya; Bankapure, Darshan; Vishal, Rahul K.; Vijayasimha, Shreya
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp149-161

Abstract

Robotic process automation (RPA) has been deployed in a plethora of industries, including the banking and insurance sectors. However, the key challenge of handling unexpected situations manifests either as an inadequacy of programming (since all situations cannot possibly be foreseen) or incongruous inputs. In parallel, deep learning models, including large language models (LLMs) and visual language models (VLMs), have shown human-like cognitive capabilities in real-world tasks, germinating the field of agentic LLMs. However, their computational expense, slow inference times, and massive energy consumption impede large-scale usage. We propose a framework that combines the two approaches to enable expedient invocation of LLMs for handling exceptions and supervising RPA bots. It aims to minimize the need for human supervision by “meta” automation, while also reducing energy usage and processing time. The automation workflow is presented as a graph, and our pipeline uses the GraphRAG framework to analyze and fix errors. We demonstrate the potential of our pipeline through two real-world examples in the banking and insurance sectors, provide our GitHub repository for reproducibility, and conclude with future research directions.
Sentiment aware interactive Chatbot AI using multi agent processing model Shukla, Vinod Kumar; Alagarsamy, Sumithra; Nagarajan, Vijaylakshmi; Shanmugam, Gavaskar
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp200-209

Abstract

Understanding user sentiment has become more important for organizations and consumers due to the rapid growth of social media platforms such as marketplaces, platforms for connecting brands and consumers, and public discussion platforms. Emotions that are based on aspects, nuanced within context, and multifaceted often require complex sentiment analysis algorithms to interpret properly. Furthermore, these systems do not provide real-time information to help companies make better decisions and enhance consumer satisfaction. To tackle these challenges, a novel Interactive Chatbot artificial intelligence (IChat-AI) approach has been proposed in this paper for sentiment-aware chatbot interaction. The word to vector (W2V), term frequency-inverse document frequency (TF-IDF), and bag of words (BoW) are utilized to effectively extract essential features. The deep Kronecker neural network (DKNN) is utilized to predict and classify the emotions into five classes, such as sad, happy, neutral, angry, and fearful. Python has been used to simulate the suggested model. The efficacy of the suggested system is examined employing parameters including recall, execution time, F1-score, complexity, precision, scalability, accuracy, and response time. The developed IChat-AI strategy performs better regarding accuracy than the existing methods, including RoBERTa, TLSA, and multimodal transformers fusion for desire, emotion, and SA (MMTF-DES) approaches, by 5.33%, 4.73%, and 14.39%.
Design and drag force analysis of an autonomous underwater remotely operated vehicles for coral reef health assessment Rajendran, Pandiyarajan; Alavandar, Srinivasan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp181-189

Abstract

This research presents the conception and building of an inexpensive remotely operated vehicle (ROV) system to ease the tasks of underwater inspection and environmental monitoring in areas where the global positioning system (GPS) signal is not available. A Raspberry Pi-based control unit, an inertial measurement unit (IMU), and depth sensors are merged in the system so that simple data acquisition and remote operation can be carried out. ROV hydrodynamic drag and stability for a state of ideal balance and maneuverability were assessed through tests based on preliminary simulations in Fusion 360 and empirical calculations. The ROV is confirmed to be behaving as expected in terms of stability, imaging capabilities, and responsiveness to operator control in the testing that was done in controlled water environments. This paper, the work, and the testing, in fact, present the initial design, but it is a significant step towards the consideration of the possible further embedding of autonomous features “simultaneous localization and mapping (SLAM)-based navigation, doppler velocity log (DVL), light detection and ranging (LiDAR) systems” for completely autonomous underwater guided missions.
Modeling and control of a 3D under-actuated bipedal robot using partial feedback linearization Guessam, Ali; Abdessemed, Foudil; Chehhat, Abdelmadjid
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp122-135

Abstract

This article presents a dynamic modeling and control framework for a 3D underactuated five-link bipedal robot with 14 degrees of freedom (DoF) and eight actuators. The robot exhibits highly nonlinear, strongly coupled, and hybrid dynamics, posing challenges for conventional control approaches. To address these issues and introduce our research contribution, a partial feedback linearization (PFL)-based tracking framework is proposed, which analytically decouples the system into actuated and unactuated subsystems, enabling efficient real-time control. Unlike hybrid zero dynamics (HZD) methods that enforce virtual constraints online and require offline gait optimization, or model predictive control (MPC) schemes that are online optimization based dependent and computationally demanding, the proposed PFL approach achieves computational simplicity and fast implementation through closed-form control laws. In contrast to zero-moment point (ZMP)-based controllers, PFL enables dynamic underactuated walking with PD feedback for accurate trajectory tracking and disturbance attenuation, though robustness to large uncertainties and disturbances may require additional mechanisms, such as adaptive control, sliding-mode, or fuzzy logic. Simulation results of the applied control method demonstrate the periodic nature and stability of generated walking gaits, which proves the effectiveness and reliability of the proposed control approach.
Remote procedure call communication and control of autonomous mobile robot for indoor smart waste monitoring Yusof, Ashaari; Man, Abdullah; Ibrahim, Azmi; Husni Zai, Mohamed Ashraf; Hossen, Md. Jakir
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp89-98

Abstract

The integration of autonomous mobile robots (AMRs) and Internet of Things (IoT) technology has revolutionized various industries, including smart waste management (SWM). In this paper, the implementation of a customized remote procedure call (RPC) methodology was successfully demonstrated. This methodology facilitated control and monitoring of AMRs for smart indoor waste management to collect and dispose waste, monitor bin threshold levels and report relevant parameters to a cloud-based platform. Key operational parameters from the AMR and the smart bins via assembled user smart dashboard ensures seamless user monitoring for indoor waste management. Our findings underscore the relevance of RPC in advancing smart waste management technologies, contributing to operational efficiency and sustainability.

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