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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 533 Documents
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Integrating large language models for context-aware decision making in autonomous mobile robots Vishnu Kumar Mishra; Megha Mishra; Talasila Ram Kumar; Yenna Geetha Reddy; Gundla Rajesh; Battula Phijik; Bandla Srinivasa Rao
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp607-620

Abstract

The dynamic evolution of industrial automation has created an imperative need to transition from inflexible, rule-based systems to flexible, intelligent agents that can facilitate human-robot collaboration. The purpose of this research was to integrate large language models (LLMs) with autonomous mobile robots (AMRs) to improve context-aware decision-making. The inflexibility of traditional systems has often hindered performance in dynamic environments, as systems often rely on predefined algorithms and sensor configurations. To improve this, a modular framework was created, consisting of a central processing unit and an LLM API to interpret natural language and process environmental information. Quantitative results have been clearly specified in the abstract, which states that the success rate in resolving navigation exceptions by the proposed framework was 89% with a 5% localization error rate. Moreover, substantial savings were observed in token usage and computational resources. This study provided an imperative framework for smarter industrial automation, filling the gap between mechanical precision and artificial intelligence. The practical experimentation of an AMR model gives an outcome of a successful navigation exception resolution rate of 89% by means of the proposed framework, with an error rate of 5% localized to each exception. Additionally, significant reductions in the number of tokens used and the time taken to process tokens provide a scalable means for developing contextually-based, robust, autonomous mobile platforms’ decisions.
Remote controlled agricultural robot for spraying liquid type pesticides Kalagotla Chenchireddy; Vadthya Jagan; M. Aruna Bharathi; Malaji Sushama; Varghese Jegathesan; Shabbier Ahmed Sydu
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp553-560

Abstract

In modern agriculture, the use of automation and robotics is becoming increasingly essential to enhance efficiency, reduce labor, and ensure the safe handling of hazardous materials. This paper presents the design and development of a remote-controlled agricultural robot specifically intended for spraying liquid-type pesticides using radio frequency communication. The system comprises a mobile robot platform equipped with a pesticide tank, a spraying mechanism powered by a DC pump, and a set of drive motors for navigation. The robot is remotely controlled through an RF transmitter and receiver pair, allowing the operator to manually guide the robot across agricultural fields without direct contact with pesticides. The RF module transmits commands such as movement directions and spray activation, which are interpreted by an Arduino microcontroller onboard the robot. The system aims to reduce human exposure to harmful chemicals, minimize labor efforts, and increase precision in pesticide application. This solution is cost-effective, user-friendly, and adaptable for small to medium-sized farms, offering a practical step toward smart farming.
Enhancing grid reliability with solar-wind energy systems Swapna Subudhiray; Smrutiranjan Nayak
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp737-744

Abstract

Grid-connected hybrid photovoltaic (PV) wind systems are widely recognized as a promising solution for mitigating renewable intermittency; however, most existing studies focus either on component-level modeling or basic integration without detailed analysis of power conditioning, control coordination, and grid-side performance. In particular, the functional role of DC–DC converters and energy management strategies in stabilizing hybrid outputs under variable environmental conditions remains insufficiently explored. This paper proposes a structured grid-connected PV wind hybrid system incorporating coordinated DC–DC conversion with maximum power point tracking (MPPT) and centralized control for voltage stabilization and power smoothing. The proposed architecture explicitly defines the operational role of each subsystem, including PV array, wind turbine generator, DC–DC converters, controller, and grid interface to ensure stable power delivery under fluctuating irradiance and wind speed. A MATLAB-based model is developed to evaluate system behavior under standalone and hybrid operating modes. Simulation results demonstrate improved voltage stability and enhanced power continuity in the hybrid configuration compared to standalone PV operation. The findings confirm that coordinated power conditioning significantly improves grid reliability and supports effective renewable energy integration.
Design of a portable IoT robot with azure machine learning for monitoring mine workers’ health Shanthi Natarajan; Vijayaraja Loganathan; Dhanasekar Ravikumar; Diwakar Venkat Nalini; Harish Elangovan; Balaji Arikrishnan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp577-588

Abstract

The mining environment exposes workers to physical, environmental, and health risks. The lack of effective real-time health monitoring systems leads to delayed medical responses. Hence, this paper discusses developing a portable Internet of Things (IoT) robot with advanced machine learning and cloud computing to monitor mine workers’ health and send out alerts. The system is equipped with IoT sensors that monitor parameters continuously, such as heart rate, body temperature, blood pressure, and environmental factors (gas concentrations, air quality). Data collected in real-time is transmitted to a cloud-based platform for analysis using advanced machine learning algorithms. MQ-135 detects harmful gases, and DHT11 measures humidity and transmits data to the Arduino UNO. The HC-SR04 sensor measures object distances by emitting ultrasonic waves and detecting their echoes, aiding in obstacle detection. The NEO-6M GPS with GSM SIM900 modules transmit location data and emergency alerts via the GSM network, enabling responses to potential dangers. Simulation via Proteus validates the robot’s transceiver connectivity, mobility, and sensing functions. To enhance monitoring precision, the system adopts XGBoost, which classifies mine conditions, and the training model achieves 96.77% accuracy with high precision and recall. The system with Azure Machine Learning improves detection accuracy, raising temperature, CO, NH₄, and NO₂ precision by 7.25%, 15%, 17%, and 18%, respectively. Thus, the system features an intelligent alert mechanism to notify users of emergencies, enhancing worker safety and minimizing health-related risks in mining operations.
Spatial-channel reconstruction for efficient multiscale attention in robotic object detection Mohammed Maiza; Chahira Cherif; Samira Chouraqui; Abdelmalik Taleb-Ahmed
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp544-552

Abstract

Real-time object detection is a core capability for autonomous robots, unmanned aerial vehicles (UAVs), and self-driving systems operating in resource-constrained environments. This paper presents spatial-channel enhanced multiscale attention (SCEMA), a novel lightweight attention module designed to enhance robotic perception while minimizing computational overhead for embedded deployment. SCEMA employs a parallel dual-branch architecture that synergistically combines spatial-channel reconstruction with multiscale attention mechanisms. When integrated into the YOLOv8n framework (3.01M baseline parameters), the proposed YOLO-SCEMA model achieves significant performance gains across multiple challenging benchmarks relevant to robotics automation. Experiments on an NVIDIA RTX 4080 GPU demonstrate that on the ExDark dataset, YOLO-SCEMA improves mAP@50 by 7.37% over the baseline (69.07% to 76.44%) while reducing parameters by 36.88% (3.01M to 1.90M) and computational cost by 8.64% (8.1 to 7.4 GFLOPs). Consistent improvements are also observed on VisDrone2019 (+3.24% mAP@50) and FYP (+1.50% mAP@50) datasets. Comparative analysis demonstrates that YOLOSCEMA achieves superior accuracy-efficiency trade-offs, making it particularly suitable for deployment in low-light conditions, dense scenes, and complex structural environments for autonomous navigation, robotic surveillance, and industrial automation applications.
Performance assessment of fractional order PID control for a 2DOF flexible joint robotic manipulator under various operating conditions Ahmed Alkamachi; Ali Hussien Mary
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp597-606

Abstract

Flexible joint robot manipulators (FJM) exhibit high nonlinear dynamics and coupling effects, which make their control a challenging task. This paper proposes a fractional order PID (FOPID) cascade control strategy for a 2DOF FJM. The controller performance is compared with that of a sliding mode controller (SMC) under identical operating conditions. Both controllers’ parameters are optimally tuned using the particle swarm optimization (PSO) algorithm to ensure a fair performance evaluation. The proposed control consists of an inner loop that regulates motor dynamics and an outer loop that ensures accurate link position tracking. Additionally, a gravity compensator is integrated to improve control efficiency by reducing the nonlinear load on the controller. Both controllers are evaluated under nominal conditions, external disturbances, and variable payloads. Numerical results show a performance trade-off: under nominal conditions, FOPID achieved superior tracking performance (ITAE = 0.1165 for link 1) compared to SMC (ITAE = 0.2423), while SMC provided a smooth, zero-overshoot response and consumed much less actuator energy (ISCE = 23.35 vs. FOPID's 154.78). Furthermore, when subjected to a severe 1.0 N.m external disturbance, FOPID showed superb robustness by maintaining an ITAE of 0.178, whereas SMC suffered severe performance degradation. These findings illustrate the trade-offs between energy efficiency and robust precision in FJM control.
DIETARY-GDM: AI-powered dual-source framework for personalized dietary recommendation in gestational diabetes management Karthick Myilvahanan Jothivel; Arun Madhan; Sathishkumar Krishnaveni; Suganya Arumugam; Kamalakannan Subbiah; Dharmaraj Thalaihatty Belli
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp709-719

Abstract

Gestational diabetes mellitus (GDM) is a condition of glucose intolerance that occurs during pregnancy, characterized by elevated blood glucose levels. However, the existing approach was validated on limited datasets, which may affect its generalizability to diverse real-world clinical settings, and its real-time deployment feasibility has not been thoroughly examined. In this research, a novel DIETARY-GDM (DIETARY food recommendation for GDM) has been proposed for managing diabetes in pregnant women through personalized diet food recommendations. Wavelet denoising and text cleaning are employed to remove noise in the signal and user input to enhance the quality. The enhanced signals and text are then extracted using the convolutional neural network-long short-term memory (CNN-LSTM) and universal sentence encoding (USE) to effectively analyze the body condition of the pregnant women. A deep neural network (DNN) integrated with a Bayesian optimization algorithm (BOA) is utilized to predict personalized dietary food recommendations. Finally, an artificial intelligence (AI)-BOT generates food suggestions for pregnant women to improve maternal health. From the experimental results, the DIETARY-GDM achieves an accuracy of 99.21%. The accuracy of the DIETARY-GDM was higher than that of the convolutional neural network (CNN), artificial neural network (ANN), gated recurrent unit (GRU), and long short-term memory (LSTM) by 7.98%, 5.65%, 3.35%, and 1.97%, respectively.
Human state digital twin architecture for physiology constrained adaptive robotic autonomy Jaganathan Nirmaladevi; S. Saranya; Nidhi Mishra; Fazal Noorbasha; Manoharan Kavitha
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp503-518

Abstract

Time-dependent human cognitive and physical conditions should be considered in adaptive autonomy in collaborative robotics to ensure safety, performance, and operator comfort. But most of the current methods depend on heuristic levels of workload or individual physiological measures and fail to combine human-state estimation with the control structure. This paper suggests a human state digital twin (HSDT) framework of physiological-constrained adaptive robotic autonomy. This framework is a combination of multimodal physiological measures of electrocardiography-based heart-rate variability, electromyography, electrodermal activity, and task context to predict latent human states such as cognitive workload, fatigue, and stress. These estimates are included in a twin-constrained adaptive autonomy controller, which varies the control authority of the robot in real time. The simulated collaborative manipulation situation is tested in the proposed framework in different workload and fatigue conditions. It has demonstrated better tracking performance, less torque ripple, reduced operator stress indicators and less torque ripple. The paper presents a controlled and reconfigurable physiology-aware adaptive autonomy control architecture and a robotics-based digital twin control design framework of human-aware control design.
PIKER-NET: Multi-class retinal disease classification using Pied Kingfisher optimization-based improved residual network Lissy Devasahayam; Ramya Devi Murugadasan; Anandhi Samuel Vijayalakshmi; Chanthiya Puhalenthi; Ramnath Muthusamy; Ahilan Appathurai; Natarajan Mohana Suganthi
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp678-689

Abstract

Retinal diseases are vision-threatening conditions, including age-related macular degeneration (ARMD), diabetic retinopathy (DR), and glaucoma, that require early and accurate detection to prevent blindness. However, existing methods often struggle with limited feature representation, high inter-class similarity, intra-class variability, and reduced performance in handling noisy and low-quality retinal images. To address these challenges, a novel PIKER-NET framework is proposed for accurate multi-class retinal disease classification. The input fundus images from the RFMiD dataset are pre-processed using a scalable range adaptive bilateral (SCRAB) filter to enhance image clarity by preserving edges while reducing noise. The Improved Residual Network-Rescaled (ImResNet-RS) integrated with Temporal Attention is then employed to extract deep hierarchical features with enhanced discriminative power. Pied Kingfisher Optimization (PKO) algorithm is utilized for feature selection, effectively reducing redundant information while retaining the most relevant features. Residual Multilayer Perceptron (ResMLP) is used to classify retinal diseases into ARMD, branch retinal vein occlusion (BRVO), diabetic neuropathy (DN), DR, healthy, and myopia (MYA). The PIKER-NET achieves an overall accuracy of 98.14% and F1-score of 97.06%. The PIKER-NET approach improves overall accuracy by 3.24%, 4.24%, 6.23%, and 2.00% compared to EyeDeep-Net, IDL-MRDD, DeepDiabetic, and VisionDeep-AI, respectively. The proposed approach has strong clinical relevance by supporting earlier disease screening, reducing misdiagnosis, and enabling faster diagnosis to assist ophthalmologists in improving patient outcomes.
Consensus-based path planning for UAV swarms under multiple constraints: A review Yana Lu; Lianpeng Li; Hui Zhao; Xu Zhao
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp621-638

Abstract

UAV swarms are essential for emergency response, logistics, reconnaissance, and environmental monitoring, yet achieving safe and scalable path planning under dynamic conditions and complex constraints remains challenging. Unlike existing surveys that categorize algorithms by theoretical foundations, this paper systematically reviews UAV swarm path planning through the lens of spatial, temporal, and task-level consistency constraints. We classify recent advances into classical path search, intelligent optimization, and deep reinforcement learning, emphasizing how each addresses geometric continuity, behavioral coordination, and full-chain perception–decision–planning consistency under multi-constraint coupling. We further identify critical limitations in scalability, dynamic adaptability, and heterogeneous swarm cooperation, and outline future directions, including distributed control, multi-source perception fusion, cross‑platform collaboration, and robust autonomous decision-making. This review provides a unique, application‑centric taxonomy based on consensus constraints, offering actionable insights for developing consistency-aware UAV swarm path planning technologies.

Page 53 of 54 | Total Record : 533

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