cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tae Jin Park
Contact Email
iaes.ijra@gmail.com
Phone
-
Journal Mail Official
iaes.ijra@gmail.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 470 Documents
 42   43   44   45   46 
LoRa-enabled remote-controlled surveillance robot for monitoring and navigation in disaster response missions Gehlot, Anita; Singh, Rajesh; Mahala, Rahul; Gupta, Mahim Raj; Singh, Vivek Kumar
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp311-321

Abstract

Rescue missions must be conducted within a strict timeframe, and the safety of all rescuers and civilians is prioritized. The proposed system aims to design a remote-operated aerial surveillance robot for disaster-affected areas for search and rescue missions. Real-time video transmission and RS-232 long-range communication enable operators to navigate rough environments and monitor data collected in real-time. This powerful tool ensures the protection of human life while collecting accurate and meaningful data. Cloud storage for data and surveillance strengthens the system, preventing part failure and fostering collaboration among users. This is a significant step towards using Internet of Things systems alongside remote-controlled robots in disaster response. The robot's key contribution to disaster management is identifying the environment, addressing issues of no visibility, complicated terrains, and speed. Its modification and expansion capabilities make it useful in armed surveillance, industrial monitoring, and environmental studies, making it an important innovation for many other fields.
Experimental evaluation of bidirectional encoder representations from transformers models for de-identification of clinical document images Sriram, Ravichandra; Sundaram, Siva Sathya; Sophie, S. LourduMarie
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp273-280

Abstract

Many health institutes maintain patients’ diagnosis and treatment reports as scanned images. For healthcare analytics and research, large volumes of digitally stored patient information have to be accessed, but the privacy requirements of protected health information (PHI) limit the research opportunities. Particularly in this artificial intelligence (AI) era, deep learning models require large datasets for training purposes, which hospitals cannot share unless the PHI fields are de-identified. Manual de-identification is beyond possible, with millions of patient records generated in hospitals every day. Hence, this work aims to automate the de-identification of clinical document images utilizing AI models, particularly pre-trained bidirectional encoder representations from transformers (BERT) models. For the purpose of experimentation, a synthetic dataset of 550 clinical document images was generated, encompassing data obtained from diverse patients across multiple hospitals. This work presents a two-stage transfer learning approach, initially employing Tesseract character recognition (OCR) to convert clinical document images into text. Subsequently, it extracts PHI fields from the text for de-identification. For the purpose of extraction, BERT models were utilized; in this work, we contrasted six pre-trained versions of such models to examine their effectiveness and achieve the F1 score of 92.45%, thus showing better potential for de-identifying PHI data in clinical documents.
Comparative insights into nonlinear PID-based controller design approaches for industrial applications Syed Salim, Syed Najib; Rahmat, Mohd Fua’ad; Abdullah, Lokman; Shamsudin, Shamsul Anuar; Kamaludin, Khairun Najmi; Ibrahim, Mazree
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp191-203

Abstract

Proportional-integral-derivative (PID) controllers are established in manufacturing due to their simple design, robustness, and wide-ranging industrial applications. However, traditional PID controllers often struggle with the complexity and nonlinearity behaviors inherent in many control systems. As a result, ongoing and future research is focused on developing more stable PID controllers that function efficiently without heavily depending on exact mathematical models, by fine-tuning controller parameters. This study explores several PID-based controllers, including non-linear PID (N-PID), multi-rate non-linear PID (MN-PID), and self-regulating nonlinear PID (SN-PID), assessing and contrasting their performance. The efficacy and robustness of these control mechanisms are substantiated through comparative analyses with the sliding mode control technique, employing experimental data from a pneumatic actuator system to assess performance across varying load scenarios. SN-PID outperforms sliding mode controller (SMC) by 90.97% and PID by 89.90%, followed by MN-PID (85.58% over SMC, 83.86% over PID) and N-PID (78.08% over SMC, 75.49% over PID), while PID offers only 10.63% improvement over SMC. These findings provide valuable insights and recommendations for enhancing controller performance. These insights aim to guide control engineers in selecting the most appropriate N-PID design strategy for specific applications, ultimately improving system performance and operational efficiency in industrial environments.
Multi-robot coverage algorithm in complex terrain based on improved bio-inspired neural network Zhang, Fangfang; Duan, Mengdie; Xin, Jianbin; Peng, Jinzhu
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp348-360

Abstract

Biological neural network (BNN) algorithms have become popular in coverage search in recent years. However, its edge activity values are weak, and it is simple to fall into a local optimum at a late stage of coverage. When applied to complex environments, the 3D BNN network structure has high computational and storage complexity. In order to solve the above problems, we propose an algorithm for multi-robot cooperative coverage of complex terrain based on an improved BNN. The algorithm models the complex terrain using a 2.5-dimensional (2.5D) elevation map. Combining the dual-layer BNN network with the 2.5D elevation map, we propose an elevation value priority mechanism. This mechanism lets the robot make elevation-based decisions and prioritizes higher terrain areas. The dual neural network's first layer plans the robot's path in normal mode. The second network layer helps the robot escape the local optimum. Finally, the algorithm's full coverage effect in complex terrains and the speed of covering high terrain are verified by simulations. The experiments show that our algorithm preferentially covers high points of the region and eventually covers 100% of complex terrain. Compared with other algorithms, our algorithm covers more efficiently and takes fewer steps than others. The speed of covering high terrain areas has increased by 34.51%.
Deep-feed: An Internet of things-enabled smart feeding system for pets powered by deep learning Kotwal, Jameer; Surana, Amruta; Adke, Pallavi; Pawar, Krunal; Shaikh, Asma; Khan, Vajid
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp227-236

Abstract

Internet of things (IoT) encompasses a variety of connected devices and technologies designed to improve care, monitoring, and management of pets. IoT technology enables voice or app-driven control of these feeders, allowing pet guardians to remotely dispense food to their pets anytime. In this paper, a novel deep-feed network has been proposed that combines Image and sensor data classification. The inputs, such as camera (images) and sensor data, are sent to the preprocessing stages, where images are preprocessed using a Bilateral filter, and the data using preprocessing techniques such as tokenization, lemmatization, etc. The preprocessed images are sent to the neural network, like a convolutional neural network (CNN) for image classification and a bidirectional gated recurrent unit (BiGRU) to predict the dog's behavior. Next, these two networks are fused, and the fuzzy concept identifies whether the dogs are near the food or not in a cage. If the dog is near the food cage, the control unit will allocate the food and water through the water pump in the dog cage. Then the control unit gives the order to fill the food and water pumps and alerts the user to identify the food in a cage via the Blynk application. The accuracy of the suggested method can reach 99.95%, compared to 84.9%, 87.58%, and 93.91% for conventional models like the cat's monitoring and feeding systems via IoT (CMFSVI), petification, and global system for mobile communications/general packet radio service (GSM/GPRS). In comparison to the current approaches, the accuracy of the suggested methodology increased by 16.09%, 13.8%, and 3.75%, for existing models like CMFSVI, petification, and GSM/GPRS, respectively.
Camera-based simultaneous localization and mapping: methods, camera types, and deep learning trends Dwimantara, Anak Agung Ngurah Bagus; Natan, Oskar; Indarto, Novelio Putra; Dharmawan, Andi
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp162-172

Abstract

The development of simultaneous localization and mapping (SLAM) technology is crucial for advancing autonomous systems in robotics and navigation. However, camera-based SLAM systems face significant challenges in accuracy, robustness, and computational efficiency, particularly under conditions of environmental variability, dynamic scenes, and hardware limitations. This paper provides a comprehensive review of camera-based SLAM methodologies, focusing on their different approaches for pose estimation, map reconstruction, and camera type. The application of deep learning also will be discussed on how it is expected to improve performance. The objective of this paper is to advance the understanding of camera-based SLAM systems and to provide a foundation for future innovations in robust, efficient, and adaptable SLAM solutions. Additionally, it offers pertinent references and insights for the design and implementation of next-generation SLAM systems across various applications.
Hybrid deep learning and active contour for segmenting hazy images Ahmad Khairul Anuar, Firhan Azri; Jone, Jenevy; Raja Azhar, Raja Farhatul Aiesya; Jumaat, Abdul Kadir
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp429-437

Abstract

Image segmentation seeks to distinguish the foreground from the background for further analysis. A recent study presented a new active contour model (ACM) for image segmentation, termed Gaussian regularization selective segmentation (GRSS). This interactive ACM is effective for segmenting certain objects in images. However, a weakness of the GRSS model becomes apparent when utilized on hazy images, as it is not intended for such conditions and produces inadequate outcomes. This paper introduces a new ACM for segmenting hazy images that hybridizes a pretrained deep learning model, namely DehazeNet, with the GRSS model. Specifically, the haze-free images are estimated using DehazeNet, which fuses the information with the GRSS model. The new formulation, designated as GRSS with DehazeNet (GDN), is addressed via the calculus of variations and executed in MATLAB software. The segmentation accuracy was evaluated by calculating Error, Jaccard, and Dice metrics, while efficiency was determined by measuring processing time. Despite the increased processing time, numerical experiments demonstrated that the GDN model achieved higher accuracy, as indicated by the lower error and higher Jaccard and Dice than the GRSS model. The GDN model can potentially be formulated in the vector-valued image domain in the future.
Design and implementation of Internet of Things-enabled long-range autonomous surveillance bot for LPG leak detection and environmental safety monitoring Singh, Rajesh; Gehlot, Anita; Mahala, Rahul; Singh, Vivek Kumar
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp361-369

Abstract

Liquefied petroleum gas (LPG) accidents pose significant safety risks, requiring continuous monitoring and Internet of Things (IoT) technology to prevent gas leakage and ensure human safety. This work proposes distributed field-oriented IoT gas sensing robots for detecting dangerous flammable gases like Ammonia, Sulphur Dioxide, Nitrogen Dioxide, and Carbon Dioxide. The SnoLURk solution enables cost-effective IoT gas leak detection in indoor and outdoor robots using budget-friendly casings and sensors. The study also discusses a robotic system for gas leak detection, aiming to detect and combat burglary using ZigBee and GSM modules. Cloud support allows Wi-Fi zone residents to receive alerts and send investigators via email, enabling remote data analytics monitoring. The IoT-based Worker's Health Monitoring System improves health and safety practices in industrial environments by monitoring workers' health 24/7. It allows on-site and off-site monitoring, enabling quick intervention and avoiding complications. The system's applications include construction, mining, manufacturing, and healthcare. Future versions may include improved sensors and machine learning.
Multi-microcontroller system for Mecanum robots with gripper-shooter mechanisms Mareai, Mohammed; Wildan, Juhen Fashikha; Afkarean, Mochamad Ridho
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp181-190

Abstract

This study presents the design and implementation of a multi-microcontroller digital control system for a Mecanum-wheeled robot with gripper and shooter mechanisms, tailored for agricultural applications. The proposed system integrates an Espressif32 master controller with Arduino Nano slave microcontrollers, enabling precise control of robot movement and functional components. Wireless control is facilitated by a PlayStation 3 controller, while Mecanum wheels ensure omnidirectional mobility in dynamic environments. Experimental results indicate a 66.67% success rate in seedling planting and an 83.33% success rate in ball collection tasks. Despite its notable performance, enhancements in sensor feedback and automation are recommended to improve efficiency. This research underscores the potential of cost-effective, multi-microcontroller systems for advancing real-time control and task execution in agricultural robotics.
A new era of technological change in the restaurant industry: focusing on perceived values of robot servers Hwang, Jinsoo; Joo, Kyuhyeon; Moon, Joonho
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp143-150

Abstract

The objective of this research is to examine the perceived values of robot servers, which include utilitarian and hedonic values, and how this influences willingness to pay more in the restaurant industry. This paper also examined the differences between the two sub-dimensions of perceived value, which are based on the demographic factors of the respondents. This research performed a data analysis based on a sample size of 295 participants, and the results indicated that the two sub-dimensions of perceived value play a crucial role in regard to the formation of willingness to pay more. Furthermore, the results showed that there were differences in perceived value in regard to the demographic factors.

Page 44 of 47 | Total Record : 470

 42   43   44   45   46 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue Vol 14, No 3: December 2025 Vol 14, No 3: September 2025 Vol 14, No 2: June 2025 Vol 14, No 1: March 2025 Vol 13, No 4: December 2024 Vol 13, No 3: September 2024 Vol 13, No 2: June 2024 Vol 13, No 1: March 2024 Vol 12, No 4: December 2023 Vol 12, No 3: September 2023 Vol 12, No 2: June 2023 Vol 12, No 1: March 2023 Vol 11, No 4: December 2022 Vol 11, No 3: September 2022 Vol 11, No 2: June 2022 Vol 11, No 1: March 2022 Vol 10, No 4: December 2021 Vol 10, No 3: September 2021 Vol 10, No 2: June 2021 Vol 10, No 1: March 2021 Vol 9, No 4: December 2020 Vol 9, No 3: September 2020 Vol 9, No 2: June 2020 Vol 9, No 1: March 2020 Vol 8, No 4: December 2019 Vol 8, No 3: September 2019 Vol 8, No 2: June 2019 Vol 8, No 1: March 2019 Vol 7, No 4: December 2018 Vol 7, No 3: September 2018 Vol 7, No 2: June 2018 Vol 7, No 1: March 2018 Vol 6, No 4: December 2017 Vol 6, No 3: September 2017 Vol 6, No 2: June 2017 Vol 6, No 1: March 2017 Vol 5, No 4: December 2016 Vol 5, No 3: September 2016 Vol 5, No 2: June 2016 Vol 5, No 1: March 2016 Vol 4, No 4: December 2015 Vol 4, No 3: September 2015 Vol 4, No 2: June 2015 Vol 4, No 1: March 2015 Vol 3, No 4: December 2014 Vol 3, No 3: September 2014 Vol 3, No 2: June 2014 Vol 3, No 1: March 2014 Vol 2, No 4: December 2013 Vol 2, No 3: September 2013 Vol 2, No 2: June 2013 Vol 2, No 1: March 2013 Vol 1, No 4: December 2012 Vol 1, No 3: September 2012 Vol 1, No 2: June 2012 Vol 1, No 1: March 2012 More Issue