JournalTOCs

IAES International Journal of Robotics and Automation
Number of Followers: 5

This is an Open Access Journal

Open Access journal
ISSN (Print) 2089-4856 - ISSN (Online) 2722-2586
Published by Institute of Advanced Engineering and Science

[11 journals]

A new era of technological change in the restaurant industry: focusing on
perceived values of robot servers
- Authors: Jinsoo Hwang, Kyuhyeon Joo, Joonho Moon
  Pages: 143 - 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp143-150
  Issue No: Vol. 14, No. 2 (2025)
Development and implementation of a mobile robot for grouting floor tile
joints
- Authors: Anees Abu Sneineh, Wael A. Salah, Mohamed Elnaggar, Mai Abuhelwa
  Pages: 151 - 161
  Abstract: Many construction tasks need time and effort from people. Thus, modern technology is one of its purposes to aid task completion. These include grouting floor tile joints. It takes time and effort to complete this process. Traditional methods for grouting floor tile joints between tiles are inefficient and require the worker to stay on his knees for extended periods, which can cause health issues. Thus, mobile robots are needed to automate floor grouting. This study describes the design and development of a mobile robot model to grout floor tile joints uniformly and effectively. Compared to manual approaches, the proposed robot can clean tiles quickly and precisely. The robot fills based on user-defined workspace coordinates. Set the robot at the start location to begin grouting. The robot then follows the user-defined code and coordinates to fill the requirement. After grout filling, the robot returned to the starting position to clean. This model was evaluated and exhibited faster, more accurate grouting and a shorter injection process than manual approaches.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp151-161
  Issue No: Vol. 14, No. 2 (2025)
Camera-based simultaneous localization and mapping: methods, camera types,
and deep learning trends
- Authors: Anak Agung Ngurah Bagus Dwimantara, Oskar Natan, Novelio Putra Indarto, Andi Dharmawan
  Pages: 162 - 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp162-172
  Issue No: Vol. 14, No. 2 (2025)
Understanding golfers’ acceptance behavior toward robotic golf caddies
by merging the task technology fits theory and the perceived risk theory
- Authors: Kyuhyeon Joo, Heather Markham Kim, Jinsoo Hwang
  Pages: 173 - 180
  Abstract: 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp173-180
  Issue No: Vol. 14, No. 2 (2025)
Multi-microcontroller system for Mecanum robots with gripper-shooter
mechanisms
- Authors: Mohammed Mareai, Juhen Fashikha Wildan, Mochamad Ridho Afkarean
  Pages: 181 - 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp181-190
  Issue No: Vol. 14, No. 2 (2025)
Comparative insights into nonlinear PID-based controller design approaches
for industrial applications
- Authors: Syed Najib Syed Salim, Mohd Fua’ad Rahmat, Lokman Abdullah, Shamsul Anuar Shamsudin, Khairun Najmi Kamaludin, Mazree Ibrahim
  Pages: 191 - 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp191-203
  Issue No: Vol. 14, No. 2 (2025)
Designing high power efficient finite impulse response filters with
three-four inexact adder-integrated Booth multiplier
- Authors: Manju Inasu Kollannur, Oudaya Coumar Souprayen
  Pages: 204 - 213
  Abstract: Finite impulse response (FIR) filters are widely utilized in several applications in digital signal processing, including data transmission, photography, digital audio, and biomedicine. It is necessary to use high sample rates for FIR filters, while moderate sample rates are needed for low-power circuits. To solve these problems, a Booth multiplier based on three-four inexact adder-based multiplication (TFIE-BM) was proposed. The goal of the proposed TFIE-based FIR Booth multiplier is to lower area usage, latency, and power consumption. The proposed method utilizes the spotted hyena optimizer (SHO) to find the optimal filter coefficient (FC) by minimizing the pass power consumption and Transition bandwidth. Moreover, a high-performance three-four inexact adder (TIFE adder) has been introduced, which uses fewer XOR gates for sum and carry generation, indicating that the logic has been simplified to reduce hardware complexity. By increasing speed and decreasing the FIR filter's critical path delay, a modified Booth multiplier that uses a 5:2 compressor is introduced. The overall delay of the proposed approach is 23.4%, 18.7%, 12.3%, and 5.7% lower than that of the Radix-4 Booth multiplier, CSA Booth multiplier, hybrid multiplier, and traditional Booth multiplier, respectively.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp204-213
  Issue No: Vol. 14, No. 2 (2025)
Design of H-/H∞ based fault detection filter for linear uncertain
systems using linear matrix inequalities
- Authors: Masood Ahmad, Rosmiwati Mohd-Mokhtar
  Pages: 214 - 226
  Abstract: One of the significant challenges in model-based fault detection is achieving robustness against disturbances and model uncertainties while ensuring sensitivity to faults. This study proposes an optimized approach for designing fault detection filters for discrete-time linear systems with norm-bounded model uncertainties. The design leverages the H-/H∞ optimization framework and is expressed through linear matrix inequality constraints. The filter is designed to produce a residual signal that balances two opposing objectives: minimizing the impact of disturbances and model uncertainties while maximizing fault sensitivity. The effectiveness of the proposed method is demonstrated through simulations involving sensor and actuator fault detection in the well-known three-tank system. Simulation results illustrate the method's ability to maintain robustness against disturbances and uncertainties while effectively detecting faults in the system.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp214-226
  Issue No: Vol. 14, No. 2 (2025)
Deep-feed: An Internet of things-enabled smart feeding system for pets
powered by deep learning
- Authors: Jameer Kotwal, Amruta Surana, Pallavi Adke, Krunal Pawar, Asma Shaikh, Vajid Khan
  Pages: 227 - 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp227-236
  Issue No: Vol. 14, No. 2 (2025)
A novel approach to enhance rice foliar disease detection: custom data
generators, advanced augmentation, hybrid fine-tuning, and regularization
techniques with DenseNet121
- Authors: Govindarajan Subburaman, Mary Vennila Selvadurai
  Pages: 237 - 247
  Abstract: Rice leaf diseases impact crop yield, leading to food shortages and economic losses. Early, automated detection is essential but often hindered by accuracy challenges. This study contributes to improving model robustness against diverse and adversarial inputs by proposing a custom data generator that applies Albumentation-based advanced augmentations, such as Gaussian blur, noise addition, brightness/contrast adjustments, and coarse dropout, to enhance model generalization. Five deep learning architectures—simple convolutional neural network (CNN), ResNet50, EfficientNetB0, Inception v3, and DenseNet121—were evaluated for classifying six categories: bacterial blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaf. A hybrid model approach is proposed, fine-tuning the DenseNet121 model by unfreezing its last 20 layers, which balances transfer learning benefits with domain-specific feature extraction. Regularization techniques, including L2 regularization and a reduced dropout rate, are incorporated to control overfitting. Additionally, a custom learning rate scheduler is proposed to promote stable training. DenseNet121 achieved the highest performance, with an accuracy of 98.41%, demonstrating the effectiveness of these advanced augmentation and tuning strategies in rice leaf disease classification.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp237-247
  Issue No: Vol. 14, No. 2 (2025)
NAPLAM: a novel ledger-based algorithm for detection and mitigation of
sinkhole attacks in routing protocol for low power and lossy
networks-based Internet of things
- Authors: Akshaya Dhingra, Vikas Sindhu, Lakshay Dhingra
  Pages: 248 - 259
  Abstract: The Internet of Things (IoT) is a network of connected physical objects that collect and share data over the Internet. However, routing attacks can disrupt data exchange, especially multi-node sinkhole attacks in low power and lossy IoT networks (LLNs). To support communication in LLN IoT, the IPv6-based routing protocol for LLNs (RPL) is used. Despite having several advantages, RPL also faces challenges like being vulnerable to attacks, having limited resources, compatibility, and scalability issues. Additionally, traditional security methods often do not work well for LLN-IoT devices because they lack the necessary computing power. To overcome these challenges, we have proposed a novel ledger-based framework called network and packet ledger to ascertain malicious devices using routing protocol for LLN (NAPLAM-RPL). This framework can effectively detect and mitigate multi-node sinkhole attacks in IoT networks. This paper also compares NAPLAM-RPL with similar protocols using the NetSim Simulator. The experimental analysis shows that NAPLAM-RPL improves network performance and outperforms existing methods like RF-trust, SoS-RPL, INTI, C-TRUST, and heartbeat algorithm in crucial areas, including packet delivery rate (PDR), throughput, End-to-End (E2E) delay, energy consumed, and detection accuracy.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp248-259
  Issue No: Vol. 14, No. 2 (2025)
CP_SDUNet: road extraction using SDUNet and centerline preserving dice
loss
- Authors: Bayu Satria Persada, Muhammad Rifqi Priyo Susanto, Laksmita Rahadianti, Aniati Murni Arymurthy
  Pages: 260 - 272
  Abstract: 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp260-272
  Issue No: Vol. 14, No. 2 (2025)
Experimental evaluation of bidirectional encoder representations from
transformers models for de-identification of clinical document images
- Authors: Ravichandra Sriram, Siva Sathya Sundaram, S. LourduMarie Sophie
  Pages: 273 - 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp273-280
  Issue No: Vol. 14, No. 2 (2025)
IoT-Based Cricket Environment System to Maximize Egg Production and Reduce
Mortality Rate
- Authors: Dominic Miracle Tjandrata, Suryadiputra Liawatimena
  Pages: 281 - 289
  Abstract: 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.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp281-289
  Issue No: Vol. 14, No. 2 (2025)
Internet of Things-enabled smart robotic baggage monitoring and tracking
system for enhanced traveler convenience and security
- Authors: Anita Gehlot, Rajesh Singh, Rahul Mahala, Vivek Kumar Singh, Mahim Raj Gupta
  Pages: 290 - 300
  Abstract: Baggage travel is a significant issue, causing inconveniences and financial losses for travellers. The rise in efficient international and domestic travel has led to the need for live baggage tracking systems. Traditional methods, such as manual tracking and locks, are inefficient and counterproductive due to power limitations. IoT has revolutionized baggage management by providing real-time tracking feedback and enhancing security. IoT-enhanced smart luggage systems use biometric locks, GPS tracking, and smart locking mechanisms to prevent theft and unauthorized usage. Geofencing allows users to draw boundaries for luggage, and smart luggage systems can adapt to airport security requirements. Some smart suitcases also have self-following features, allowing travellers to have better control over their bags. IoT-enabled baggage solutions also improve airport and travel centre efficiency. RFID and barcode identification devices enable airline employees to quickly recognize, monitor, and manage luggage, reducing waiting times and loss risks. Cloud-based systems allow users to remember their luggage and receive travel suggestions based on predicted frequency of use. IoT-enabled baggage management systems have the potential to transform airport ecosystems into smarter ones through automated tracking with minimal human involvement and errors. AI and machine learning can also proactively address concerns and improve the overall customer journey.
  PubDate: 2025-06-01
  DOI: 10.11591/ijra.v14i2.pp290-300
  Issue No: Vol. 14, No. 2 (2025)

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