IAES International Journal of Robotics and Automation (IJRA)
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.
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<![CDATA[Faraid distribution calculation using AI-based Quranic chatbot ]]>
<![CDATA[Md Zin, Iman Hafizi]]>;
<![CDATA[Mansor, Nur Farraliza]]>;
<![CDATA[Mat Diah, Norizan]]>;
<![CDATA[Hashim, Shakirah]]>;
<![CDATA[Mansor, Mastura]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp393-406
<![CDATA[Faraid, Islamic inheritance law, refers to that aspect of Shariah law which is not properly understood and has created issues and impediments in the distribution of estates. This paper discusses the development of an AI-based Quranic chatbot to be used by the public to learn the Faraid rules and automate calculations of inheritance distribution. The chatbot has been developed using natural language processing and a rule-based algorithm, which intends to search and get an accurate interpretation from the user queries, retrieve relevant verses of the Quran, and compute the share of inheritance according to the established Islamic jurisprudence. Fuzzy match identifies and corrects variation in queries, enhancing user interaction, ensuring that it appears more intuitive and accessible. The system processes user input regarding heirs of the deceased, estate value, and debts, and applies Faraid rules to generate accurate distribution results that happen to be web-based platforms of this chatbot. It intends to link traditional Islamic knowledge with modern digital solutions, bringing Faraid calculations closer, more comfortable, faster, and transparent. Through rigorous tests and user feedback will prove above, revealing the chatbot’s potential in understanding the application of Islamic inheritance law and promoting digital engagement in all these through Quranic teachings.]]>
<![CDATA[Robotic mist bath wheelchair: innovations in automated body drying and sanitization for improved patient hygiene ]]>
<![CDATA[Mane, Vijay Mahadeo]]>;
<![CDATA[Durge, Harshal Ambadas]]>;
<![CDATA[Shieh, Chin-Shiuh]]>;
<![CDATA[Dey, Rajesh]]>;
<![CDATA[Mahajan, Rupali Atul]]>;
<![CDATA[Bhorge, Siddharth]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp301-310
<![CDATA[This paper presents the development and evaluation of the robotic mist bath wheelchair (MBWC), a multifunctional assistive device designed to enhance hygiene and comfort for individuals with limited mobility. The MBWC integrates mist-based bathing, automated sanitization, and warm air-drying into a compact, wheelchair-mounted system suitable for home and clinical settings. Experimental evaluations demonstrated effective temperature maintenance and a 30% reduction in bathing time compared to conventional methods. User trials with 20 participants indicated a 92% satisfaction rate, reflecting improvements in hygiene, comfort, and operational ease. MBWC provides a cost-effective, hygienic alternative to traditional bathing methods, addressing critical challenges in eldercare and rehabilitation environments.]]>
<![CDATA[IntelliDrive autonomous robot powered by large language model ]]>
<![CDATA[Khan, Imran Ulla]]>;
<![CDATA[Raja, D. R. Kumar]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp339-347
<![CDATA[The rapid advancements in artificial intelligence (AI) and robotics have paved the way for innovative autonomous systems capable of performing complex tasks. This project integrates robotics with Large Language Models (LLMs) to develop an intelligent, versatile and user-friendly robotic system. The robot is designed to interpret structured commands, make real-time decisions, and navigate autonomously in dynamic environments, addressing key challenges faced by traditional autonomous systems. Central to the system is a Raspberry Pi 4, which serves as the main processing unit, integrating components such as a webcam for visual data capture, an L298N motor driver for motor control, and a Bluetooth speaker for real-time feedback. The LLM API enables the robot to process natural language commands, providing context-aware task execution and adaptability to changing scenarios. Testing has demonstrated the system’s ability to perform autonomous navigation, detect obstacles, and execute tasks effectively. This research offers a foundation for various industries, including logistics, healthcare, education, and hazardous environment operations. By incorporating LLMs the robot overcomes limitations of traditional rule-based systems, enhancing dynamic decision-making and user interaction. With its modular design and scalability, it bridges the gap between human-like intelligence and mechanical precision, setting the stage for future advancements in AI-driven robotics.]]>
<![CDATA[Disease detection on coconut tree using golden jackal optimization algorithm ]]>
<![CDATA[Ramaiah, Arun]]>;
<![CDATA[Shunmugathammal, Muthusamy]]>;
<![CDATA[Kalidindi, Hari Krishna]]>;
<![CDATA[Kumareson, Anish Pon Yamini]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp407-417
<![CDATA[Millions of people depend on coconut palms for their food and livelihoods, making them one of the most essential crops in tropical countries. However, Diseases may significantly reduce the output of coconut trees and possibly result in their death. To overcome this, a novel golden jackal optimized disease detection in COCOnut tree (GOD-COCO) has been proposed for detecting diseases in coconut trees. First, the input dataset images are pre-processed in pre-processing image rotation, image rescaling, and image resizing, and the enhanced images are gathered. The enhanced images are segmented using the PSP-Net. From the segmented images, the features are extracted using the Dense-Net. Then the features needed are selected using the golden jackal optimization algorithm (GJOA). Finally, the deep belief network (DBN) classifier classifies whether it is normal or abnormal. The experimental analysis of the proposed GOD-COC has been evaluated using the Plant Pathology datasets based on the accuracy, precision, and recall standards. By this, the proposed GOD-COCO achieves an accuracy rate of 99.31% and it achieves an overall accuracy rate of 0.77%, 0.31% and 1.17% by the existing methods such as AIE-CTDDC, DL-WDM, and CLS. Similarly, the proposed GOD-COCO model takes less time, 1.13 milliseconds to detect the disease, than the existing methods, which take 3.04, 2.5, and 2.67 milliseconds, respectively.]]>
<![CDATA[LoRa-enabled remote-controlled surveillance robot for monitoring and navigation in disaster response missions ]]>
<![CDATA[Gehlot, Anita]]>;
<![CDATA[Singh, Rajesh]]>;
<![CDATA[Mahala, Rahul]]>;
<![CDATA[Gupta, Mahim Raj]]>;
<![CDATA[Singh, Vivek Kumar]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp311-321
<![CDATA[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.]]>
<![CDATA[Multi-robot coverage algorithm in complex terrain based on improved bio-inspired neural network ]]>
<![CDATA[Zhang, Fangfang]]>;
<![CDATA[Duan, Mengdie]]>;
<![CDATA[Xin, Jianbin]]>;
<![CDATA[Peng, Jinzhu]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp348-360
<![CDATA[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%.]]>
<![CDATA[Hybrid deep learning and active contour for segmenting hazy images ]]>
<![CDATA[Ahmad Khairul Anuar, Firhan Azri]]>;
<![CDATA[Jone, Jenevy]]>;
<![CDATA[Raja Azhar, Raja Farhatul Aiesya]]>;
<![CDATA[Jumaat, Abdul Kadir]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp429-437
<![CDATA[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.]]>
<![CDATA[Design and implementation of Internet of Things-enabled long-range autonomous surveillance bot for LPG leak detection and environmental safety monitoring ]]>
<![CDATA[Singh, Rajesh]]>;
<![CDATA[Gehlot, Anita]]>;
<![CDATA[Mahala, Rahul]]>;
<![CDATA[Singh, Vivek Kumar]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp361-369
<![CDATA[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.]]>
<![CDATA[An Internet of Things based mobile-controlled robot with emergency parking system ]]>
<![CDATA[Kareem, Abdul]]>;
<![CDATA[Kumara, Varuna]]>;
<![CDATA[Shervegar, Vishwanath Madhava]]>;
<![CDATA[Shetty, Karthik S.]]>;
<![CDATA[Devadig, Manvith]]>;
<![CDATA[Shamma, Mahammad]]>;
<![CDATA[Maheshappa, Kiran]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp370-380
<![CDATA[This paper presents an Internet of Things (IoT) based mobile-controlled car with an emergency parking system that integrates advanced functionalities to enhance safety and user convenience, utilizing the ESP32 microcontroller as its core. The system allows users to control the car remotely via a mobile application, leveraging Wi-Fi connectivity for seamless communication. Key features include LED indicators for various operations such as reversing, left and right turns, and brake activation, ensuring clear signaling in real-time. The innovative emergency parking system detects obstacles or emergencies using sensors and halts the vehicle automatically, reducing the risk of accidents. The car's lightweight, energy-efficient design, combined with the versatility of the ESP32, ensures a responsive and reliable operation. Additionally, the system provides an intuitive user interface through the mobile app, enabling precise control and real-time feedback. The proposed system is faster in response compared to the existing systems. Moreover, the proposed system consumes less energy, and hence, it uses the battery more efficiently, extending the time of operation. Lower power consumption ensures longer operation time, reducing the need for frequent charging and making the system more practical. This paper demonstrates the integration of IoT and embedded systems to create a smart vehicle solution suitable for various applications, including robotics, automation, and personal transport. Its cost-effectiveness and scalability make it a viable choice for both hobbyists and developers.]]>
<![CDATA[Robot Gaussian-historical relocalization: inertial measurement unit-LiDAR likelihood field matching ]]>
<![CDATA[Shen, Ye-Ming]]>;
<![CDATA[Kang, Min]]>;
<![CDATA[Yang, Jia-Qiang]]>;
<![CDATA[Cai, Zhong-Hou]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp438-450
<![CDATA[Robot localization is a foundational technology for autonomous navigation, enabling task execution and adaptation to dynamic environments. However, failure to return to the correct pose after power loss or sudden displacement (the “kidnapping” problem) can lead to critical system failures. Existing methods often suffer from slow relocalization, high computational cost, and poor robustness to dynamic obstacles. We propose a novel inertial measurement unit (IMU)-LiDAR fusion relocalization framework based on Gaussian historical constraints and adaptive likelihood field matching. By incorporating IMU-derived yaw constraints and modeling historical poses within a 3σ Gaussian region, our method effectively narrows the LiDAR search space. Curvature and normal vector-based feature extraction reduces point cloud volume by 50–70%, while dynamic obstacle filtering via multi-frame differencing and neighborhood validation enhances robustness. An adaptive spiral search strategy further refines pose estimation. Compared to ORB-SLAM3 and adaptive Monte Carlo localization (AMCL), our method maintains comparable accuracy while significantly reducing relocalization time and CPU usage. Experimental results show a relocalization success rate of 84%, average time of 1.68 seconds, and CPU usage of 38.4%, demonstrating high efficiency and robustness in dynamic environments.]]>
