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.
Articles
470 Documents
<![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[Designing high power efficient finite impulse response filters with three-four inexact adder-integrated Booth multiplier ]]>
<![CDATA[Kollannur, Manju Inasu]]>;
<![CDATA[Souprayen, Oudaya Coumar]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i2.pp204-213
<![CDATA[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.]]>
<![CDATA[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 ]]>
<![CDATA[Dhingra, Akshaya]]>;
<![CDATA[Sindhu, Vikas]]>;
<![CDATA[Dhingra, Lakshay]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i2.pp248-259
<![CDATA[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.]]>
<![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.]]>
<![CDATA[Localization and mapping of autonomous wheel mobile robot using Google cartographer ]]>
<![CDATA[Hidayati, Qory]]>;
<![CDATA[Setyawan, Novendra]]>;
<![CDATA[Faruq, Amrul]]>;
<![CDATA[Irfan, Muhammad]]>;
<![CDATA[Kasan, Nur]]>;
<![CDATA[Yakub, Fitri]]>
<![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.pp322-331
<![CDATA[COVID-19 has become a world concern because of the spread and number of cases that have befallen the world. Medical workers are the first exposed group because they have direct contact with patients. So, a vehicle is needed to replace tasks such as logistics, delivery, and patient waste transportation. An autonomous wheeled mobile robot (AWMR) is a wheeled robot capable of moving freely from one place to another. AWMR is required to have good navigation and trajectory control skills. The purpose of this study is to develop an AWMR navigation system model based on the simultaneous localization and mapping (SLAM) algorithm, accurately in a dynamic environment. With this research, developing a good navigation and trajectory method for AWMR, in the future, it can be applied to produce an AWMR platform for multipurpose. This research was conducted in two stages of development. The first year is the research that is currently being carried out, focused on sensor modeling, designing SLAM-based navigation models, and making navigation system testbeds. This research produces a trajectory navigation and control system that can be implemented on an AWMR platform for the purposes of logistics, transportation, and patient waste in hospitals.]]>
<![CDATA[Design of H-/H∞ based fault detection filter for linear uncertain systems using linear matrix inequalities ]]>
<![CDATA[Ahmad, Masood]]>;
<![CDATA[Mohd-Mokhtar, Rosmiwati]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i2.pp214-226
<![CDATA[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.]]>
<![CDATA[SCADA system in water storage tanks with NI vision LabVIEW ]]>
<![CDATA[Kartika, Kartika]]>;
<![CDATA[Misriana, Misriana]]>;
<![CDATA[Naqi, M. Fathan]]>;
<![CDATA[Asran, Asran]]>;
<![CDATA[Jannah, Misbahul]]>;
<![CDATA[Hasibuan, Arnawan]]>;
<![CDATA[Suryati, Suryati]]>
<![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.pp381-392
<![CDATA[Advances in technology have driven the need for efficient water management systems. This study presents a SCADA-based water management system that integrates LabVIEW and Arduino to monitor and regulate water levels and flow rates in a storage tank. The system uses an HC-SRF04 ultrasonic sensor for water level measurement with 99.77% accuracy and an HX710 pressure sensor, which achieves 98.54% accuracy. The LabVIEW interface displays real-time data, giving users an intuitive view of system performance. A proportional integral derivative (PID) algorithm optimizes the water pump through pulse width modulation (PWM), achieving water flow rate control. The Ziegler-Nichols method tunes the PID parameters to Kp = 16.59, Ti = 1.102, and Td = 0.2755. This tuning ensures the system maintains a consistent target flow rate of 4 liters per minute (L/min) with minimal variation. Initial testing showed a 2.5% overshoot but stabilized at the desired flow rate within 10 seconds, indicating effective control. This SCADA system reduces water and energy waste by enabling continuous real-time monitoring and control. The system provides accurate data through a LabVIEW interface, ensuring effective and informed operational decisions. This robust solution supports efficient water management for industrial and environmental applications, contributing to sustainability and resource optimization.]]>
<![CDATA[Energy efficient clustering and routing method for Internet of Things ]]>
<![CDATA[Ahlawat, Bhawna]]>;
<![CDATA[Sangwan, Anil]]>
<![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.pp418-428
<![CDATA[The Internet of Things is crucial in monitoring environmental conditions in remote areas, but it faces significant challenges related to energy consumption, which affects network longevity and coverage. Clustering has proven effective in prolonging the life of sensor networks. Adaptive clustering in wireless sensor networks allows for more effective cluster organization via real-time rearranging of sensor nodes according to important parameters, which include energy levels and the distance between them. Fruit fly algorithm (FFA) and ant colony optimization (ACO) are emerging as encouraging techniques for creating clusters and establishing paths, respectively. This paper describes the use of the FFA to make the clustering process better by selecting the best cluster head and reducing energy consumption. This paper proposes a novel solution that integrates ACO for establishing paths with FFA for clustering. This method is tested in both homogeneous and heterogeneous settings using MATLAB, comparing its performance with two existing algorithms: low energy adaptive clustering hierarchy (LEACH) and biogeography-based optimization algorithm (BOA). According to the findings, the suggested algorithm performs noticeably better than BOA and LEACH in the context of coverage area and network service period, especially in heterogeneous settings.]]>
<![CDATA[A novel approach to enhance rice foliar disease detection: custom data generators, advanced augmentation, hybrid fine-tuning, and regularization techniques with DenseNet121 ]]>
<![CDATA[Subburaman, Govindarajan]]>;
<![CDATA[Selvadurai, Mary Vennila]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i2.pp237-247
<![CDATA[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.]]>
<![CDATA[Development and implementation of a mobile robot for grouting floor tile joints ]]>
<![CDATA[Abu Sneineh, Anees]]>;
<![CDATA[Salah, Wael A.]]>;
<![CDATA[Elnaggar, Mohamed]]>;
<![CDATA[Abuhelwa, Mai]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i2.pp151-161
<![CDATA[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.]]>
