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[Design and implementation of QUADRESCUE: A ROS-based quadruped robot for disaster response support ]]>
<![CDATA[Deshmukh, Sanjay]]>;
<![CDATA[Chanakya, Ojas]]>;
<![CDATA[Gabani, Om]]>;
<![CDATA[Patni, Kashish]]>;
<![CDATA[Deshmukh, Asmita]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp387-398
<![CDATA[Search and rescue (SAR) operations in hazardous environments demand robotic systems capable of traversing complex terrains while ensuring responder safety. Traditional wheeled platforms often fail in debris-laden areas, and fully autonomous quadrupeds remain financially out of reach for many rescue agencies. This paper presents the design and development of QUADRESCUE, a modular operator-assisted quadruped robot built to bridge the gap between affordability and capability in disaster response. QUADRESCUE delivers core SAR functionalities including remote visual inspection, real-time terrain mapping via an RGB-D camera, payload transport, and GPS-based survivor localization. Built with a robust three degrees of freedom (3DoF) per leg design, the robot uses inverse kinematics algorithms to precisely control twelve servo motors for stable locomotion across uneven terrain. The system integrates the robot operating system (ROS) for seamless operation, real-time joystick control for easy navigation, an IMU for orientation sensing, and a GPS module with 3-meter accuracy. Field evaluations demonstrate 80–94% success rates on challenging surfaces, substantially outperforming wheeled counterparts 19% to 39% with a 200-meter control range and 45 minutes of runtime. QUADRESCUE offers a lightweight, cost-effective, and repairable solution that combines practical usability with advanced performance, making it well-suited for real-world deployment in emergency rescue situations.]]>
<![CDATA[A method integral sliding mode control to minimize chattering in sliding mode control of robot manipulator ]]>
<![CDATA[Nguyen, Mai Hoang]]>;
<![CDATA[Nguyen, Truc Thi Kim]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp345-355
<![CDATA[This paper presents an improved sliding mode control (SMC) strategy for robotic manipulators by introducing a novel exponential integral-based adaptive gain law, referred to as integral sliding mode control (ISMC). The proposed approach dynamically adjusts the switching gain KKK in real-time, based on the accumulated system error, thereby effectively reducing chattering while preserving system robustness. Unlike many existing methods, the ISMC strategy eliminates the need for state observers or complex estimation techniques, simplifying implementation. Theoretical analysis is provided using Lyapunov stability theory, ensuring global convergence. Simulation results on 2-DOF and 3-DOF robotic arms demonstrate superior tracking accuracy and smoother control signals compared to conventional SMC approaches. This work contributes a lightweight yet effective SMC enhancement with practical benefits for real-world robotic applications.]]>
<![CDATA[Edge-aware distilled segmentation with pseudo-label refinement for autonomous driving perception ]]>
<![CDATA[Indarto, Novelio Putra]]>;
<![CDATA[Natan, Oskar]]>;
<![CDATA[Dharmawan, Andi]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp376-386
<![CDATA[Achieving precise semantic segmentation is essential for enabling real-time perception in autonomous systems, yet leading approaches typically require substantial annotated data and powerful hardware, restricting their use on devices with limited resources. This work introduces an efficient segmentation framework that integrates pseudo-label refinement, knowledge distillation, and entropy-based confidence filtering to train compact student networks suitable for edge deployment. High-quality pseudo-labels are first produced by a robust teacher network, then further improved using a dense conditional random field to boost spatial consistency. An entropy-based selection mechanism removes unreliable predictions, ensuring that only the most trustworthy labels guide the student model's training. The use of knowledge distillation effectively transfers detailed semantic understanding from the teacher to the student, enhancing accuracy without added computational overhead. Experimental results with multiple EfficientNet backbones reveal that this pipeline improves segmentation accuracy and output clarity, while also supporting real-time or near real-time inference on CPUs with limited processing power. Extensive ablation and qualitative studies further confirm the method's robustness and flexibility for real-world edge applications.]]>
<![CDATA[Humanoid robot balance control system during backward walking using linear quadratic regulator ]]>
<![CDATA[Arsyi, Muhammad]]>;
<![CDATA[Dharmawan, Andi]]>;
<![CDATA[Sumbodo, Bakhtiar Alldino Ardi]]>;
<![CDATA[Auzan, Muhammad]]>;
<![CDATA[Istiyanto, Jazi Eko]]>;
<![CDATA[Natan, Oskar]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp320-330
<![CDATA[Humanoid robots are designed to replicate human activities, including tasks in hazardous environments. However, maintaining balance during backward walking remains a significant challenge due to center of mass (CoM) shifts beyond the support polygon and limited knee joint motion. This study proposes a control strategy that integrates a linear quadratic regulator (LQR) with optimized walking patterns to enhance dynamic stability. The approach combines LQR-based control with CoM trajectory planning to ensure safe and stable backward walking. The methodology includes inverse kinematics for generating walking patterns and the use of Inertial Measurement Unit (IMU) sensors to estimate the CoM trajectory. LQR parameters were tuned through simulation to improve responsiveness to disturbances. Evaluation metrics focused on CoM deviation, rise time, settling time, and overshoot. Experimental results demonstrate that the proposed LQR system effectively maintains the CoM within 5% of the support polygon boundary. The system achieved rise times under one second and settling times below two seconds, while minimizing pitch and roll overshoots. Compared to proportional control, the proposed method significantly improves stability and reduces the risk of falling. This research advances control strategies for humanoid robots, contributing to improved mobility and operational safety. Moreover, it supports Sustainable Development Goal (SDG) 9 by promoting innovation in intelligent robotic systems that can assist in complex or high-risk environments.]]>
<![CDATA[ADC-LIO: A direct LiDAR-inertial odometry method based on adaptive distortion covariance ]]>
<![CDATA[Yang, Lixiao]]>;
<![CDATA[Feng, Youbing]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp399-408
<![CDATA[Focusing on the localization challenges for robots in dynamic navigation environments, this study proposes a direct LiDAR-inertial odometry (LIO) system named ADC-LIO, which achieves robust pose estimation and accurate map reconstruction using adaptive distortion covariance. ADC-LIO is engineered to address uncertain motion patterns in autonomous mobile robots, effectively integrating LiDAR scan undistortion within the Kalman filtering update process by embedding an iterative smoothing process and a backpropagation strategy. The ADC-LIO architecture enhances point cloud accuracy, improving the system's overall performance and robustness. In addition, an adaptive covariance processing method is developed to resolve motion-induced sensing uncertainties, which calculates different covariances according to the error characteristics of the point cloud. This method enhances the constraints of high-quality point clouds, reduces the limitations on low-quality point clouds, and utilizes information more effectively. Experiments on the publicly available NTU-VIRAL dataset validate the effectiveness of ADC-LIO, which improves pose estimation accuracy and reduces absolute position errors compared to other state-of-the-art methods, including FAST-LIO, Faster-LIO, FR-LIO, and Point-LIO. The proposed ADC-LIO is an appealing odometry method that delivers accurate, real-time, and reliable tracking and map-building results, posing a practical solution for robotic applications in structured indoor and GPS-denied outdoor environments.]]>
<![CDATA[Optimal battery sizing using modified spider monkey optimization in grid connected microgrids ]]>
<![CDATA[Fatima, Meraj]]>;
<![CDATA[Subbamma, Manne Rama]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp356-365
<![CDATA[Microgrids (MGs) must have optimally sized storage and renewable energy sources to operate efficiently, economically, and reliably. MG may benefit from optimization techniques in their scheduling and sizing since they have a variety of energy sources with varying availability conditions and necessary costs. In this research, a novel modified spider monkey-based energy management system (MSM-EMS) has been proposed by increasing the photovoltaic (PV) or battery energy storage system (BESS) module capacity while minimizing grid connectivity dependency. The fundamental idea behind the proposed approach is greater dependability at the lowest feasible cost. By taking into account the BESS utilization factor and PV forced outage rates in a MG, the method becomes more realistic. Despite the absence of renewable energy sources and the grid, the proposed strategy provided critical loads according to schedule while maintaining reserve margins. Experimental findings demonstrate that the modified spider monkey optimization (MSMO)-based algorithm can determine the best BESS size and PV depending on cost. In comparison to particle swarm optimization (PSO) of $2756.1 and ABC of $2912.65, the ideal cost for EMS-MSMO is $2215.77 which is relatively low compared to the existing technique. As a result, the suggested MSMO algorithm and innovative energy management system has been optimized along with PV and battery dimensions.]]>
<![CDATA[Antecedents and consequences of memorable experience in the airline industry: service robots versus human staff ]]>
<![CDATA[Hwang, Jinsoo]]>;
<![CDATA[Choe, Ja Young (Jacey)]]>;
<![CDATA[Joo, Kyuhyeon]]>;
<![CDATA[Kim, Jinkyung Jenny]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp409-417
<![CDATA[The study aims to examine the type of service providers, such as service robots and human staff, as a potential moderator in the relationship between SERVQUAL and memorable experience in the airport industry. In order to verify 15 hypotheses, data were collected from 313 travelers who acquired information from service robots and 313 travelers who acquired information from human staff at the airport. The results of data analysis revealed that the five sub-dimensions of SERVQUAL, including tangibles, reliability, responsiveness, assurance, and empathy, enhance memorable experience. In addition, a memorable experience has a positive effect on customer satisfaction, which subsequently influences attitude and intention to use. In addition, the type of service providers moderated the links between i) responsiveness and memorable experience and ii) empathy and memorable experience.]]>
<![CDATA[A hybrid transformer-graph neural networks framework for enhanced physical activity recognition and sedentary behavior analysis ]]>
<![CDATA[Anandanarayanan, Sudarsanam]]>;
<![CDATA[Thirumaran, Suvarnalingam]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp429-438
<![CDATA[Sedentary behavior has been identified as a major risk factor for chronic diseases such as cardiovascular disorders, obesity, and diabetes. The accurate prediction of sedentary health risks is essential for early intervention and personalized healthcare strategies. This study proposes a novel machine learning-based predictive model that leverages transformer-based architectures and graph neural networks to analyze multidimensional behavioral data. Unlike traditional models, our approach incorporates temporal attention mechanisms to capture long-term dependencies in activity patterns and graph-based learning to model complex relationships between physiological and behavioral factors. The study utilizes real-world datasets, including wearable sensor data and self-reported activity logs, to train and validate the models. Experimental results demonstrate that the proposed framework outperforms conventional machine learning techniques such as random forest and XGBoost, achieving superior predictive accuracy and robustness. The findings highlight the potential of advanced machine learning algorithms in assessing sedentary health risks, enabling proactive health management and intervention strategies.]]>
<![CDATA[Design of low-power, high-speed approximate 4:2 compressors for efficient partial product reduction in multipliers ]]>
<![CDATA[Michael, Jabez Daniel Vincent David]]>;
<![CDATA[Gorantla, Anusha]]>;
<![CDATA[Appathurai, Ahilan]]>;
<![CDATA[Ramachandran, Dinesh]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp459-467
<![CDATA[Partial product reduction becomes the main task in the multiplication process. Therefore, the partial product stages of multipliers are reduced with the usage of compressors, by using compressors in the multiplier. Using compressors in the multiplier circuit significantly impacts multiplier performance. Approximate compressors are crucial for achieving better design metrics in parallel multipliers. This paper proposes to create various new approximate 4:2 compressor circuits. A trade-off is made between the performance and accuracy of this approximate circuit design approach. The proposed designs have been implemented using XOR-XNOR gates with a 2-to-1 multiplexer, and also XOR-XNOR gates with transmission gates. All these circuits have been simulated using Cadence in different technological nodes. Compared with the existing technique, the proposed 4:2 approximation compressor provides 51.4% power reduction and 26.45% delay reduction for 45 nm equipment.]]>
<![CDATA[A survey on convolutional neural network hardware acceleration through approximate computing multiple and accumulates unit ]]>
<![CDATA[Sudhakaran, Suvitha Pathiyadan]]>;
<![CDATA[Thangakalai, Aathmanesan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp366-375
<![CDATA[Convolutional neural networks (CNNs) are applied to a different range of real-world complex tasks to provide effective solutions with high accuracy. Based on the application's complexity, CNN demands a lot of processing units and memory spaces for its effective implementation. Bringing this computational task to hardware for processing the data to enhance the acceleration helps in achieving real-time performance improvement. Recent studies focused on approximation methodology to overcome this problem. This proposed survey analyzes various recent methods involved in implementing approximating computing-based processing elements and their usage in CNNs. Primarily, the survey focuses on multiple and accumulates (MAC) unit and their various approximation methods, which acts as a fundamental block as a processing element in the CNN layers. Secondly, it focuses on various CNN hardware acceleration architectures and their layers designed using different methods and their wide range of applications. Some of the recent design methods applied to various ranges of applications are also analyzed in the proposed survey. This detailed analysis gives an outlook on effective approximation blocks and the CNN architecture to be effectively used in various designs, with a scope of area in which future improvement can be made.]]>
