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[A comparative look at how emerging technologies evolve to managing otitis media ]]>
<![CDATA[Pandey, Divya]]>;
<![CDATA[Awasthi, Monisha]]>;
<![CDATA[Kumar, Dharmendra]]>;
<![CDATA[Pant, Deepak Kumar]]>;
<![CDATA[Goel, Ankur]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp162-170
<![CDATA[Otitis media (OM) is an epidemic of middle ear infection in tens of millions of patients across the globe, most vulnerable of whom are children, with hearing loss and other negative consequences unless treated. Conventional diagnosis and treatment are marred by failure to diagnose, service shortage, and delayed diagnosis. This present paper is directed towards a comparative outlook of the newly emerging technologies, such as artificial intelligence (AI), machine learning, telemedicine, and wearable biosensors, that are revolutionizing the management of OM. We emphasize the way such devices enhance diagnostic accuracy, facilitate remote and real-time monitoring, and provide tailored treatment schemes. Our approach is more sophisticated compared to the currently available state-of-the-art methods reported in the literature based on real-time telemedicine systems, multimodal data fusion, and interpretable AI. Privacy issues of information, model generalizability issues, and technological adoption barriers are also discussed. The results also substantiate that adoption of these advanced devices can effectively reduce OM's burden globally and improve patient outcomes.]]>
<![CDATA[ISTD-LIOM: Direct LiDAR-inertial odometry and mapping with intensity-enhanced stable triangle descriptor ]]>
<![CDATA[Yang, Lixiao]]>;
<![CDATA[Hua, Sheng]]>;
<![CDATA[Feng, Youbing]]>;
<![CDATA[Yang, Shangzong]]>;
<![CDATA[Wang, Jie]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp52-62
<![CDATA[To address the cumulative drift problem of light detection and ranging (LiDAR)-inertial odometry (LIO) in long-duration localization and mapping tasks, this paper proposes a LiDAR-inertial odometry and mapping system, intensity-enhanced stable triangle descriptor-LiDAR-inertial odometry and mapping (ISTD-LIOM), based on the intensity-enhanced stable triangle descriptor (ISTD). This system, built on the FAST-LIO2 front-end architecture, achieves global consistency localization through loop closure detection and global optimization. First, we design the ISTD descriptor by combining geometric descriptors of triangles (including vertex plane normal vectors and edge lengths) with local intensity distribution descriptors to form a compact, rotation-invariant feature representation. Next, an adaptive keyframe management mechanism is constructed, which filters keyframes based on inter-frame relative poses and generates a descriptor database. A hybrid retrieval strategy is then proposed, which combines descriptor similarity matching and spatial distance filtering, forming an efficient loop closure candidate recognition mechanism. After applying plane iterative closest point (ICP) refinement and geometric-intensity consistency validation, the loop closure constraints are integrated into a pose graph optimization framework, correcting odometry drift. Experiments on the KITTI dataset demonstrate that the ISTD-LIOM system significantly enhances map global consistency while maintaining real-time computational performance.]]>
<![CDATA[Real-time control signal rectification and actuation mapping for robot joint control ]]>
<![CDATA[Irawan, Addie]]>;
<![CDATA[Razali, Akhtar Razul]]>;
<![CDATA[Amran, Aliza Che]]>;
<![CDATA[Ahmad, Hamzah]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp43-51
<![CDATA[This paper presents the control signal rectification and actuation mapping (CSRAM) framework, developed to improve the reliability and precision of real-time robot joint control. The framework integrates three modules, namely the drive signal rectifier (DSR), the signal pole detector (SPD), and the rising/downstream detector (RDD), which ensure signal compatibility, dynamic mapping consistency, and directional stability during actuation. Unlike conventional control converters, CSRAM effectively compensates for nonlinearities, latency, and synchronization issues in closed-loop systems. Experimental validation using a hexapod-to-quadruped (Hexaquad) robot showed that the proposed method, when combined with an anti-windup PI controller, reduced steady-state error from 14% to below 1%, improved transient and settling times by 0.3 to 0.4 seconds, and decreased three-dimensional trajectory RMSE by 63.7%. These results confirm that CSRAM provides a low-complexity and computationally efficient preprocessing layer for improving real-time performance in multi-joint and legged robotic systems, with strong potential for adaptive and industrial robotic platforms.]]>
<![CDATA[System design for hydrogen gas detection with intelligent embedded communication and Internet of Things integration ]]>
<![CDATA[Menon, Shyam Kumar]]>;
<![CDATA[Kumar, Adesh]]>;
<![CDATA[Mondal, Surajit]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp222-233
<![CDATA[The design and analysis of an embedded hydrogen gas detection system embody a complex engineering challenge that integrates sensor technology, embedded system design, and safety engineering. The fast advancement of microcontrollers, energy-efficient electronics, and sophisticated sensing algorithms is facilitating the creation of compact, efficient, and dependable hydrogen detection solutions. The research case article focuses on the hardware system design of Hydrogen gas detection. The hydrogen gas detection system includes an MQ8 sensor for gas sensing, a Raspberry Pi-4 as the main controller, Zigbee for wireless communication, a 16×2 liquid crystal display (LCD) for display, a light emitting diode (LED), and a buzzer for alerts, along with supporting circuitry for signal processing. The gas concentration is monitored and verified through the Thingspeak.com Internet of Things platform, which enables wireless data transmission. The designed system is verified based on environmental factors such as temperature and humidity for comprehensive analysis. The system response was analyzed and tested under different threshold conditions, including 300 ppm and 1000 ppm.]]>
<![CDATA[A study on motivated consumer innovativeness in robotic golf caddies ]]>
<![CDATA[Hwang, Jinsoo]]>;
<![CDATA[Song, Sujin]]>;
<![CDATA[Park, Sungbeen]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp99-106
<![CDATA[The current study examined the antecedents and consequences of image in the field of robotic golf caddy. Data were collected from 393 golfers in Korea. The data analysis revealed that functionally, hedonically, and cognitively motivated consumer innovativeness are the key factors that affect image. It was also found that image helps in regard to enhancing desire, and then it positively affects intentions to use and WOM intention. Perceived price unfairness of caddy fees additionally moderated the relationship between functionally motivated consumer innovativeness and image. This study is significant from a theoretical perspective as it is the first to identify consumer motivations in the field of robotic golf caddies. From a practical standpoint, the findings offer important implications for the development of marketing strategies for robotic golf caddies, which are currently at the commercialization stage.]]>
<![CDATA[Hybrid force/position approach for flexible-joint robot with fuzzy-super twisting sliding mode control ]]>
<![CDATA[Amaini, Rafik]]>;
<![CDATA[Ferguene, Farid]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp21-32
<![CDATA[This paper presents a novel hybrid force/position control strategy for rigid-link flexible-joint robots (RLFJR) operating in constrained environments. The proposed approach integrates fuzzy logic with the super-twisting sliding mode control (FSTSMC) algorithm to enhance robustness and reduce the chattering phenomenon typically associated with sliding mode controllers. A two-loop control structure is adopted: an inner loop dedicated to position control using the FSTSMC, and an outer loop for force regulation employing a classical PI controller. To address the challenge of limited joint state measurements in industrial robots, a high-gain nonlinear observer is designed for accurate joint state estimation. The effectiveness of the proposed method is validated through simulations on a PUMA 560 robot model, performing a circular trajectory while applying a constant contact force. Results demonstrate high tracking precision in both joint and Cartesian spaces, rapid convergence of errors, and significant mitigation of chattering effects, confirming the feasibility and efficiency of the proposed control scheme for interaction tasks involving flexible-joint manipulators.]]>
<![CDATA[Semantic segmentation for data validation in unmanned robotic vehicles ]]>
<![CDATA[Rout, Ivan Sunit]]>;
<![CDATA[Pandian, P Pal]]>;
<![CDATA[Raj, Anil]]>;
<![CDATA[Rego, Anil Melwyn]]>;
<![CDATA[Panigrahi, Sajna Parimita]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp71-79
<![CDATA[Semantic segmentation is a vital aspect of computer vision, widely used in fields such as autonomous driving, medical imaging, and industrial automation. Maintaining high-quality datasets is crucial for enhancing model accuracy and minimizing real-world errors. This paper focuses on developing a comprehensive data validation pipeline for semantic segmentation using OpenCV. The proposed framework integrates automated integrity checks, preprocessing techniques, and consistency verification to manage large-scale datasets effectively. Key validation processes include image quality assessment (detection of blurriness and noise), verification of annotation accuracy, class distribution analysis, and identification of anomalies. Additionally, OpenCV-powered preprocessing steps, such as image resizing, normalization, contrast optimization, and data augmentation, are applied to refine dataset quality for segmentation models. This paper also addresses scalability concerns associated with processing extensive datasets, introducing optimized batch handling and parallel validation techniques. By implementing a structured validation workflow, this research enhances the reliability, robustness, and overall effectiveness of semantic segmentation models, ensuring high-quality training data for deep learning applications.]]>
<![CDATA[Development of autonomous quadcopter unmanned aerial vehicle using APM 2.8 flight controller ]]>
<![CDATA[Amran, Mohd Yusuf]]>;
<![CDATA[Mohd Basri, Mohd Ariffanan]]>;
<![CDATA[Noordin, Aminurrashid]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp63-70
<![CDATA[This paper presents the development of a quadcopter unmanned aerial vehicle (UAV) using the APM 2.8 flight controller as the core of its navigation and control system. The project aims to design, assemble, and evaluate a stable and cost-effective quadcopter platform suitable for basic autonomous flight tasks such as waypoint navigation and altitude hold. The system incorporates essential components, including brushless DC motors, ESCs, a GPS module, a telemetry radio, and a power distribution system, integrated with the APM 2.8 running on the ArduPilot firmware. Waypoints are planned via Mission Planner software, with a flight control system embedded in the firmware. Real-world flight tests were conducted to evaluate the UAV’s performance in executing autonomously predefined survey grid and zigzag waypoints trajectories over open terrain. The root mean square error (RMSE) was calculated to assess the performance of waypoint tracking accuracy. The results show that the quadcopter UAV achieved an RMSE of 1.78 meters during zigzag waypoint tracking and 1.56 meters during survey grid, demonstrating reliable flight control performance offered by the APM 2.8 for basic autonomous mission tasks. This work highlights the feasibility of using APM 2.8 for cost-effective UAV development in research, education, and prototyping purposes.]]>
<![CDATA[Multi-modal transformer and convolutional attention architectures for melanoma detection in dermoscopic images ]]>
<![CDATA[Amina, Guidoum]]>;
<![CDATA[Bougherara, Maamar]]>;
<![CDATA[Rafik, Amara]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp136-148
<![CDATA[The deadliest type of skin cancer, melanoma, requires early and accurate detection for a successful course of treatment. Traditional diagnostic techniques, which rely on visual inspection and dermoscopy, are frequently arbitrary and prone to human error. Automated melanoma detection exemplifies the integration of multimedia, a truly interdisciplinary field that melds visual data processing, human-computer interaction, and digital technologies. This study presents a multi-modal architecture: a multi-modal transformer network (MMTN) and a convolutional attention mechanism multi-modal (CAMM) that combines clinical data and dermoscopy images to enhance melanoma detection. The models achieve higher performance compared to other approaches by utilizing the strengths of architecture based on transformers, an encoder for image processing, dense layers for clinical data also Spatial Attention for the second architecture proposed. We evaluate the models on the entire set of ISIC 2019 data, showing significant improvements in accuracy and AUC. The models achieve high accuracy and AUC using CPU in both architectures. Our findings highlight the potential of a multi-modal learning architecture to enhance clinical decision-making and diagnostic accuracy in dermatology. To our knowledge, this is the first implementation combining MobileNet, transformer encoder attention, and clinical data fusion for the ISIC 2019 dataset, providing a significant advancement in the automated categorization of skin malignancies.]]>
<![CDATA[A review of human swarm interaction ]]>
<![CDATA[Barca, Jan Carlo]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i1.pp80-88
<![CDATA[A review of recent activities in Human Swarm Interaction (HSI) research is presented in this paper. The paper begins with providing a short description of swarming. It then discusses HSI and explains why it is beneficial to enable human operators to supervise swarms of robots. Then, a wide range of papers, which present novel methods for interacting with swarms of robots, are reviewed. Four control methods that can be used to transmit an operator’s intent to a swarm are also discussed. Levels of autonomy and flexible autonomy in HSI are furthermore described. At the end of the paper, a discussion of the gaps in knowledge that still must be filled to enable swarms of robots to operate in the real world is presented. It is suggested that more research into techniques for remote interaction with robotic swarms be conducted. This includes methods that enable remote interaction with swarms of swarms. More work on HSI in degraded communications environments is also required. Additional research into swarm autonomy is furthermore needed to facilitate efficient supervisory control. Lastly, there is room for more work on trust in HSI, as robotic swarms can only be used by humans if they can be trusted.]]>
