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
512 Documents
<![CDATA[Vibration control of semi-active suspension system using super-twisting sliding mode controller ]]>
<![CDATA[Sun, Liuding]]>;
<![CDATA[Ahmad, Siti Azfanizam]]>;
<![CDATA[Ong, Jun Kit]]>;
<![CDATA[Hanapi, Suhadiyana]]>;
<![CDATA[As'arry, Azizan]]>
<![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.pp171-180
<![CDATA[The development of suspension systems arises from the impact of vehicle vibrations caused by road irregularities on passengers. Among various suspension systems, semi-active suspension (SAS) is favored for its cost-effectiveness and power efficiency. Magnetorheological (MR) dampers are commonly used in SAS to enhance vibration control by adjusting the magnetic field. However, the traditional sliding mode control (SMC) method often causes chattering, which affects performance. This study proposes the application of a super-twisting sliding mode controller (STSMC) to improve vibration control in SAS and overcome the chattering problem. Simulations and experimental evaluations were conducted on a quarter-car test bench with different road excitations. The results show that the STSMC-based system outperforms the traditional controller in vibration suppression. Specifically, the suppression effect on the root mean square value of body acceleration on a sinusoidal road surface can reach up to 38.2%. Therefore, the STSMC controller demonstrates superior vibration control in SAS systems equipped with MR dampers, providing a valuable reference for future research on SAS vibration control.]]>
<![CDATA[Autonomous reconstruction of strip-shredded documents via self-supervised deep learning and global optimization ]]>
<![CDATA[Wu, Yi-Chang]]>;
<![CDATA[Chiang, Pei-Shan]]>;
<![CDATA[Liu, Yao-Cheng]]>
<![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.pp107-121
<![CDATA[Autonomous reconstruction of mechanically shredded documents is a labor-intensive challenge in forensic and archival workflows, particularly for scripts with complex structures such as Simplified Chinese. While traditional manual reassembly is tedious, existing digital tools typically rely on extensive human intervention. This paper presents an automated reassembly framework that integrates a lightweight convolutional feature extractor with global combinatorial optimization. By adapting the established SqueezeNet v1.1 backbone, we employ a task-specific self-supervised learning strategy trained on synthetically shredded samples, enabling the adapted model to capture local stroke continuity and edge-geometry cues without manual annotation. The framework infers pairwise relationships from calibrated edge-region inputs, organizing compatibility scores into an asymmetric traveling salesman problem (ATSP) formulation. The optimal fragment sequence is solved deterministically using the Concorde TSP solver, yielding a globally consistent reconstruction. Experimental results on physically shredded documents demonstrate reconstruction accuracies of 86.5% for Simplified Chinese and 94.8% for Western scripts. These results indicate that the proposed pipeline effectively generalizes from synthetic training data to real-world scenarios, providing a practical, high-throughput foundation for automated document recovery under computational constraints typical of robotic or embedded systems.]]>
<![CDATA[Real-time low-drift global optimization for dynamic scene LiDAR SLAM localization ]]>
<![CDATA[Yang, Peiyan]]>;
<![CDATA[Yu, Jiuyang]]>;
<![CDATA[Liu, Pan]]>;
<![CDATA[Xia, Wenfeng]]>;
<![CDATA[Dai, Yaonan]]>
<![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.pp1-20
<![CDATA[To address challenges like global drift, unstable matching, and high computational cost in light detection and ranging simultaneous localization and mapping (LiDAR SLAM) under complex conditions, this paper proposes an improved algorithm based on the LeGO-LOAM framework. A Newton-optimized normal distributions transform (NDT) is integrated to improve point cloud registration by constructing a negative log-likelihood objective and optimizing pose estimation. Using initial pose information from LeGO-LOAM accelerates convergence and enhances system robustness. This work addresses the problem of insufficient adaptability of existing algorithms in real scenarios. By deploying an independently designed four-wheel omnidirectional mobile robot platform, a hybrid LiDAR SLAM framework is used for precise positioning and map construction in complex campus environments, successfully reducing the positioning error to the centimeter level. Experiments on the KITTI dataset show a 43.51% reduction in maximum localization error and a 30.83% decrease in average error. Field tests in real-world campus environments with pedestrians, bicycles, and vehicles demonstrate strong reliability, adaptability, and resistance to interference. Horizontal error was reduced by about 58.26%, lowering the average error from 4.60 m to 1.92 m. Although computational load increases, it is offset by using high-performance LiDAR and processors. The enhanced accuracy and drift reduction significantly outperform traditional methods. At critical time points such as 50 seconds and 100 seconds, the system achieved high-precision pose estimation and accurate environmental reconstruction.]]>
<![CDATA[Constrained model predictive control for enhanced trajectory tracking in multi-DOF robotic manipulators ]]>
<![CDATA[Shyamalagowri Murugesan]]>;
<![CDATA[Gomathi Periyavattam Shanmugam]]>;
<![CDATA[Mohammadha Hussaini Mohammed Ibrahim]]>;
<![CDATA[Ramesh Ponnusamy]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i2.pp331-340
<![CDATA[Controlling a multi-degree-of-freedom (multi-DOF) robotic manipulator is complicated by nonlinear dynamics, coupled joints, and constraints such as joint limits, actuator saturation, and collision avoidance. The focus of this proposed work is the development and implementation of constrained model predictive control (MPC) algorithms for robotic manipulators. The key features of this proposal include the use of the dynamics of the manipulator in the process of prediction and the ability for the controller to take optimal actions over a fixed time horizon, while ensuring that the full range of physical and safety constraints is satisfied. The proposed MPC framework incorporates a discrete-time state-space model of the robotic manipulator that can be optimized using quadratic programming (QP), which allows for the model to be expressed in a general stable form to enable optimization. Linear and nonlinear MPC approaches will be considered, but the emphasis will be on the feasibility of real-time implementation and robustness of the controller to modelling errors and disturbances from the environment. The algorithm can be used in simulation and on a physical multi-DOF robotic arm in applications ranging from trajectory tracking to obstacle avoidance and precision positioning of the end-effector. Compared to traditional control techniques like PID, and computed torque control proves the superiority of MPC in controlling dynamic constraints and increasing control accuracy. The research also discusses implementation techniques involving reduced-order models and efficient solvers to address real-time computational needs, enabling safe and effective deployment in sophisticated robotic devices.]]>
<![CDATA[A path generation and control framework for 6-DOF robot in precision writing and drawing ]]>
<![CDATA[Khoi Hoang Dinh]]>;
<![CDATA[Khanh Tran Duy]]>;
<![CDATA[Thien Bui Thanh]]>;
<![CDATA[Quy Vo Quoc]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i2.pp281-294
<![CDATA[Robots are becoming increasingly integrated into everyday life, not only in industrial applications but also in creative, educational, and entertainment contexts. With recent advancements, collaborative robots are now lighter, safer, and easier to deploy alongside humans, making them well-suited for tasks that require precision and adaptability. This paper presents an integrated control framework for the ABB GoFa 6-DOF collaborative robot, enabling it to autonomously perform precise writing and drawing tasks. The system leverages CAD-based path design in SolidWorks and ABB RobotStudio’s AutoPath tool to generate motion trajectories from a library of modeled characters, symbols, and figures. A socket-based communication interface connects the robot controller with a user-friendly human-machine interface (HMI), allowing users to input custom text or select predefined figures in real time. The framework has been implemented and validated on the physical ABB GoFa robot, demonstrating high accuracy, repeatability, and usability for applications such as public exhibitions and educational settings.]]>
<![CDATA[Robust efficient ego-vehicle path prediction based on Bezier curves for autonomous driving ]]>
<![CDATA[Hanan H. Hussein]]>;
<![CDATA[Ahmed Atef]]>;
<![CDATA[Mohamed Hanafy Radwan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i2.pp427-444
<![CDATA[Accurate ego-vehicle path prediction is essential for safety-critical functions in advanced driver assistance systems (ADAS), such as automatic emergency braking (AEB) and collision avoidance. Existing models based on Clothoid curves are typically not sufficient in expressing complex maneuvers and are not highly adaptive to various vehicle dynamics. In addition, these models struggle with accuracy in circular maneuvers and fail to use in complex paths (e.g., S-shapes). This paper proposes a novel representation of the ego-vehicle path prediction using Bezier curves. The proposed Bezier curves are composed of two Cartesian third-order polynomial functions. They are formulated efficiently to model both circular and S-shaped trajectories with high accuracy and low computational cost. Our method significantly reduces prediction error, achieving over 95% improvement in average Euclidean distance error compared to Clothoidal models along about 50 m paths in controlled circular scenarios. The proposed algorithm, designed with O(n) complexity, is suitable for real-time applications on low-power automotive hardware. Its effectiveness is demonstrated through simulation using CarMaker, and a collision estimation module for AEB is developed based on the predicted paths.]]>
<![CDATA[Vector-logic models of digital circuits for simulation and rendering ]]>
<![CDATA[Vladimir Hahanov]]>;
<![CDATA[Svetlana Chumachenko]]>;
<![CDATA[Eugenia Litvinova]]>;
<![CDATA[Andrii Voronov]]>;
<![CDATA[Oleh Demchenko]]>;
<![CDATA[Nataliya Maksymova]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i2.pp319-330
<![CDATA[Vector-logical in-memory computing for solving modelling for simulation (MOSI) problems by using read-write transactions free of processor instructions is proposed. A parser mechanism has been developed for converting the HDL code of the logical circuit into the internal vector-logical data structures of the MOSI service, addresses of logical vectors of elements. Deductive vectors are generated from the vector-logic model of the digital circuit for fault as address simulation of the input test sets. A mechanism for modelling a fault simulation matrix as the addresses of the deductive vector bits of each element on the test set has been created. The results of good-value and fault as address simulation are rendered and synchronized in the good-value simulation, fault as address simulation, fault simulation matrices on the input set, and on the lines of the logical circuit displayed on the monitor. Modeling and simulation mechanisms encoded and verified using examples of logical circuits of the ISCAS library. The scientific novelty is represented by vector-logical models of digital circuit elements, good-value simulation of test set as address and fault as address simulation of a digital circuit, and fault as address simulation of the input set, using a quadratic simulation matrix.]]>
<![CDATA[A comprehensive review of sound source localization methods for robotics ]]>
<![CDATA[Muhammad Akmal Aliff]]>;
<![CDATA[Emerson Joseph Raja]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i2.pp257-266
<![CDATA[Sound source localization (SSL) is a key technology in robotics that allows machines to detect and locate auditory cues in real time. This review provides a thorough examination of SSL techniques classified into classical, artificial intelligence (AI), and hybrid methods. Classical methods, which account for 44% of reviewed studies, excel in computational efficiency and reliability under controlled conditions but have limitations in dynamic environments. AI methods, which account for 16% of studies, use deep learning to adapt to complex scenarios, but they require large datasets and computational resources. Hybrid methods, which combine classical signal processing and AI, are the most robust and accurate, with an average accuracy of 97.45%. The review also looks at the role of microphone arrays in SSL performance, revealing that systems with ten or more microphones achieve the highest accuracy of 99.23%, while single- and dual-microphone systems still perform competitively (97.60% and 97.21%, respectively). These findings suggest that hybrid methods combined with larger microphone arrays are the most effective SSL solution in robotics, balancing precision and adaptability. This paper discusses current SSL trends, challenges, and future research directions, providing insights for the development of advanced auditory systems capable of reliable performance in dynamic, real-world environments.]]>
<![CDATA[Finite time convergence based on third-order integral terminal sliding mode for tracking control perturbed quadrotor UAV ]]>
<![CDATA[Hala Hayder Al-Ankooshi]]>;
<![CDATA[Ali Al-Ghanimi]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i2.pp341-352
<![CDATA[Precise trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) remains challenging due to inherent nonlinear dynamics, external disturbances, and model uncertainties encountered during flight operations. This paper presents a novel third-order integral terminal sliding mode control (3-ITSMC) algorithm for regulating the altitude (z) and roll (ϕ) dynamics of a quadrotor UAV subject to wind disturbances and parametric uncertainties. The proposed controller integrates an integral terminal sliding surface with a third-order super-twisting algorithm, achieving precise tracking with near-zero steady-state error, chattering-free control signal, and rapid finite-time convergence. Rigorously established through Lyapunov stability analysis on Closed-loop stability and finite-time convergence. Extensive simulation results conducted under step and sinusoidal reference trajectories with added sinusoidal wind disturbances demonstrate the effectiveness of the proposed method. The 3-ITSMC reduction in root-mean-square (RMS) up to 98.1% in tracking error and energy savings from 51.2% to 95.3% as compared to second-order (SMC), while maintaining preserving robust disturbance rejection throughout operation. These findings achieve that the proposed 3-ITSMC offers a robust and energy-efficient solution for high precision quadrotor control under realistic flight perturbations.]]>
<![CDATA[LiDAR-based sensor fusion and navigation for indoor autonomous mobile robots in warehouse environments ]]>
<![CDATA[Rifda Hakima Sari]]>;
<![CDATA[Jazi Eko Istiyanto]]>;
<![CDATA[Oskar Natan]]>;
<![CDATA[Zaidan Hakim]]>;
<![CDATA[Danang Lelono]]>;
<![CDATA[Andi Dharmawan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i2.pp295-306
<![CDATA[An indoor navigation system for an autonomous mobile robot was developed using LiDAR-based perception and multi-sensor fusion. The system combines 2D LiDAR, inertial measurement unit (IMU), and wheel encoder measurements within a simultaneous localization and mapping (SLAM) framework to support real-time localization, while the ROS2 Nav2 stack manages global path planning and local obstacle avoidance through A*-based planning and costmap-driven control. Evaluation in a warehouse-like environment showed that the robot maintained stable localization with low drift and completed autonomous navigation missions with a success rate of 93.33%. During operation, the robot was able to avoid static obstacles consistently and adjust its trajectory in response to simple dynamic obstacles through online replanning. These results indicate that the proposed system is suitable for practical indoor logistics scenarios requiring reliable navigation in structured environments. At the same time, the findings suggest the need for further improvement to handle environments with higher dynamics and denser obstacle configurations.]]>
