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IAES International Journal of Robotics and Automation (IJRA)
Published by Institute of Advanced Engineering and Science
ISSN : 20894856     EISSN : 27222586     DOI : -
Core Subject : Engineering,
Automotive Engineering Electrical & Electronics Engineering
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 23 Documents
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Search results for , issue "Vol 15, No 1: March 2026" : 23 Documents clear
Vibration control of semi-active suspension system using super-twisting sliding mode controller Sun, Liuding; Ahmad, Siti Azfanizam; Ong, Jun Kit; Hanapi, Suhadiyana; As'arry, Azizan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp171-180

Abstract

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.
Autonomous reconstruction of strip-shredded documents via self-supervised deep learning and global optimization Wu, Yi-Chang; Chiang, Pei-Shan; Liu, Yao-Cheng
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp107-121

Abstract

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.
Real-time low-drift global optimization for dynamic scene LiDAR SLAM localization Yang, Peiyan; Yu, Jiuyang; Liu, Pan; Xia, Wenfeng; Dai, Yaonan
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp1-20

Abstract

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.

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