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
533 Documents
<![CDATA[Design of beefsteak tomato harvesting robot system in greenhouse ]]>
<![CDATA[Thien An Dinh]]>;
<![CDATA[So Nam Phung]]>;
<![CDATA[Tri Cong Phung]]>
<![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.pp353-364
<![CDATA[One challenge for tomato harvesting robots is that some of the tomato stems were not detectable because they were hidden behind the leaves or other obstacles. The primary objective of this research is to design, simulate, and experiment with a tomato harvesting robot and propose an improved detection algorithm to overcome the above problem. The suggested detection algorithm is designed to first detect the tomato fruit itself, and if the stem is not visible, the system will automatically adjust the camera's viewing angle to provide a better perspective and uncover the hidden stem. Simulation and experimental tests were carried out in a real tomato greenhouse to evaluate the cutting and holding mechanism, as well as the camera-based detection algorithm. These experimental results confirmed the effectiveness of the gripper and detection system and revealed several challenges in the harvesting algorithm. By integrating advanced algorithms for tomato detection and harvesting, this robot will reduce damage to the tomatoes, ensuring higher quality and yield.]]>
<![CDATA[Design and implementation of NMPC for a two-DOF robotic arm using CasADi ]]>
<![CDATA[Lahcen Boulbalah]]>;
<![CDATA[Faiza Dib]]>;
<![CDATA[Nabil Benaya]]>;
<![CDATA[Khaddouj Ben Meziane]]>
<![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.pp307-318
<![CDATA[Achieving accurate joint-space tracking in multi-link robotic arms is complicated by strong configuration-dependent nonlinearities and mandatory actuator limits that classical controllers are structurally unable to enforce. This paper presents a nonlinear model predictive control (NMPC) scheme for a two-degree-of-freedom (2-DOF) serial robotic arm, implemented within the CasADi symbolic computing environment to leverage automatic differentiation and sparse interior-point solving. The complete set of Lagrangian equations of motion-inertia, Coriolis, and gravity terms-is incorporated directly into the optimizer's prediction model through fourth-order Runge-Kutta (RK4) integration, eliminating the need for linearization. Torque, velocity, and angle bounds are imposed as native hard inequality constraints at every step of the finite-horizon optimization. Systematic simulations pit the proposed NMPC against a Ziegler-Nichols-tuned decentralized PID at two distinct sampling periods. The NMPC achieved a 95% reduction in peak tracking error relative to PID (0.0058 rad vs. 0.1347 rad for Joint 1), with mean error decreases of 64.65% and 57.58% for Joints 1 and 2 respectively, at an average solver time of 0.053 s-comfortably within the 0.1 s control cycle. The findings demonstrate that online NMPC with unabridged nonlinear dynamics is computationally practical for real-time joint control on standard computing hardware.]]>
<![CDATA[Cost-aware global frontier matching for ROS 2 multi-robot exploration ]]>
<![CDATA[Chu Van Cuong]]>;
<![CDATA[Tran Tuan Anh]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i3.pp589-596
<![CDATA[Multi-robot frontier exploration supports warehouse mapping and inspection robotics, but geometric assignment can produce overlapping motion and inefficient target pairing. This paper presents a ROS 2 frontier-allocation layer that combines a weighted frontier cost with global one-to-one matching. The study isolates global matching from sequential assignment while keeping the cost formulation and ROS 2 execution stack fixed. Three policies are evaluated on two indoor maps, four team sizes, and three seeds. Across 72 completed main-policy runs, global matching gives the lowest mean completion time, travelled distance, path overlap, and assignment conflict. Relative to sequential cost-based assignment, it reduces completion time by 24.3%, travelled distance by 14.2%, and path overlap by 65.8%, with lower final coverage under the same stopping rule. The results support a coordination-efficiency benefit in the tested ROS 2 simulations; broader claims require larger, heterogeneous, dynamic, and physical deployments.]]>
<![CDATA[IoT-based approach to locate books in library system for integration with the automated library systems ]]>
<![CDATA[Sagar S. Ajanalkar]]>;
<![CDATA[Harshadeep S. Joshi]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i3.pp647-657
<![CDATA[The main challenge in a large library is to retrieve a book effectively, where traditional barcode- or RFID-based systems require significant infrastructure and complex inventory management. An integrated IoT system consisting of LED-assisted shelf identification and an interactive web visualization application is proposed in this study for enhancing the reliability and effectiveness of automated library book retrieval. In this, a networked microcontroller architecture is used for real-time identification of the exact physical location of books by allowing users to visually locate the required books to be retrieved using LED indicators. Along with this, an interactive web visualization application is developed using HTML5, CSS, JavaScript, PHP, JSON, and MySQL for visualization of the shelf real-time location and location of the book within the shelf. The proposed system offers a cost-effective, scalable, and user-friendly solution compared to conventional RFID or barcode-based systems. Experimental testing of the proposed system passed all 9 tests conducted for its conformance in identifying the required book locations across four distinct test conditions. Beyond the library book retrieval application, the proposed system architecture has potential applications in smart warehouses, storage inventory management, and application as a supporting architecture for future systems such as autonomous service robots.]]>
<![CDATA[ROVAA: Offline attendance automation using a voice–OCR-based 3-DOF robotic arm with Raspberry Pi ]]>
<![CDATA[Rajanikanth Kashi Nagaraj]]>;
<![CDATA[Archana Harihara Ranganatha]]>;
<![CDATA[Surendra Hanumanthaiah Honnamachanahalli]]>;
<![CDATA[Venu Manighatta Gopalakrishnappa]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i3.pp561-576
<![CDATA[Conventional classroom attendance systems suffer from limitations in accuracy, hygiene, data privacy, and reliability in low-connectivity environments, whether they are manual, cloud-dependent, or single-modality systems. To address these gaps, this paper presents ROVAA, a low-cost, fully offline, AI-driven robotic attendance system in the classroom environment that uniquely integrates three complementary modalities: offline voice recognition, optical character recognition (OCR), and a 3- degrees of freedom (DOF) robotic arm controlled via inverse kinematics, an integration not demonstrated in prior work. The system operates on a Raspberry Pi 4 model B and employs the Vosk speech recognition model and Tesseract OCR for accurate offline processing. Audio and visual inputs are matched in real time to enable the arm to mark attendance at pre-calibrated positions on a touchscreen. Experimental validation under varied lighting and acoustic conditions yielded 96.2% speech recognition accuracy, 95.8% OCR accuracy, and 97.6% robotic arm precision, producing an overall system success rate of 92.8%, demonstrating that high reliability is achievable without cloud infrastructure. The system is designed for cost-effectiveness, data privacy, and scalability, making it suitable for resource-constrained environments such as rural schools and institutions with limited network access. It additionally serves as an educational platform for human–robot collaboration.]]>
<![CDATA[Optimal robust control for self-balancing robot-based feedback linearization and Atom Search Optimization ]]>
<![CDATA[Alaa Jumaah Al-Maiahy]]>;
<![CDATA[Yahya Ghufran Khidhir]]>;
<![CDATA[Adnan Jabbar Attiya]]>;
<![CDATA[Hisham H. Jasim]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i3.pp519-528
<![CDATA[In this paper, a robust control method is suggested for attitude control of the two-wheeled self-balancing robot by combining feedback linearization with sliding mode control techniques. The proposed method takes into account important challenges such as external disturbance and system uncertainty. Feedback linearization cancels the nonlinearities in the dynamics of the robotic system, while the sliding mode control handles the uncertainties and the external disturbance. The parameters of the proposed controller are selected by tuning the controller with the Atom Search Optimization algorithm. MATLAB is used to simulate the proposed controller. Simulation results indicate a good performance of the presented controller with high robustness compared with the proportional-integral-derivative (PID) controller. Moreover, the proposed method reduces the rise time by approximately 40% and 50% with respect to PID. These results illustrate the feasibility of the presented control method to be used for real-time implementation in autonomous robotic balancing systems.]]>
<![CDATA[Zeroing neurodynamics proportional-integral-derivative controller for order-2 system: design, analysis, and verification ]]>
<![CDATA[Yunong Zhang]]>;
<![CDATA[Junyan Liu]]>;
<![CDATA[Zhonghua Li]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i3.pp690-697
<![CDATA[This paper presents a zeroing neurodynamics proportional-integral-derivative (PID) controller for an order-2 single-input single-output (SISO) affine-in-control nonlinear system. The controller is derived from zeroing neurodynamics (ZN) and reformulates PID gains through ZN parameters, establishing an explicit correspondence to the closed-loop poles of the error dynamics. This enables systematic pole placement for stability and performance tuning, eliminating empirical trial-and-error. Theoretical analysis proves exponential convergence of the tracking error. The inverted pendulum, a benchmark system with strong geometric nonlinearity, is used to verify the controller. Simulations with large initial offsets and constant references show that the proposed controller maintains stable tracking in operating conditions where the conventional PID controller exhibits substantial performance degradation. The resulting framework provides a model-based and theoretically grounded alternative for nonlinear control.]]>
<![CDATA[Visual and electrical approaches for automated verification of electronic components ]]>
<![CDATA[Sowmya Santhanam]]>;
<![CDATA[Aadhitya Swaminathan Velmurugan]]>;
<![CDATA[Durkadevi Chandrahasan]]>;
<![CDATA[Krithika Arcot Rathna Kumar]]>;
<![CDATA[Jeevanthika Chepauk]]>;
<![CDATA[Deepika SasiKumar]]>;
<![CDATA[Chaithanya Sarangaraj]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i3.pp658-668
<![CDATA[Reliable inspection and sorting of electronic components have become pivotal along the path of electronic manufacturing toward higher density and automation. Automated optical inspection systems at present depend on visual assessment methods because they do not include electrical testing capabilities, which results in a component verification reliability gap. In spite of recent advances, largely due to the fact that most automated optical inspection systems still abide by visual evaluation, verification of the actual electrical behavior of components became quite impossible. This paper is focused on bridging this gap through the introduction of a unified inspection framework whereby visual analysis is executed along with programmable electrical validation under a single automated process in conformity with Industry 4.0 practices. The system's synchronized workflow includes vision-based detection, optical character recognition, resistor color-band parsing, surface defect analysis, and electrical testing in real time. The component localization task uses YOLOv5, while EasyOCR with a convolutional neural network-long short-term memory (CNN-LSTM) structure and HSV-based segmentation delivers exact value extraction results. The testing system achieved 98.4% classification accuracy, 98.9% value recognition accuracy, and 94.9% overall sorting accuracy when tested on 3,000 photos and 400 physically inspected components at a throughput rate of seven components per minute.]]>
<![CDATA[Automated smart handbag with enhanced women's safety using cutting edge technology ]]>
<![CDATA[Vijayaraja Loganathan]]>;
<![CDATA[Dhanasekar Ravikumar]]>;
<![CDATA[Ashish Ragavendra Nattamai Uthayakumar]]>;
<![CDATA[Arulmurugan Nagarajan Renukadevi]]>;
<![CDATA[Rishikeshwaran Balamurugan Rani]]>;
<![CDATA[Rupa Kesavan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i3.pp669-677
<![CDATA[Women’s safety has been an area of concern, especially in public places where timely assistance cannot be provided. To mitigate this problem, this paper proposes an intelligent handbag-based women’s safety system that utilizes the concept of biometric identification, location tracking, and edge computing-based AI threat verification. The proposed system, unlike other traditional women’s safety devices that rely on GPS-GSM for emergency alerts and are more likely to send false alarms, utilizes fingerprint identification for secure and authorized use, along with YOLO v3 vision model on an ESP32-CAM for threat verification. Upon failure in the authentication process or threat detection, the system sends an SOS message with the current location via GSM with the help of GPS coordinates. The system achieves an emergency response time of 33 seconds, primarily limited by GPS acquisition delay. The results confirm the effectiveness and applicability of the proposed system in providing an intelligent emergency response system for women’s safety.]]>
<![CDATA[Precision enhancement in drone position control based on optimized PID: A comparative study ]]>
<![CDATA[Imam Barket Ghiloubi]]>;
<![CDATA[Latifa Abdou]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v15i3.pp529-543
<![CDATA[This paper presents a comparative study of quadrotor trajectory tracking performance using proportional integral derivative (PID) controllers optimized by two nature-inspired metaheuristic algorithms: the flower pollination algorithm (FPA) and particle swarm optimization (PSO). A nonlinear dynamic model of the quadrotor is first established, capturing both translational and rotational motions. A cascaded PID control architecture is then designed, where the outer loop regulates position and the inner loop controls attitude and altitude. The PID gains are tuned offline by minimizing an objective function combining the integral of time-weighted absolute error (ITAE) and an overshoot penalization term. The optimized controllers are evaluated through three reference scenarios: step response, circular trajectory, and lemniscate trajectory. Quantitative performance metrics, including RMSE, MAE, IAE, and ITAE, are used to assess tracking accuracy. Results show that the PID-FPA controller significantly outperforms PID-PSO in terms of overshoot reduction, faster settling time, smoother responses, and improved tracking precision, especially for complex trajectories. A robustness analysis is finally conducted by introducing a realistic synthetic wind disturbance modeled using sinusoidal components and stochastic turbulence. The FPA-tuned PID demonstrates strong robustness, maintaining accurate trajectory tracking under aerodynamic perturbations.]]>
