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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Dang, Thai-Viet
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : journal of robotics and control jrc

 1 
A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet Dang, Thai-Viet; Tran, Hoai-Linh
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18780

Abstract

Artificial Intelligence and IoT have always attracted a lot of attention from scholars and researchers because of their high applicability, which make them a typical technology of the Fourth Industrial Revolution. The hallmark of AI is its self-learning ability, which enables computers to predict and analyze complex data such as bio data (fingerprints, irises, and faces), voice recognition, text processing. Among those application, the face recognition is under intense research due to the demand in users’ identification. This paper proposes a new, secured, two-step solution for an identification system that uses MTCNN and FaceNet networks enhanced with head pose estimation of the users. The model's accuracy ranges from 92% to 95%, which make it competitive with recent research to demonstrate the system's usability.
Smart Attendance System based on improved Facial Recognition Dang, Thai-Viet
Journal of Robotics and Control (JRC) Vol 4, No 1 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i1.16808

Abstract

Nowadays, the fourth industrial revolution has achieved significant advancement in high technology, in which artificial intelligence has had vigorous development. In practice, facial recognition is one most essential tasks in the field of computer vision with various potential applications from security and attendance system to intelligent services. In this paper, we propose an efficient deep learning approach to facial recognition. The paper utilizes the architecture of improved FaceNet model based on MobileNetV2 backbone with SSD subsection.  The improved architecture uses depth-wise separable convolution to reduce the model size and computational volume and achieve high accuracy and processing speed. To solve the problem of identifying a person entering and exiting an area and integrating on advanced mobile devices limits to (such as limited memory and on-device storage) highly mobile resources. Especially, our approach yields better results in practical application with more than 95% accuracy on a small dataset of the original face images. Obtained frame rate (25 FPS) is very favorable compared to the field of facial recognition using neural network. Besides, the deep learning based on solution could be applicable in many low-capacity hardware or optimize system’s resource. Finally, the smart automated attendance systems is successfully designed basing on the improved efficient facial recognition.
An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning Tran, Hoai-Linh; Dang, Thai-Viet
Journal of Robotics and Control (JRC) Vol 4, No 3 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i3.18758

Abstract

Computer vision plays a significant role in mobile robot navigation due to the abundance of information extracted from digital images. On the basis of the captured images, mobile robots determine their location and proceed to the desired destination. Obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement due to the complexity of the environment. This research provides a real-time solution to the issue of extracting corridor scenes from a single image. Using an ultra-fast semantic segmentation model to reduce the number of training parameters and the cost of computation. In addition, the mean Intersection over Union (mIoU) is 89%, and the high accuracy is 95%. To demonstrate the viability of the prosed method, the simulation results are contrasted to those of contemporary techniques. Finally, the authors employ the segmented image to construct the frontal view of the mobile robot in order to determine the available free areas for mobile robot path planning tasks.
Smart home Management System with Face Recognition Based on ArcFace Model in Deep Convolutional Neural Network Dang, Thai-Viet
Journal of Robotics and Control (JRC) Vol 3, No 6 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i6.15978

Abstract

In recent years, artificial intelligence has proved its potential in many fields, especially in computer vision. Facial recognition is one of the most essential tasks in the field of computer vision with various prospective applications from academic research to intelligence service. In this paper, we propose an efficient deep learning approach to facial recognition. Our approach utilizes the architecture of ArcFace model based on the backbone MobileNet V2, in deep convolutional neural network (DCNN). Assistive techniques to increase highly distinguishing features in facial recognition. With the supports of the facial authentication combines with hand gestures recognition, users will be able to monitor and control his home through his mobile phone/tablet/PC. Moreover, they communicate with data and connect to smart devices easily through IoT technology. The overall proposed model is 97% of accuracy and a processing speed of 25 FPS. The interface of the smart home demonstrates the successful functions of real-time operations.
Hybrid Path Planning for Wheeled Mobile Robot Based on RRT-star Algorithm and Reinforcement Learning Method Pham, Hoang-Long; Bui, Nhu-Nghia; Dang, Thai-Viet
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.27678

Abstract

In the field of wheeled mobile robots (WMRs), path planning is a critical concern. WMRs employ advanced algorithms to find out the feasible path from a starting point to a specific destination. The paper proposes efficient and optimal path planning for WMRs, integrating collision avoidance strategies and smoothed techniques to determine the best route during navigation. The proposed hybrid path planning consists of improved RRTstar algorithm and reinforcement learning method. Therefore, the RRT* algorithm employs random sampling in conjunction with a reinforcement learning model to purposefully guide the sampling process towards areas that demonstrate an increased likelihood of successful navigation completion. The proposed RRTstar-RL algorithm generates significantly shorter trajectories compared to the traditional RRT and RRTstar methods. Specifically, the path length with the proposed algorithm is 11.323 meters, while the lengths for RRT and RRTstar are 15.74 and 14.40 meters, respectively. Moreover, the optimization of computation time, especially when using pre-trained data, greatly speeds up the path-finding calculation process. In particular, the time needed to generate the optimal path with the RRTstar-RL algorithm is 2.02 times faster than that of RRTstar and 1.6 times faster than RRT. Finally, the proposed RRTstar-RL algorithm has been successfully verified for feasibility and effectively meets numerous objectives established during simulations and validation experiments.
Co-Authors Bui, Nhu-Nghia Bui, Phuong-Thao Thi La, Ngoc-Anh Thi Le, Tieu-Thanh Pham, Hoang-Long Tran, Hoai-Linh