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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Soroush Zare Mohammad Ghanatian Mohammad Reza Hairi Yazdi Mehdi Tale Masouleh School of Mechanical Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

In this paper, a novel application of an under-constrained cable-driven parallel robot is introduced. By this application, the 3D model of any object with any size limited to the workspace of the robot can be reconstructed, which is a definite asset for creating the 3D model in such an easy and cost-efficient way despite other methods like a laser scanner. The most significant problem of an under-constrained cable-driven robot is the orientations of the end-effector which cannot be controlled regardless of its position, i.e., the orientation and position of the end-effector are not independent. In this paper, the foregoing problem was solved by using a stabilizer to capture a video in prescribed orientations. The results demonstrate that the end-effector can be controlled with high precision by a PID controller to reconstruct the 3D model of an object based on the captured videos of a camera which is mounted on the end-effector of the robot. In this paper, the object is a social robot called Sanbot that has a shiny surface, which decreases the 3D model reconstruction precision. However, the results reveal that the 3D model of the Sanbot robot has a very clear view, and the experimental results confirm the efficiency of the proposed method. This method provides an easy way to reconstruct the 3D model of an object which cannot be reconstructed by other methods.

关键词： Solid modeling Parallel robots Three-dimensional displays robot vision systems Cameras End effectors Sports

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Kinematics and Control of a 4-DOF Delta Parallel Manipulator

Kinematics and Control of a 4-DOF Delta Parallel Manipulator

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Mehdi Azmoun Ali Rouhollahi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory Faculty of New Sciences and Technologies University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

In this paper, the design, kinematic problem and control of a 4-DOF Delta parallel manipulator are investigated. Delta is a parallel manipulator which is widely used for pick-and-place applications. A simulation of this manipulator is designed in SimMechanic environment. The verification of the inverse kinematic problem has been done in simulation environment. Afterwards, different controllers like PID and Sliding-Mode based on the inverse kinematic problem are designed in simulation environment and their results are compared with each other. According to value of the root mean square error, PID controller exhibits better performance and tracked the desired path with less error compared to other controllers. Then the Sliding-Mode control is designed and based to the obtained results, leads to less chattering in tracking the desired path.

关键词： Kinematics End effectors Mathematical model Legged locomotion Solid modeling

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks

arXiv

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arXiv 2022年

作者： Haghpanah, Mohammad Amin Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Science and Engineering University of Nevada Reno United States

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Collab.rative Multi-Agent Reinforcement Learning Control of Parallel robots

Collaborative Multi-Agent Reinforcement Learning Control of ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Amirhossein Afkhami Ardekani Mehdi Tale Masouleh Mohammad Reza Hairi Yazdi Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665454537

In this paper, a novel algorithm based on Multi-Agent Reinforcement Learning for controlling parallel robots has been suggested. The dynamic models of parallel robots are complex and full of uncertainties, and deriving them requires deep knowledge of the mechanism of the robot. Therefore, the proposed algorithm is designed model-free to be independent of prior knowledge about the system from the outset. Moreover, this algorithm comprises two primary components, making it efficient in training and convergence. The proposed algorithm takes each loop or limb in parallel robots as a separate agent. These agents then learn to collab.rate to fulfill the robot’s defined task by producing appropriate control signals from a decentralized point of view. For studying the performance of the proposed algorithm, a 3-DOF parallel robot called Agile Eye is taken into account as a case study which is simulated in CoppeliaSim simulation environment for the task of reference tracking. Two other controllers, including the classic Proportional Integral Derivative (PID) controller and the single-agent counterpart of the suggested algorithm, have been implemented for a better performance comparison of the proposed algorithm. Using the Root Mean Square Error (RMSE) index, the recommended algorithm with an RMSE value of 0.0553 is superior to its single-agent counterpart with an RMSE of 0.1105. On the other hand, the proposed algorithm is inferior to the PID controller with an RMSE of 0.0275, mainly due to the fact that the PID Controller is in velocity control mode, while the proposed algorithm manipulates the robot in torque control mode, which is less stable.

关键词： Training Parallel robots Torque Uncertainty Heuristic algorithms Torque control Reinforcement learning

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Design and Evaluation of Adaptive and Sliding Mode Control for a 3-DOF Delta Parallel robot

Design and Evaluation of Adaptive and Sliding Mode Control f...

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Iranian Conference on electrical engineering (ICEE)

作者： Faraz Abed Azad Saeed Rahimi Mohammad Reza Hairi Yazdi Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

In this paper, simulation, kinematics analysis, and control of a 3-DOF Delta parallel robot have been investigated. Delta parallel robots are one of the most popular manipulators for industrial applications. The kinematic equations of the robot have been established by the screw theory, in which, the position and velocity analyses have been carried out. In SimScape simulation environment, the kinematic model of the robot has been verified and subsequently, an inverse kinematic model was adopted for control purposes. In order to improve the tracking ability of the robot, three important control approaches, consisting in PID, adaptive control, and a Sliding Mode Control (SMC) have been implemented. Afterwards, the effectiveness of each approach has been investigated by employing the root mean square error (RMSE) index. By comparing the obtained index values, the SMC demonstrates the best performance with the RMSE value of 0.4188 in comparison to other methods. Simulation results were illustrated to show the ability of the controllers to reject white noise disturbance added to the system's input signals.

关键词： robots Kinematics Mathematical model Service robots Fasteners Actuators Parallel robots

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A deep learning based automated hand hygiene training system

arXiv

引用

arXiv 2021年

作者： Shahbandeh, Mobina Ghaffarpour, Fatemeh Vali, Sina Haghpanah, Mohammad Amin Torkamani, Amin Mousavi Masouleh, Mehdi Tale Kalhor, Ahmad School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory University of Tehran Tehran Iran Department of Computational Engineering Lappeenranta University of Technology Lappeenranta Finland School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran

Hand hygiene is crucial for preventing viruses and infections. Due to the pervasive outbreak of COVID-19, wearing a mask and hand hygiene appear to be the most effective ways for the public to curb the spread of these viruses. The World Health Organization (WHO) recommends a guideline for alcohol-based hand rub in eight steps to ensure that all surfaces of hands are entirely clean. As these steps involve complex gestures, human assessment of them lacks enough accuracy. However, Deep Neural Network (DNN) and machine vision have made it possible to accurately evaluate hand rubbing quality for the purposes of training and feedback. In this paper, an automated deep learning based hand rub assessment system with real-time feedback is presented. The system evaluates the compliance with the 8-step guideline using a DNN architecture trained on a dataset of videos collected from volunteers with various skin tones and hand characteristics following the hand rubbing guideline. Various DNN architectures were tested, and an Inception-ResNet model led to the best results with 97% test accuracy. In the proposed system, an NVIDIA Jetson AGX Xavier embedded board runs the software. The efficacy of the system is evaluated in a concrete situation of being used by various users, and challenging steps are identified. In this experiment, the average time taken by the hand rubbing steps among volunteers is 27.2 seconds, which conforms to the WHO guidelines. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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A valid frequency range analysis of throat signal for voice command system

A valid frequency range analysis of throat signal for voice ...

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2011 International Conference on Information Science and Applications, ICISA 2011

作者： Jung, Young-Giu Han, Mun-Sung Chung, Kyung-Yong Lee, Sang Jo Multimodal and Human Interaction Lab. YM-NaeulTech. Incheon Korea Republic of Robot/Cognition System Research Department ETRI Deajeon Korea Republic of School of Computer Information Engineering Sangji University Wonju Korea Republic of Language and Information Processing Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)9781424492244

In this paper, a valid frequency range of throat signal analyses is presented. Compare to speech signals, the throat signals have many differences in the amount of information. In this context, targeting on throat signals to find valid frequency range is one of the most important analyses for the development of new breed of feature extraction algorithm. In this respect, using analysis similarity of throat signals feature distribution and correlation analysis of throat signals in the frequency domain, the frequency range can be found with effective and quick response time to be applied to a voice command recognition system using neck-microphone. As a result, the proposed frequency range is from 200Hz(center frequency) to 2001Hz(center frequency). For the neck-microphone voice command system using the proposed frequency range, a significant reduction in time complexity of 25% can be achieved without any loss in recognition rate. © 2011 IEEE.

关键词： Microphones

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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger robot Hand

A 2D Geometry Based Grasping Pose Generation Algorithm for a...

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Iranian Conference on electrical engineering (ICEE)

作者： Arash Akbari Arman Akbari Mehdi Tale Masouleh Department of Computer Science University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

robot grasping is of paramount importance in industrial and service robotics. In recent years, various data-driven algorithms have been proposed to solve the problem of grasp detection and a part of them are based on reinforcement learning (RL) approaches. In a variety of proposed algorithms, random key points are being employed which will make the learning process inefficient and time-consuming. In this paper, a geometry-based algorithm is presented which can find grasp poses based on the geometry of the unknown object and propose the ones which may lead to successful grasping. For the grasp contacts computation part, the presented algorithm produces a finite number of key points based on the 2D shape of the object from a specific point of view. Afterward, it will narrow down the candidate points and output a finite number of successful grasp poses based on three grasp quality metrics for various unknown objects. Three approaches are proposed in order to achieve center points which can describe different parts of a 2D shape. Then, the obtained points are used as the center of circles which are tangent to the 2D shape contour. Also, a new grasp quality metric is proposed. The time of the grasp and the amount of object disorientation after grasping are considered as a metric to evaluate the successfulness of the grasp. Simulation results demonstrate that the proposed algorithm for unknown object grasping can find a finite number of successful grasp poses for different seen or unseen objects without using any random point,

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Generating Synthetic Medical Images by Using GAN to Improve CNN Performance in Skin Cancer Classification

Generating Synthetic Medical Images by Using GAN to Improve ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Pooyan Sedigh Rasoul Sadeghian Mehdi Tale Masouleh Science and Research Branch Islamic Azad University Tehran Iran Human and Robot Interaction Laboratory Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

One of the main reasons of slow progress in using deep learning methods for cancer detection is the lack of data, especially the annotated data which is usually used for supervised learning algorithms. This paper presents a Convolutional Neural Network (CNN) to detect skin cancer. The primary database which is used to train the designed CNN algorithm has 97 members (50 benign and 47 malignant), which are collected from the International Skin Imaging Collab.ration (ISIC). In order to compensate the lack of data for training the proposed CNN algorithm, a Generative Adversarial Network (GAN) is designed to produce synthetic skin cancer images. The classification performance of the designed trained CNN without the obtained synthetic images is near 53%, but by adding the synthetic images to the primary database the performance of the model is increased to 71%.

关键词： Gallium nitride Generative adversarial networks Databases Skin cancer Medical diagnostic imaging

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Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel robot

Practical Implementation of Real-Time Waste Detection and Re...

引用

International eConference on computer and Knowledge engineering (ICCKE)

作者： Hasan Jalali Shaya Garjani Ahmad Kalhor Mehdi Tale Masouleh Parisa Yousefi School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Computer Engineering Imam Reza International University Mashhad Iran

Intelligent robots play an essential role in waste management and recycling due to their high speed and a wide variety of applications. In this paper, two methods for waste detection and accurate pick-and-place based on computer vision and neural networks are presented. The suggested methods have been put into practical application on a 3-DOF Delta parallel robot to show the accuracy and fastness of the foregoing method for real intelligence systems. The first method, Multi-Stage Detection, consists of two stages to detect the waste objects, namely, object localization and segmentation, and classification. The second method, known as One-Stage object detectors, such as YOLOv5, has the capability to simultaneously localize and classify the waste objects. The dataset utilized in this paper relies on the TrashNet dataset as its foundation. In order to improve the classification capabilities in the multi-stage method, a larger dataset was created by utilizing data augmentation. Also, for the one-stage method, a new multi-lab.l dataset is constructed based on the TrashNet dataset. Additionally, the results of the experimental implementation were compared based on time and evaluation metrics for detection and classification. The ResNet50 model achieved the highest accuracy in the multi-stage method, with 99.31% accuracy. In the one-stage detection method, the YOLOv5x model achieved the best mAP (@IoU = 0.75) of 97.4%, which outperformed the YOLOv5s model by 0.8 percent; however, the inference speed of the YOLOv5x in comparison with the YOLOv5s models was six times as slow. Therefore, the YOLOv5s model was employed in real-time online waste detection, which resulted in 82.1% mAP (@IoU = 0.5) after being trained on real images from the waste-sorting platform.

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