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

作者： Alireza Fateh Hamidreza Momeni Mehdi Tale Masouleh dept. of Electrical and Computer Eng Tarbiat Modares University Tehran Iran Human and Robot Interaction Lab School of Electrical and Computer Eng University of Tehran Tehran Iran

Time Delay Control (TDC) is recognized as a simple yet effective alternative to model-based or intelligent-based control due to its simplicity and robustness. Proportional-Integral-Derivative (PID) control is one of the industry’s most used and straightforward control schemes. While this approach has proven to be effective, the tuning of its coefficients has remained a difficult task. Though showing similar performance, a definite connection between the two approaches has yet to be defined. In this paper, by taking advantage of padé approximation, a direct connection between the two control methods coefficients is obtained. As a result, a new approach for choosing the classical PID controller coefficients is introduced using TDC as the primary control scheme design framework, which has led to a decrease in the number of required control design coefficients of the PID control system. Further on, the uniformly ultimately bounded stability of the control system under bounded external disturbances is proven. Finally, via simulations on the SCARA robot, the obtained results and the validity of the proposed connection are explored.

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Designing Controller for the 2-DoF Cable-Driven Serial robot with Variable Configuration based on Adaptive Neuro-Fuzzy Systems

Designing Controller for the 2-DoF Cable-Driven Serial Robot...

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

作者： Vahid Bahrami Ahmad Kalhor Mehdi Tale Masouleh School of Electrical and Computer Engineering University of Tehran Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9781665454537

This paper addresses the problem of actuation and design of a controller for a 2-DoF Cable-Driven Serial Chain (CDSC) with variable configuration based on adaptive neuro-fuzzy systems. The mentioned structure uses several light cables to actuate rigid links of a passive planar serial robot, performing a 2-DoF motion pattern, which can apply tensile forces for moving the links and thus produce the necessary torque to track the desired reference model by the robot’s end-effector. Additionally, cable attachment point coordinates of the CDSC are considered variable configurations. So, a novel concept is expressed in the calculation of the CDSC’s Jacobian matrix. In this mechanism, upon formulating the sliding mode error dynamics, an Adaptive Neural Network (ANN) is used to cope with complex mathematical calculations of the CDSC dynamics. Then, the coordinates of cables are considered as variables that depend on the main states of the plant. These co-states are adaptively determined by applying an appropriate fuzzy system. As a result, an Adaptive Neuro-Fuzzy Controller (ANFC) algorithm is introduced for the mentioned redundant mechanism. Finally, by defining a new Cost of Redundancy (CoR), an optimal configuration of the cables is found, so the maximum Degrees of Redundancy (DoR) is obtained. The simulation results indicate that using the ANFC algorithm leads to a reduction of 2.23% in the CoR for the 2-DoF CDSC.

关键词： Adaptive systems Costs Torque Tracking robot kinematics Heuristic algorithms Simulation

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Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator

SSRN

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SSRN 2022年

作者： Chehelgami, Shirin Ashtari, Erfan Basiri, Mohammad Amin Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this research, a global path planning method based on recurrent neural networks by means of a new loss function is presented, which regardless of the complexity of the configuration space, generates the path in a relatively constant time. The new loss function is defined in such a way that in addition to learning the input data of the network, it creates an adjustable safety margin around the obstacles and ultimately creates a safe path. Moreover, a new global path planning method is also introduced, which is used to create the dataset needed to train the proposed neural network. The convergence of this method is mathematically proven and it is shown that this method can also produce a suboptimal path in a much shorter time than the common methods of global path planning reported in the literature. In short, the main purpose of this research consists in providing a method which can create a suboptimal, fast and safe path for a mobile robot from any random starting point to any random destination in a known environment. First, the proposed methods will be implemented for different two-dimensional environments consisting of convex and non-convex obstacles, considering the robot as a pointmass, and then it will be implemented in a 3D simulation environment, *** to classical global path planning algorithms, such as RRT and A*, the proposed approach demonstrates better performance in complex and challenging environments. © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Deep-HFID: Deep Neural Network Based Hands and Face bio-metric Identification System Using Metric Learning

Deep-HFID: Deep Neural Network Based Hands and Face bio-metr...

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

作者： Zahra Parvin Ashtyani Sara Ghanbari Navid Zare Mehdi Tale Masouleh Ahmad Kalhor School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665454537

This paper presents a device for person identification based on hand bio-metrics. The main objective of a person recognition device is to perform accurate identification at a reasonable production cost. The so-called Deep-HFID device is designed to meet these objectives as well as to provide users with ease of use. For the purpose of identifying individuals, this device uses hand and face bio-metrics. In terms of dimensions, this device has been designed to facilitate the use of a variety of individuals. For ease of deployment, the Deep-HFID system relies on a server for most of its processes. The routine tasks, like showing the graphical user interface are handled by a Raspberry Pi board. A dataset from the palm side of both hands from 110 individuals has been created. Using transfer learning, the six pre-trained state-of-the-art neural network architecture was fine-tuned and calibrated based on the F1 score and precision criteria. An augmented dataset containing samples from the palm side of 30 individuals with between fingers angles variation is collected. By applying the main and augmented dataset and using the center-based separation index as a complexity measure algorithm to rank pre-trained deep neural networks, a compression on the last layer of the best performed neural network, EfficientNetV2B1, is done which results in a more robust system without reducement in accuracy.

关键词： Performance evaluation Training Face recognition Neurons Liquid crystal displays Libraries Servers

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition

SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Farbod Siahkali Seyed Amirmahdi Alavi Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Computer Science Islamic Azad University Central Tehran Branch Tehran Iran

ISBN: (纸本)9781665476249

In this paper, a new publicly availab.e 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks. 1 https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition

关键词： Representation learning Image recognition Video tracking Machine learning algorithms Neural networks Real-time systems Automobiles

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Experimental Study on Motion Controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel robot

Experimental Study on Motion Controller based on NN-ARX and ...

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

作者： Hasan Jalali Hossein Damavandi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers.

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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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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao robot: A Closed-Loop Approach

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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

作者： Keyhan Rayati Amirhossein Feizi Alireza Beigy Pourya Shahverdi Mehdi Tale Masouleh Ahmad Kalhor Wing-Yue Geoffrey Louie Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan USA

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs.

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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 Optimisation of Electronic Nose with Multichannel Gas Sensor

Design Optimisation of Electronic Nose with Multichannel Gas...

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International Conference on Sensing Technology, ICST

作者： Yugal Pachori Richa Thakur Avinash Prashant D. Kulkarni Dinesh Singh Aditya Nigam Shubhajit Roy Chowdhury Biomedical Systems Lab School of Computing and Electrical Engineering Indian Institute of Technology Mandi Centre for Human Computer Interaction Indian Institute of Technology Mandi Multimedia Analytics Networks and Systems Lab School of Computing and Electrical Engineering Indian Institute of Technology Mandi

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

human nose detects every kind of odors and our brain responds to every kind of smell that we are inhaling, so in the same way artificial nose or the electronic nose is a device that detects the smell and can enable the scope of digital smell technology. So, this paper proposes to optimize the design of an electronic nose that will eventually bring modification to the various digital platforms like internet, computers, mobile, television and tablets. We focused on design optimization of electronic nose which is built on a multichannel gas sensor array. The proposed electronic nose will enable the transmission or reception of smell over the internet and other digital platforms. The device can detect the smells of vapors coming out of volatile organic compounds. The sensing system is 3D printed in the model of a human nose. The sensing system works with an accuracy of detection of 90.45%. The response time of the electronic nose is 60 to 90 seconds, sometimes it is lesser than 60 seconds and it varies from substance to substance.

关键词： Volatile organic compounds Solid modeling TV Nose Electronic noses Three-dimensional printing Sensors Time factors Gas detectors Sensor arrays

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