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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

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

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： Time delay

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robotic Sorting of Mechanical and electrical Parts: An Autonomous Vision-Based Approach in a Practical Case Study

Robotic Sorting of Mechanical and Electrical Parts: An Auton...

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International Conference on Control, Instrumentation, and Automation

作者： Mohammad Yousefi Mahdi Dalvand Navid Asadi Khomami Mehdi Tale Masouleh Mohammad Reza Haeri Yazdi Human and Robot Interaction Lab. School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Lab. School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

In today’s industrial landscape, automation has become increasingly vital, particularly in the deployment of robots for tasks such as sorting machine components. The use of robotic systems enhances process accuracy and speed, resulting in significant cost reductions and improved productivity compared to manual lab.r. This paper aims to design and develop an automated sorting system for various types of mechanical and electrical parts, utilizing image processing and machine vision algorithms with a Delta parallel robot equipped with a two-finger gripper. The target mechanical and electrical parts in this study are screws, nuts, metal washers, rubber washers, retaining rings, rectangular keys, wall plugs, resistors, potentiometers, capacitors, batteries, ICs, and LEDs. The YOLOv3 algorithm and Adaptive Thresholding method are employed to detect and distinguish objects from the background, and size measurement is achieved with the help of a custom marker with known dimensions. In this study, transfer learning based on pre-trained weights of YOLOv3 for the COCO dataset is applied. The proposed system attains a final mean Average Precision (mAP@0.5) value exceeding 0.95 for part detection using YOLOv3. Additionally, it demonstrates an overall pick-and-place success rate exceeding $\mathbf{9 0 \%}$.

关键词： YOLO Parallel robots Automation Thresholding (Imaging) Service robots robot kinematics Transfer learning Size measurement Grippers Sorting

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Synergy of Deep Learning and Artificial Potential Field Methods for robot Path Planning in the Presence of Static and Dynamic Obstacles 30

Synergy of Deep Learning and Artificial Potential Field Meth...

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30th International Conference on electrical engineering, ICEE 2022

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

ISBN: (纸本)9781665480871

In a fast-changing world we are in today, unmanned vehicles are displacing old, obsolete and frustrating tasks. Since unmanned vehicles are intended to work in an environment without any conduction, finding a collision-free path of movement is of paramount importance and a definite asset in practice. The objective of a global path planner consists in generating an optimized path without considering unknown obstacles. In contrast, a local path planner properly avoids the unknown obstacles while getting close to the goal. This results in an inefficient movement, particularly in complex circumstances. In this paper, a complete global and local path planning method is proposed for avoiding both static and dynamic obstacles. The proposed method benefits the advantages of both approaches while covering each other's weaknesses. First, a method for generating numerous obstacle-free paths from random pairs of start and goal points is introduced. Next, a novel deep-learning approach is proposed in order to train the robot in an environment free of moving obstacles. After extracting the desired via points, an efficient Artificial Potential Field (APF) approach for attaining the local path planning is introduced with the aim of avoiding dynamic obstacles while the robot travels through the aforementioned via points. The proposed method can be well extended to different platforms such as mobile robots, arm robots, quadrotors, etc;in this paper, both local and global path planning methods are implemented on a simulated quadrotor to examine the robot's performance for both approaches. Furthermore, it has been revealed that implementing both approaches should be implemented seamlessly in order to attain a complete efficient path planning with the presence of both static and dynamic obstacles. Finally, both local and global path planning methods are implemented on a detailed simulated quadrotor to evaluate the robot's performance for the aforementioned approaches. © 2022 IEEE.

关键词： Motion planning

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User Identification Based on Hand Geometrical Biometrics Using Media-Pipe 30

User Identification Based on Hand Geometrical Biometrics Usi...

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30th International Conference on electrical engineering, ICEE 2022

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

ISBN: (纸本)9781665480871

The main objective of a hand-geometry-based user identification model consists in designing a method that could be implemented with a satisfactory trade-off between cost, accuracy, and ease of utilizing. In this paper, an efficient, peg-free hand geometry-based approach for user identification is presented, which can be easily implemented with low-cost pieces of equipment and results up to a 98.7% accuracy in user identification. Three different feature-based methods, namely, circular representation, FPL, and FPLKW, are designed and examined. In order to extract required landmarks to the end of generating intended features, the so-called Media-Pipe is used. The first method is the circular representation, in which the radius ratio of the circles formed by the three middle finger straps is taken as model features. The second method extracts fingers length and phalanges length of each finger and uses their ratio as features, which is called Finger Phalanges Length (FPL) method. The third method extracts Fingers Length and Phalanges Length of each finger and Knuckles Width, abbreviated as (FPLKW), and uses their ratios for extracting features. Two datasets are collected, one for examining the efficiency of the proposed methods and finding the promising one, and the second one for validating the performance of the selected method. The first dataset contains images of the back-side of both hands in spread fingers mode and picked altogether fingers mode, taken from 21 individuals on a black background. The second dataset contains images of 100 individual hands in the front-side of both hands and the back-side of both hands in spread fingers mode. Different classifications are used to obtain maximum accuracy, including SVM SVC, SVM NuSVC, the decision tree classifier, the extra tree classifier, the random forest classifier, and one hidden layer MLP. Obtained results reveal that the extra tree classifier leads to better performance with respect to the methods mentioned abov

关键词： Biometrics

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Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes

Scene Understanding in Pick-and-Place Tasks: Analyzing Trans...

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Iranian Conference on electrical engineering (ICEE)

作者： Seraj Ghasemi Hamed Hosseini MohammadHossein Koosheshi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

With robots increasingly collab.rating with humans in everyday tasks, it is important to take steps toward robotic systems capable of understanding the environment. This work focuses on scene understanding to detect pick-and-place tasks given initial and final images from the scene. To this end, a dataset is collected for object detection and pick-and-place task detection. A YOLOv5 network is subsequently trained to detect the objects in the initial and final scenes. Given the detected objects and their bounding boxes, two methods are proposed to detect the pick-and-place tasks which transform the initial scene into the final scene. A geometric method is proposed which tracks objects' movements in the two scenes and works based on the intersection of the bounding boxes which moved within scenes. Contrarily, the CNN-based method utilizes a Convolutional Neural Network to classify objects with intersected bounding boxes into 5 classes, showing the spatial relationship between the involved objects. The performed pick-and-place tasks are then derived from analyzing the experiments with both scenes. Results show that CNN-based method, by VGG-16 backbone, outscores the geometric method by roughly 12% in certain scenarios, with an overall success rate of 84.3%.

关键词： YOLO electrical engineering Accuracy Tracking Transforms Feature extraction Manipulators

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Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes

arXiv

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arXiv 2024年

作者： Ghasemi, Seraj Hosseini, Hamed Koosheshi, MohammadHossein Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran

With robots increasingly collab.rating with humans in everyday tasks, it is important to take steps toward robotic systems capable of understanding the environment. This work focuses on scene understanding to detect pick-and-place tasks given initial and final images from the scene. To this end, a dataset is collected for object detection and pick-and-place task detection. A YOLOv5 network is subsequently trained to detect the objects in the initial and final scenes. Given the detected objects and their bounding boxes, two methods are proposed to detect the pick-and-place tasks which transform the initial scene into the final scene. A geometric method is proposed which tracks objects’ movements in the two scenes and works based on the intersection of the bounding boxes which moved within scenes. Contrarily, the CNN-based method utilizes a Convolutional Neural Network to classify objects with intersected bounding boxes into 5 classes, showing the spatial relationship between the involved objects. The performed pick-and-place tasks are then derived from analyzing the experiments with both scenes. Results show that CNN-based method, by VGG-16 backbone, outscores the geometric method by roughly 12% in certain scenarios, with an overall success rate of 84.3%.MSC Codes computer Vision and Pattern Recognition (***), Systems and Control (***) © 2024, CC BY.

关键词： Convolutional neural networks

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition 8

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

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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

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

ISBN: (纸本)9781665476232

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.1https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition © 2022 IEEE.

关键词： Model automobiles

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Experimental Study on Motion Controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel robot 11

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

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

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

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： Controllers

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Sound Source Localization in a 3D Multi-Surface Environment Using Learning-Based Methods

Sound Source Localization in a 3D Multi-Surface Environment ...

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

作者： Soroush Esfahanian Keyhan Rayati Mehdi Tale Masouleh Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This study presents a three-dimensional localization of a ball drop in a multi-surface environment using cost-effective data acquisition devices, and proposes two learning-based methods to improve the baseline classical localization method based on GCC-PHAT-estimated TDOA values. The first proposed method employs a ball drop surface classification executed by Random Forest and XGBoost models to enhance localization performance, whereas the second method conducts region classification by using both basic and multi-branch neural network models, achieving improved and more efficient localization performance. A dataset of real-world sparse ball drop samples was collected in a controlled multi-surface environment for experimental execution, where results demonstrate that the two proposed methods improve the localization performance across various scenarios, with the region classification method significantly outperforming the baseline method, achieving a 17% reduction in the Mean Euclidean Distance error metric. Furthermore, the surface and region classification methods reduce the localization time compared to the baseline method by 50% and 97%, respectively. The obtained results reveal the potential of combining simple learning-based models with affordable microphone sensors for achieving an accurate localization of sparse audio samples in a multi-surface environment.

关键词： Location awareness Learning systems Visualization Artificial neural networks Data models Sensors robots Random forests Microphones Context modeling

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Explorable Grasp Pose Detection for Two-Finger robot Handover

Explorable Grasp Pose Detection for Two-Finger Robot Handove...

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Iranian Conference on electrical engineering (ICEE)

作者： AliReza Beigy Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

The efficient generation of datasets for deep neural networks is crucial for enhancing their performance, especially in complex tasks like grasp pose detection in robotic grasp planning. Traditional methods often resort to random selection or rely on human-annotated datasets, making it challenging for networks to accurately learn graspable areas. For 3D grasp pose detection, an exhaustive search within an expansive space of points consumes substantial time and often overlooks an object's potential graspable regions. This paper introduces an algorithm capable of leveraging depth sensor information, particularly from devices like Kinect, to precisely determine the best grasp poses for object handovers. The proposed method, named Explorable Grasp Detection is based on the idea of exploitation and exploration, ensuring there is no redundant grasp detection within the same region. The results derived from DexNet's mesh objects dataset indicate remarkable computational efficiency, achieving grasp results in less than one second for various objects and maintaining a high success rate. Furthermore, error functions as geometry grasp quality metrics tailored for a two-finger gripper are incorporated to evaluate the grasp quality. One of the most notable contributions of this paper is the algorithm's ability to not only detect optimal grasping regions rapidly but also to serve as an instrumental tool for generating annotated datasets, thereby pushing forward the boundaries in robotic object manipulation techniques.

关键词： Point cloud compression Three-dimensional displays Instruments Grasping Handover robot sensing systems Search problems

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