检索结果-内蒙古大学图书馆

Iranian Conference on electrical engineering (ICEE)

作者： Reihaneh Yourdkhani Arash Tavoosian Navid Asadi Khomami Mehdi Tale Masouleh Electrical and Computer Engineering Department Human and Robot Interaction Laboratory University of Tehran Mechanical Engineering Department University of Tehran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper introduces a pioneering experimental study on the automated packing of a catering package using a two-fingered gripper affixed to a 3-degree-of-freedom Delta parallel robot. A distinctive contribution lies in the application of a deep learning approach to tackle this challenge. A custom dataset, comprising 1,500 images, is meticulously curated for this endeavor, representing a noteworthy initiative as the first dataset focusing on Persian-manufactured products. The study employs the YOLOV5 model for object detection, followed by segmentation using the FastSAM model. Subsequently, rotation angle calculation is facilitated with segmentation masks, and a rotated rectangle encapsulating the object is generated. This rectangle forms the basis for calculating two grasp points using a novel geometrical approach involving eigenvectors. An extensive experimental study validates the proposed model, where all pertinent information is seamlessly transmitted to the 3-DOF Delta parallel robot. The proposed algorithm ensures real-time detection, calibration, and the fully autonomous packing process of a catering package, boasting an impressive over 80% success rate in automatic grasping. This study marks a significant stride in advancing the capabilities of robotic systems for practical applications in packaging automation.

关键词： Deep learning YOLO Parallel robots Image segmentation 3-DOF Focusing Grasping

来源：评论

学校读者我要写书评

暂无评论

Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks 12

Real-Time Facial Expression Recognition using Facial Landmar...

引用

12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

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

ISBN: (纸本)9781665412162

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. © 2022 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Experimental Study on Chess Board Setup Using Delta Parallel robot Based on Deep Learning

Experimental Study on Chess Board Setup Using Delta Parallel...

引用

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

作者： Parsa Yarmohammadi Navid Asadi Khomami Mehdi Tale Masouleh Mohammad Reza Zakerzadeh 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 University of Tehran Tehran Iran

In recent years, robotics and object detection applications have seen remarkable improvements in executing routine tasks. In the near future, it is expected that most of the tasks can be performed by means of these two foregoing tools. This paper presents a system that combines these two approaches in order to automate the setup of a chessboard using a Delta parallel robot equipped with a two-finger gripper designed for grasping small objects. This system utilizes the so-called YOLO model for object detection in order to detect the chess pieces. The system begins by taking a picture of a scene, where the chess pieces are placed randomly around a board. The location of the chessboard is predefined and YOLO is used to detect and locate chess pieces in the given picture. The final object detection model used to execute this task is YOLOv5m, although this model encountered some challenges in detecting four types of chess pieces from a top view, these challenges were addressed by increasing the instances of those objects in the dataset. Upon extracting the location of each piece, the coordinates are sent to the robot, and the Delta parallel robot arranges the initial setup of a chess game. The model achieved an overall mAP of 0.99, indicating excellent precision and the grasp success rate in the process of grasping the chess pieces was 95%. This study demonstrates the potential of automating everyday tasks by exploring the intersection of robotics and artificial intelligence and can be well extended to tasks, namely, sorting medications and pills in hospitals, sorting different wastes like plastic and papers for recycling, identifying and removing spoiled fruits in agricultural industries, etc.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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 labor. 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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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,

关键词：

来源：评论

学校读者我要写书评

暂无评论

Experimental Study on Reducing the Oscillations of a Cable-Suspended Parallel robot for Video Capturing Purposes using Simulated Annealing and Path Planning 7

Experimental Study on Reducing the Oscillations of a Cable-S...

引用

7th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2021

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

ISBN: (纸本)9781665409384

This paper aims to smoothen the movements of an under-constrained cable-suspended parallel robot which carries a camera for video capturing purposes, especially for video capturing of football games. This goal is achieved by means of an accurate while simple PID controller optimized by the Simulated Annealing algorithm and implemented on the joint space. Moreover, a planning strategy is considered for the joint space trajectory of the robot which guarantees the zero jerk, acceleration, and velocity at the start and the end of motions. On this regard, a septic function for each joint of the robot is considered and the corresponding boundary conditions are applied to the function to make the end-effector movements less oscillatory. This method is implemented and tested on an experimental setup while the end-effector oscillation data is recorded using an IMU sensor attached to the end-effector of the robot. Applying frequency analysis on the oscillatory data of the end-effector reveals that this simple method, on average, resulted in a 33.8% reduction in the average amplitude of the end-effector oscillations. Moreover, the maximum joint space error was decreased by 76.68% when using septic joint profile compared to the ordinary linear Cartesian trajectory planning approach. Upon applying the proposed strategy, the error of the controller has been reduced by 92.26% with respect to the previous research performed on this experimental setup. Without requiring any knowledge on the dynamic model of the robot or the natural frequencies of the end-effector or using any complex controller, this method significantly increased the smoothness and accuracy of the robot movements. The proposed method can be regarded as a definitive asset when this robot is used for video capturing purposes. © 2021 IEEE.

关键词： Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Relocation Tracking in Dynamic Kitchen Environments

arXiv

引用

arXiv 2025年

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. © 2025, CC BY.

关键词： Optical flows

来源：评论

学校读者我要写书评

暂无评论

AGILE: Approach-based Grasp Inference Learned from Element Decomposition

arXiv

引用

arXiv 2024年

作者： Koosheshi, MohammadHossein Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Yazdi, Mohammad Reza Hairi 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

humans, this species expert in grasp detection, can grasp objects by taking into account hand-object positioning information. This work proposes a method to enable a robot manipulator to learn the same, grasping objects in the most optimal way according to how the gripper has approached the object. Built on deep learning, the proposed method consists of two main stages. In order to generalize the network on unseen objects, the proposed Approach-based Grasping Inference involves an element decomposition stage to split an object into its main parts, each with one or more annotated grasps for a particular approach of the gripper. Subsequently, a grasp detection network utilizes the decomposed elements by Mask R-CNN and the information on the approach of the gripper in order to detect the element the gripper has approached and the most optimal grasp. In order to train the networks, the study introduces a robotic grasping dataset collected in the Coppeliasim simulation environment. The dataset involves 10 different objects with annotated element decomposition masks and grasp rectangles. The proposed method acquires a 90% grasp success rate on seen objects and 78% on unseen objects in the Coppeliasim simulation environment. Lastly, simulation-to-reality domain adaptation is performed by applying transformations on the training set collected in simulation and augmenting the dataset, which results in a 70% physical grasp success performance using a Delta parallel robot and a 2-fingered gripper. © 2024, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

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-label 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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Relocation Tracking in Dynamic Kitchen Environments

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

引用

International eConference on computer and Knowledge engineering (ICCKE)

作者： Arash Nasr Esfahani Hamed Hosseini Mehdi Tale Masouleh Ahmad Kalhor Hedieh Sajedi Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

关键词： YOLO Accuracy Three-dimensional displays Computational modeling Heuristic algorithms Training data Smart homes Data models Object recognition Testing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：