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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger robot Hand 31

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

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31st International Conference on electrical engineering, ICEE 2023

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

ISBN: (纸本)9798350312560

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 © 2023 IEEE.

关键词： Geometry

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Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel robot 13

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

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

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

ISBN: (纸本)9798350330151

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. © 2023 IEEE.

关键词： Classification (of information)

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Robust PID Control of robot Manipulator Using Time Delay Control Design Approach 11

Robust PID Control of Robot Manipulator Using Time Delay Con...

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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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Accuracy Comparison of Spherical Parallel Manipulators Based on Joint Clearance 5

Accuracy Comparison of Spherical Parallel Manipulators Based...

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5th RSI International Conference on robotics and Mechatronics, IcRoM 2017

作者： Mojtaba, Seyed Mousavi, Seyed Khoogar, Ahmadreza Masouleh, Mehdi Tale Mechanical Engineering Department Malek Ashtar University of Technology Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Iran

ISBN: (纸本)9781538657034

In this paper, three types of Spherical Parallel Manipulators (SPM) are compared from accuracy point of view based on joint clearances. The 3-RRR SPM is an overconstraint parallel mechanism and one can presume that it may exhibits an accurate motion. But beside the foregoing advantage, this structure has some drawbacks such as requiring high manufacturing precision and the difficulty of assembling the mechanism. Two other types, 3-RRS and 3-RSR, are non-overconstrained and do not have the above disadvantages, therefore these two types with 3- RRR are put into contrast from accuracy point of view. First, a method to obtain a model of moving platform pose (position and orientation) error is introduced which leads to a standard convex optimization problem. Since error calculation depends on the end-effector configuration, more than 1000 configurations are taken into account in order to cover SPMs workspaces. Then maximum values of six components of the pose error in each configuration are computed for each type. Finally, the three SPMs are compared based on maximum position and orientation error for given joint clearances. The obtained results revealed that the 3- RRR SPM has better position accuracy while in the case of orientation, the 3-RRS SPM has lowest maximum error between SPMs under study in the prescribed workspace. © 2017 IEEE.

关键词： Mechanisms

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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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A dual-channel beamformer based on time-delay compensation estimator and shifted PCA for speech enhancement

A dual-channel beamformer based on time-delay compensation e...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Zhang Jie Hong Liu Open Lab on Human-Robot Interaction School of Electrical and Computer Engineering Peking University China Open Lab on Human-Robot Interaction School of Electrical and Computer Engineering Peking University Beijing China

Speech enhancement is an essential technique to process degraded audio in various applications. Beamforming to eliminate interferences based on sensor arrays is the most well-known method for this issue. However, traditional beamformers often face magnitude incoherence towards received signals due to directional weighting. Therefore, a novel dual-channel beamformer based on time-delay compensation (TDC) and shifted principal components analysis (PCA) is presented in this work. Firstly, our enhancement algorithm utilizes TDC estimator to preserve binaural cues, including interaural time-delay and intensity difference. Then the estimated cues are comprised to improve the shifted PCA, which can reduce noise by extracting primary components. Finally, the aforehand processed audio are input to a beamformer with post-filter to obtain enhanced speech. Experiments have demonstrated that the proposed algorithm could achieve some superiorities in speech intelligibility compared with the state-of-the-arts against real scenarios.

关键词： Speech Principal component analysis Speech enhancement Signal to noise ratio Array signal processing

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Road Sign Classification Using Transfer Learning and Pre-trained CNN Models 1

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1st International Conference on Artificial Intelligence and Smart Vehicles, ICAISV 2023

作者： Hosseini, Seyed Hossein Ghaderi, Foad Moshiri, Behzad Norouzi, Mojtaba Human Computer Interaction Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran School of ECE University of Science and Technology Tehran Iran

ISBN: (数字)9783031437632

ISBN: (纸本)9783031437625

In this paper, we propose a transfer learning-based approach for road sign classification using pre-trained CNN models. We evaluate the performance of our fine-tuned VGG-16, VGG-19, ResNet50 and EfficientNetB0 models on the German Traffic Sign Recognition Benchmark (GTSRB) test dataset. Our work makes several contributions, including the utilization of transfer learning with pre-trained CNN models, the integration of augmentation techniques, and a comprehensive evaluation of multiple state-of-the-art architectures on the road sign classification task. Our experimental results show that the proposed approach achieves an average accuracy of 99.2% on the GTSRB test dataset, outperforming existing state-of-the-art approaches. The results confirm that transfer learning and pre-trained models can significantly improve the accuracy of road sign classification, even with a relatively small dataset. Our study contributes to the field of intelligent transportation systems by providing an efficient and accurate method for road sign classification. We believe that our approach can be easily extended to other computer vision tasks and applied to real-world scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao robot: A Closed-Loop Approach 11

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

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

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

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： Emotion Recognition

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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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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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