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

作者： Koosheshi, Mohammadhossein Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Yazdi, Mohammad Reza Hairi University of Tehran Human and Robot Interaction Laboratory School of Mechanical Engineering Iran University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Iran University of Tehran School of Mechanical Engineering College of Engineering Iran

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： Grippers

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On Graph Reduction in Consensus Control of Multi-agent Systems: A Preliminary Study

On Graph Reduction in Consensus Control of Multi-agent Sys...

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27th RoboCup International Symposium, 2024

作者： Setayeshi, Amirali Babi, Zeynab Ezzati Saadati, Ali Sadeghnejad, Soroush Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Tehran Iran

ISBN: (纸本)9783031858581

Since the use of multi-agent systems has significantly increased over the past few decades, it is crucial to study the methods used in this area and take implementation-related practical issues into consideration. For instance, in the formulation of the consensus problem, the communication between the agents is depicted using a graph, where each edge represents a communication link between the agents. In practical terms, there will be significant operational challenges and costs associated with implementing each of these communication channels in various forms. Therefore, in this study, the graph reduction problem has been presented as an optimization problem, accompanied by six distinct cost functions. The objective is to reduce the size of a graph without compromising its fundamental characteristics. Hence, the cost functions provided are formulated to minimize the number of edges in the resulting graph while ensuring that the consensus error remains bounded. Ultimately, simulations were performed to demonstrate the effectiveness of the proposed approach. The outcomes derived from the specified set of cost functions were then compared to several conventional topologies in multi-agent systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cost functions

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JoTyMo: Joint Type and Movement Detection from RGB Images of before and after Interacting with Articulated Objects 11

JoTyMo: Joint Type and Movement Detection from RGB Images of...

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

作者： Forootan, Ehsan Ghasemi, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Moshiri, Behzad University of Tehran Human and Robot Interaction Laboratory Electrical and Computer Engineering Department Iran

ISBN: (纸本)9798350308105

This paper presents a CNN-based network architecture aimed to classify and detect joint types within Articulated objects, specifically the Push-P joints, P-joints, R-joints and objects lacking joints. The movement modeling of the detected objects is explored by processing pre and post interaction RGB images within the SAPIEN PartNet-Mobility dataset. In order to achieve this, an architecture is proposed to leverage consecutive CNN encoders based on the VGG architecture, in order to classify joints based on pre and post-interaction images. Additionally, in order to detect the effect-point and movement vector, a convolutional encoder is applied for each joint type. Moreover, extensive evaluation is made to showcase the results, achieving 96% accuracy in joint classification and 94% accuracy in regression on the considered dataset. Elaborate details regarding network architectures, training procedures, and testing methodologies are available at source code repository. © 2023 IEEE.

关键词： Network architecture

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Experimental Study on Chess Board Setup Using Delta Parallel robot Based on Deep Learning 11

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

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

作者： Yarmohammadi, Parsa Khomami, Navid Asadi Masouleh, Mehdi Tale Zakerzadeh, Mohammad Reza University of Tehran Human and Robot Interaction Laboratory School of Mechanical Engineering Tehran Iran University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran University of Tehran School of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： Object detection

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robotic Sorting of Mechanical and electrical Parts: An Autonomous Vision-Based Approach in a Practical Case Study 10

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

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

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

ISBN: (纸本)9798331509972

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 90%. © 2024 IEEE.

关键词： Grippers

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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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KINEMATICS ANALYSIS AND SIMULATION OF LASER WELDING robot FOR SOLAR PANEL BUSBAR

International Journal of Mechatronics and Applied Mechanics

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International Journal of Mechatronics and Applied Mechanics 2025年第19期1卷 198-204页

作者： Peng, Wang Mingliang, Zheng School of Mechanical and Electrical Engineering Huainan Normal University Huainan232038 China Human-computer collaborative robot Joint Laboratory of Anhui Province Huainan232038 China

To overcome the insufficient quality stability of the overlap welding joints of solar panel busbar, using laser brazing technology, a new type of serial gantry robot with five degrees of freedom (three translations and two rotations) is designed. Firstly, the structure scheme of the gantry laser welding robot with five degrees of freedom for solar panel busbar is introduced. Secondly, the D-H linkage coordinate system of the welding robot is established, and the kinematics forward and inverse solutions and the velocity Jacobian matrix are solved. Finally, the kinematics forward and inverse solutions are verified by using robotics toolbox in Matlab;the reachable space of the robot is calculated by Monte-Carlo method;the kinematics simulation of the laser welding robot end effector is carried out by using Adams software. The results show that the displacement of the end effector of the gantry laser welding robot with five degrees of freedom for solar panel busbar is non-interference and the velocity curves change smoothly, which verifies the rationality of this structural design. By making a prototype and building a test platform for experimental verification, the robot ran smoothly, the weld seam layout was efficient and accurate, and the error at the end of the robot welding gun was within 0.28 mm. © 2025 The Author(s).

关键词： MATLAB

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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-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. © 2023 IEEE.

关键词： Classification (of information)

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

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

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

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

ISBN: (纸本)9798331511272

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. ©2024 IEEE.

关键词： Optical flows

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Autonomous Vision-based robotic Grasping of Household Objects: A Practical Case Study 11

Autonomous Vision-based Robotic Grasping of Household Object...

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

作者： Roudbari, Asal Dalvand, Mahdi Pasiar, Navid Asadi, Navid Yousefi, Mohammad Masouleh, Mehdi Tale Sharif University of Technology Electrical Engineering Department Tehran Iran University of Tehran Human Robot Interaction Lab Tehran Iran University of Tehran Human Robot Interaction Lab School of Electrical Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Autonomous robotic grasping has emerged as a valuable capability for automating everyday household tasks like table setting (bossing), cleaning, and meal preparation. Still, accurately detecting and manipulating diverse everyday objects in unstructured home environments remains a significant challenge. In this study, an integrated robotic system combining YOLOv8s-seg deep learning for object detection and segmentation with computer vision techniques to determine optimal grasping points is proposed. The accuracy of this model has also been compared with other models through their mAP0.5, mAP.05-.95, precision and recall. A custom dataset of over 1000 images containing common household objects was created, with manual annotation and augmentation to train the YOLOv8s-seg segmentation model. The approach processes input images to understand desired arrangements and identify target objects and their locations for grasping. The detected objects are localized using camera-to-robot calibration and aligned with estimated orientations from computer vision techniques. Extensive tests have proven that representative household objects in cluttered arrangements have a grasping success rate of over 90% using a 3DOF Delta Parallel robot with a 2-finger gripper guided entirely by visual perception. By overcoming real-world challenges like transparent and reflective materials, the perception-guided robots provide precise and efficient robotic grasping in unstructured home environments. This integrated approach combines robust perception and localization, providing promising evidence for reliable automation of repetitive household organization tasks1.1A video of real-time grasp performance is uploaded as supplementary material. © 2023 IEEE.

关键词： Object detection

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