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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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AI-Driven Relocation Tracking in Dynamic Kitchen Environments

arXiv

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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

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

arXiv

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arXiv 2025年

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey 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 United States

—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. © 2025, CC BY-NC-ND.

关键词： Anthropomorphic robots

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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 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 90%. © 2024 IEEE.

关键词： Grippers

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Generating a General Culturing Microorganism Pattern Using a Delta Parallel robot and Cam-in-Hand Calibration Method 11

Generating a General Culturing Microorganism Pattern Using a...

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

作者： Pasiar, Navid Mohammadi, Arvin Ahangar, Sajjad Khomami, Navid Asadi Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Lab. Department of Mechanical Eng. Tehran Iran University of Tehran Human and Robot Interaction Lab. Tehran Iran University of Tehran Human and Robot Interaction Lab. School of Electrical and Computer Eng. Tehran Iran

ISBN: (纸本)9798350308105

robots can play a vital role in lab.ratory tasks, especially in culturing microorganisms. Currently, many of these operations are performed manually, which leads to biased and irreproducible results. This paper explores a calibration method for minimizing errors of a performing two certain lab.ratory tasks of swabbing and pipetting. This leads to enhanced accuracy and productivity. This research intends to culture microorganisms in a petri-dish in a specific pattern, measuring the coordinates of each object in the output image so the object location in the real world can be calculated. This research investigates the Cam-in-Hand method for a Delta Parallel robot, meaning that the camera is mounted on the End-Effector. In this approach, a method of translating image coordinates into real-world coordinates is introduced. It is essential to provide an additional coordinate (in this case $z -$value) before the conversion, given that the camera outputs a 2D image. A vector-based image, in SVG format, as input to the algorithm generates a set of coordinates which determines the main points for swabbing or pipetting operation. Using this data, an array of coordinates is linearly interpolated in 3D space for the swabbing operation. Conversely, the trajectory generated for pipetting uses the 4-5-6-7 interpolating polynomial. The robot then adheres to following the interpolated array of coordinates as a function of time, using a PID controller. The pipetting device is built with a 3D printer using PLA materials. The calibration is done in different heights. The Camin-Hand method leads to ±1 cm precision as a result. Minimum, maximum, and mean errors are 0.092,1.61 and 0.76 respectively for the central point. © 2023 IEEE.

关键词： Calibration

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Corrigendum to "DSTF: A diversified spatio-temporal feature extraction model for traffic flow prediction" [Neurocomputing 621 (2025) 129280] (Neurocomputing (2025) 621, (S0925231224020514), (10.1016/***.2024.129280))

引用

Neurocomputing 2025年 638卷

作者： Wang, Xing Wang, Xiaojun Huang, Faliang Zou, Fumin Liao, Lyuchao Zeng, Ruihao Fujian Normal University College of Computer and Cyber Security China Fujian Normal University Digital Fujian Institute of Big Data Security Technology China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision China Fujian University of Technology Fujian Key Laboratory of Automotive Electronic and Electrical Drive Technology China The University of Sydney School of Civil Engineering Sydney Australia

The authors regret to inform that: (1) During the resubmission stage of the revised manuscript, in compliance with reviewers' recommendations to show standard deviations in the experimental results (In Section 5. Experiment implementation), a batch replacement operation (intended to replace "+" with "±" for data formatting) inadvertently replaced the "+" symbols in the mathematical formulas and the explanatory text with "±" symbols (In Section 3. Problem statement and Section 4. Methodology). (2) In proof stage, we sincerely apologize for failing to identify this issue during the final proofreading stage. Correction statement and details: As this error may affect readers' understanding, we request a corrigendum to rectify this error. In Section 3. Problem statement and Section 4. Methodology, all instances of "±" symbols within mathematical formulas and their explanatory text should be reverted to the original "+" symbols. The authors would like to apologize for any inconvenience caused. © 2025 The Authors

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Autonomous robotic Assembly and Sequence Planning Based on YOLOv8 11

Autonomous Robotic Assembly and Sequence Planning Based on Y...

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

作者： Babi, Zeynab Ezzati Khomami, Navid Asadi Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran School of Mechanical Engineering Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798350308105

This study contributes to autonomous assembly by introducing a robotic building system designed to observe its workspace, plan building sequences, determine appropriate grasps and manipulator motions, and operate hardware in real-time. This study involves the creation of a wooden brick dataset and employs a YOLOv8 deep learning model for object segmentation in two implemented real-time algorithms, namely, Desired Construction Object Detection (DCOD) and robot Current Frame Object Detection (RCFOD). The DCOD algorithm is designed for the desired structure, while the RCFOD algorithm is utilized for detecting objects within the operational environment of the robot. By comparing the outputs of these two algorithms, sequences of objects for assembly are formulated, and the appropriate grasp positions are determined. Subsequently, the robot's motion planning for both grasping and construction tasks is executed. Due to the limitations of using a single RGB gripper-mounted camera, various issues, such as failing to recognize items with the same cross-section but varying heights, have been addressed in this study. The results of this study indicate the effective learning of the YOLOv8 model, as shown by a loss value below 0.2 and an accuracy over 90% for 17 out of the total 18 classes. Practical implementation is done by a common industrial Delta parallel robot, a 2-finger gripper, and a simple RGB camera. Overview of practical implementation system is shown in Fig. 1. The proposed robotic assembly method demonstrates its effectiveness in autonomously constructing structures utilizing a reduced and cost-efficient configuration. © 2023 IEEE.

关键词： Object detection

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Sound Source Localization in a 3D Multi-Surface Environment Using Learning-Based Methods 12

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

引用

12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

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

ISBN: (纸本)9798331529734

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. © 2024 IEEE.

关键词： Deep neural networks

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A dual-channel beamformer based on time-delay compensation estimator and shifted PCA for speech enhancement 23

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

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23rd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2015

作者： Jie, Zhang Liu, Hong Open Lab on Human-Robot Interaction School of Electrical and Computer Engineering Shenzhen Graduate School Peking University Shenzhen518055 China Open Lab on Human-Robot Interaction School of Electrical and Computer Engineering Shenzhen Graduate School Peking University Beijing100871 China

ISBN: (纸本)9789532900569

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. © 2015 University of Split, FESB.

关键词： Principal component analysis

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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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