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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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Controllability analysis of mechanical multibody dynamics control systems based on lie symmetry

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Alexandria engineering Journal 2025年 127卷 374-379页

作者： 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 quantitatively characterize the dynamic controlled performance of mechanical multibody systems and design collaborative control strategies, this paper investigates the controllability of mechanical multibody dynamic control systems. Firstly, under the affine connection framework of Lagrange mechanical control systems, we establish the Euler-Poincaré equations of unconstrained mechanical multibody dynamics control systems by differential geometry method;Further, we transform the Euler-Poincaré equations into the classical state space form of nonlinear control systems by augmented vector method;Secondly, by introducing Lie group representation theory, the definition and the solution of generalized Lie symmetry for mechanical multibody dynamics control systems are given, and the necessary and sufficient conditions for state controllability (local controllability) are given by using Lie symmetry;Finally, an application example of dynamic control of a single degree of freedom manipulator with basic vibration is provided, it explains the effectiveness and correctness of the Lie symmetry method in this paper. The research has shown that using Lie symmetry to analyze the controllability problem of mechanical multibody dynamics control systems can be transformed into its low-dimensional quotient space analysis, and if the quotient space is controllable at a point, then the system can be controllable for all points on the orbit where that point at. © 2025

关键词： Controllability

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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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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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In Vivo Bioelectronic Nose Based on a Bioengineered Rat for the Explosive Odors with Benzene Ring Detection and Identification 24

In Vivo Bioelectronic Nose Based on a Bioengineered Rat for ...

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11th International Conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Ma, Jing Wang, Yimeng Yang, Jun Sun, Biao Yang, Qing Zheng, Chenguang Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China College of Precision Instruments & Optoelectronics Engineering Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Accidents caused by explosives occur frequently, which seriously threaten people's lives and health, so it is urgent to develop new technologies for sensitively and rapidly detecting trace explosives. In this study, a bioengineered rat with the olfactory receptor (OR3) overexpression in the olfactory epithelium (OE) was successfully developed, and the unique features of three representative explosives (NT, DNT, and TNT) were conducted by recording electrophysiological-responsive signals from the olfactory bulb (OB) with implanted multichannel microelectrodes during awake-state-independent sniffing behavior induced by explosives and non-explosives. These three explosives were regarded as goal odors, while citral and air were no-goal odors. The analysis of local field potential (LFP) power changes revealed that, goal, and no-goals could be distinguished in both the vector group and the OR3 group at concentrations as low as 10-12 mol/L, and the classification performance of the OR3 group was superior to that of the vector group overall. Our findings suggest that OR3 bioengineered rats can reliably and accurately identify trace explosives, which provides a novel approach for detecting and identifying explosives with promising applications in odor discrimination in complex environments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic nose

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Effect of the configuration and concentration of B-doping on the thermal properties and structural stability of graphene: A theoretical study

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Diamond and Related Materials 2025年 156卷

作者： Li, Dongbo Da, Liangguo Huang, Kaifeng Li, Dong Yu, Liang Wang, Haini Yu, Yaqi Wu, Long Tang, Yunqing School of Mechanical and Electrical Engineering Huainan Normal University Huainan232038 China Human-computer collaborative robot Joint Laboratory of Anhui Province Huainan232038 China School of Mechanical Engineering Shandong University Jinan250061 China State Key Laboratory of Advanced Equipment and Technology for Metal Forming Shandong University Jinan250061 China Key Laboratory of High Efficiency and Clean Mechanical Manufacture Ministry of Education Shandong University Jinan250061 China

The thermal properties and structural stability of B-doped graphene were investigated by employing density functional theory (DFT), ab initio molecular dynamics (AIMD), and molecular dynamics (MD) simulations, with a focus on the effects of doping configuration and concentration. DFT calculations indicate that B-doping reduces the cohesive energy of graphene, consequently diminishing its structural stability. Among the three B double-doping configurations, only the para configuration exhibits a phonon dispersion without imaginary frequencies, whereas both meta and ortho configurations show imaginary frequencies, indicating structural instability. The AIMD results further demonstrate that B-doped graphene preferentially undergoes cleavage at B[sbnd]B bonds, leading to decreased structural stability. MD results show that B-doping significantly reduces the thermal conductivity of graphene. At a doping concentration is 0.6 %, the thermal conductivity of meta and para configurations decreases to 48.08 % and 54.6 % of that of the pristine graphene, respectively. The results of phonon properties show that B-doping reduces the phonon participation rate and induces strong phonon localization at the B-doped site. These results and conclusions will provide valuable theoretical insights for the thermal design of graphene-based semiconductor devices. © 2025 Elsevier B.V.

关键词： Temperature distribution

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

arXiv

引用

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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HUGGA: human-like Grasp Generation with Gripper's Approach State Using Deep Learning 11

HUGGA: Human-like Grasp Generation with Gripper's Approach S...

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

作者： Balazadeh, Elnaz Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Laboratory Tehran Iran University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

In this study, an attempt has been made to develop a new method for producing a successful grasp based on human behavior, by considering the gripper's approach state in the final grasp of an object. The dataset collected for the purpose of this paper consists of 14 different daily usage objects with different orientations (In total 2526 images) which a human approaches and grasps them. Data collection has been done based on a protocol shown in TableI to cover all achievable states. From Fig1, it can be seen that the dataset includes one image from a single object without the user's hand. The second image contains the human hand approach and its configuration regarding the object which is subject to be grasped and the third image represents the object which is grasped by the human hand and thus evidently contains both object and human hand grasp configuration. It is important to acknowledge that providing a static hand image does not suffice to facilitate the acquisition of human-like grasping capabilities by robot grippers, so, a mapping from hand fingers to a two-finger gripper should be implemented to fill this gap. In the collected dataset, all grasps are performed by the index finger and thumb to make it possible to map the coordinates of each finger on a gripper finger. Using MediaPipe, which operates a single shot detection network, with only an RGB image containing a human hand the index finger and thumb landmarks were extracted. Finally, a detailed human approach and grasp information were available, and with the collected data, a two-input and one-output deep neural network was trained. The designed model produced a grasp with a human pattern by receiving only the RGB image of the object and the approach vector to the object. Ultimately, through testing and evaluation based on existing criteria, the trained network successfully produced a grasp with 61% accuracy in grasp rectangle overlap, 97% in grasp angle and overall 60.5% met two established grasp crite

关键词： Grippers

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Autonomous Unmanned Aerial Vehicle Indoor Navigation Based on Deep Learning Approaches 11

Autonomous Unmanned Aerial Vehicle Indoor Navigation Based o...

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

作者： Ghasemi, Hamed Balazadeh, Elnaz Malekpour, Shayan Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran University of Tehran Human and Robot Interaction Laboratory Tehran Iran University of Tehran School of Electrical and Computer Engineering Department of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

This article presents two approaches based on deep learning for achieving autonomous navigation and subsequently, conducts an in-depth comparative analysis to elaborate their respective performance. Both methods leverage neural networks to facilitate navigation. The first approach employs a deep convolutional neural network to identify suitable areas for forward movement, while the second method utilizes the drone's front camera to capture images, which are then processed by a generative network to generate corresponding depth images. Importantly, both approaches rely solely on an RGB camera, resulting in cost savings for the robot and reduced energy consumption. The training data used for classifying collision-free and appropriate paths were gathered from simulated environments closely resembling real indoor settings. The proposed model calculates the probability associated with each segment of the input image, enabling the selection of the most viable area for drone flight. Incorporating generative networks as a depth estimation module enhances flight accuracy, making navigation in uncharted environments more straightforward. The accuracy of depth estimation was evaluated using standard criteria, achieving an accuracy rate of 84%. This result underscores the effectiveness of the proposed method. © 2023 IEEE.

关键词： Drones

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