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检索条件"机构=Human and Robot Interaction Lab. School of Electrical and Computer Engineering"

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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...

引用

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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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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Model-Free Dynamic Control of Five-bar Mechanism via Subchain Assembly and Reinforcement Learning 12

Model-Free Dynamic Control of Five-bar Mechanism via Subchai...

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

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

ISBN: (纸本)9798331529734

Parallel robots are versatile but challenging to control due to their complex dynamics and the need for precise modeling. This research presents a novel approach to modular control of parallel robots, utilizing reinforcement learning to dynamically assemble and control complex arbitrary systems. By treating each sub chain of a parallel robot as an individual agent, the proposed method offers a flexible and adaptable framework for various control tasks. The study compares the learning performance of different RL algorithms and proposed decision modeling on the tracking control of a five-bar robot with a 55 cm link length, aiming to achieve an error of less than 5 cm in the PyBullet simulator. In single-agent reinforcement learning, three algorithms (DDPG, PPO, and SAC) were employed, with PPO and SAC effectively solving the problem. In multi-agent reinforcement learning, two algorithms (FACMAC and MAPPO) were utilized. While MAPPO successfully learned the problem, FACMAC exhibited difficulties in grasping the problem. The results obtained from comparing the proposed RL algorithms indicated that single-agent RL learns the control problem better and faster than multi-agent reinforcement learning. In short, the paper focuses on formulating an RL solution which can effectively control parallel robots and can potentially be used for pre-training subchains. © 2024 IEEE.

关键词： Reinforcement learning

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From Bricks to Bots: Automated Collision-Aware Sequence Planning for LEGO Reconstruction with a Two-Finger Gripper 10

From Bricks to Bots: Automated Collision-Aware Sequence Plan...

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

作者： Barghi, Arman Pasiar, Navid Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331509972

An automated, vision-guided assembly method is proposed for reconstructing LEGO arrangements using a 3 DOF Delta Parallel robot with a two-finger gripper. Assembly sequence planning, an NP-complete problem, focuses on determining the optimal sequence of bricks for constructing a target structure. The complexity increases when robotic con-straints-such as avoiding collisions between the gripper and the partially constructed structure-Are integrated into the planning. To address this, the Arrangement Planning Process is introduced, combining target arrangement identification with collision-Aware sequence planning. This process includes precise detection techniques that merge instance segmentation with stud detection. Notable innovations include an assembly-by-disassembly algorithm for efficient planning and experimental techniques such as the Safe Approach and the Wiggling Technique for accurate brick placement. By implementing a homing method, positioning accuracy improved approximately twofold compared to previous work. The results demonstrate the feasibility and precision of the proposed method in automated LEGO reconstruction. © 2024 IEEE.

关键词： Grippers

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Autonomous Rearrangement Planning Using Object Similarity Learning 12

Autonomous Rearrangement Planning Using Object Similarity Le...

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

作者： Jabari, Parmida Babi, Zeynab Ezzati Ghasemi, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad School of Electrical and Computer Engineering University of Tehran Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798331529734

This study investigates the integration of artificial intelligence in robotics assembly systems, focusing on enhancing flexibility and adaptability in dynamic environments where the robot might not be given the precise objects of the intended construction. To this end, a developed approach is presented which is based on object detection, similarity assessment, and sequence planning for autonomous construction tasks. The system utilizes a YOLOv8 model for object detection and a Siamese network architecture, which facilitates object replacement. It processes two input images, one representing the target structure and another showing the availab.e objects, extracting attributes such as bounding boxes, centers, and orientations. An exact object-matching step is performed before a two-stage similarity assessment. The two-stage similarity assessment prioritizes single-object matches and employs a multi-object similarity network only when necessary. This multi-stage approach is designed to maintain high similarity to the intended design while minimizing possible object shortcomings. During the construction phase, motion planning is performed using a 3-degrees-of-freedom Delta parallel robot, and a 2-finger gripper is utilized for robotics grasping. The results of this study, conducted on wooden bricks of varying shapes and sizes, demonstrate the effectiveness of the YOLOv8 model for object detection, achieving over 80% accuracy across 18 classes with a classification loss of approximately 0.25. Additionally, the Siamese network performed robustly, with a loss of less than 0.01 for both single and multiple object comparisons. © 2024 IEEE.

关键词： robotic assembly

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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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Autonomous robotic Assembly and Sequence Planning Based on YOLOv8 11

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

引用

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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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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Dynamic Modeling of Double Segment Redundant Gough-Stewart Hybrid Manipulator based on the Principle of Virtual Work 12

Dynamic Modeling of Double Segment Redundant Gough-Stewart H...

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

作者： Ardakani, Alireza Kamali Akbari, Hossein Namazian, Parsa Masouleh, Mehdi Tale Bahrami, Arash 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 School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (纸本)9798331529734

This paper presents the dynamic modeling and analysis of a hyper-redundant manipulator with two Gough-Stewart platforms. Initially, the kinematic model of the manipulator is reviewed and used, since it acts as a base component for the subsequent dynamic modeling. Then, the virtual work approach is introduced to establish a foundation for the dynamic modeling of a single Gough-Stewart platform. This modeling for a single segment is used to form the equation of motion for both segments. However, the external forces applied to the lower Gough-Stewart robot are different from the upper one, since the force and masses in the upper platform act on the lower one. By considering these effects on the lower platform, the dynamic equations for the whole have been formed and presented. The accuracy of the proposed model is validated through a detailed comparison with simulations performed using a Simscape environment. Notable challenges in aligning the results between the model and simulations are discussed, highlighting the complexities involved in ensuring consistency across different platforms. The comparisons show an average error of 1.537 percent per actuator. © 2024 IEEE.

关键词： Digital elevation model

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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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