检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Ghasemi, Seraj Hosseini, Hamed Koosheshi, MohammadHossein Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran

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%.MSC Codes computer Vision and Pattern Recognition (***), Systems and Control (***) © 2024, CC BY.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

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

Generating a General Culturing Microorganism Pattern Using a...

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

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

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Autonomous robotic Assembly and Sequence Planning Based on YOLOv8

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

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

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

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Kinematic Sensitivity Evaluation of Revolute and Prismatic 3-DOF Delta robots

Kinematic Sensitivity Evaluation of Revolute and Prismatic 3...

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Behzad Mehrafrooz Mohsen Mohammadi Mehdi Tale Masouleh Human and Robot Interaction Lab. University of Tehran Tehran Iran School of Electrical and Computer Engineering. University of Tehran Tehran Iran

ISBN: (纸本)9781538657041

The Delta parallel robot establishes itself as one of the most successful parallel robot designs thanks to its intrinsic properties. Generally, based on the type of actuator, the 3-DOF Delta robot falls into two groups, namely, revolute-input and prismatic-input. The main aim of this paper consists in evaluating and comparing the kinematic performance of the two foregoing types of Delta robots. So far, different kinematic sensitivity indices have been proposed. In this paper, by employing different kinematic sensitivity indices such as manipulab.lity, dimensionally kinematic sensitivity and weighted kinematic sensitivity indices. the kinetostatic performance of the aforementioned robots are compared over a certain workspace. The obtained results showed that the sensitivity of prismatic Delta robot is high in the center of the workspace and the sensitivity increases when the end-effector moves to edges from the center. However sensitivity of the revolute Delta robot is totally different. It is high on the edges and it decreases by moving to center. weighted kinematic sensitivity results show that geometrical features of the robots are very important and it changes the sensitivity values and trend greatly. Moreover, in order to enhance the kinematic performance of the robots, the configuration of the robots are redesigned using optimization of the kinematic sensitivity indices. The results of this paper revealed that kinematic sensitivity indices can be employed in the design step of the robot to reach an optimal kinematic sensitivity in the workspace.

关键词： Kinematics Sensitivity robot sensing systems Jacobian matrices Service robots Manipulators

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

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

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

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.

关键词： Location awareness Learning systems Visualization Artificial neural networks Data models Sensors robots Random forests Microphones Context modeling

来源：评论

学校读者我要写书评

暂无评论

Explorable Grasp Pose Detection for Two-Finger robot Handover

Explorable Grasp Pose Detection for Two-Finger Robot Handove...

引用

Iranian Conference on electrical engineering (ICEE)

作者： AliReza Beigy Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

The efficient generation of datasets for deep neural networks is crucial for enhancing their performance, especially in complex tasks like grasp pose detection in robotic grasp planning. Traditional methods often resort to random selection or rely on human-annotated datasets, making it challenging for networks to accurately learn graspable areas. For 3D grasp pose detection, an exhaustive search within an expansive space of points consumes substantial time and often overlooks an object's potential graspable regions. This paper introduces an algorithm capable of leveraging depth sensor information, particularly from devices like Kinect, to precisely determine the best grasp poses for object handovers. The proposed method, named Explorable Grasp Detection is based on the idea of exploitation and exploration, ensuring there is no redundant grasp detection within the same region. The results derived from DexNet's mesh objects dataset indicate remarkable computational efficiency, achieving grasp results in less than one second for various objects and maintaining a high success rate. Furthermore, error functions as geometry grasp quality metrics tailored for a two-finger gripper are incorporated to evaluate the grasp quality. One of the most notable contributions of this paper is the algorithm's ability to not only detect optimal grasping regions rapidly but also to serve as an instrumental tool for generating annotated datasets, thereby pushing forward the boundaries in robotic object manipulation techniques.

关键词： Point cloud compression Three-dimensional displays Instruments Grasping Handover robot sensing systems Search problems

来源：评论

学校读者我要写书评

暂无评论

2D Skeleton-Based Keypoint Generation Method for Grasping Objects with Roughly Uniform Height Variation

2D Skeleton-Based Keypoint Generation Method for Grasping Ob...

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Ali Sabzejou Mehdi Tale Masouleh Ahmad Kalhor School of Electrical and Computer Engineering Department Human and Robot Interaction Laboratory University of Tehran

robotic grasping poses a fundamental challenge in robotics, particularly when dealing with unknown objects. This skill has always been a focal point in robotics research due to its fundamental importance and inherent complexity. Recent advances utilize state-of-the-art learning techniques, including deep learning and deep reinforcement learning, presenting their unique set of challenges. Training these models requires extensive data and computational resources, with the generalization to unknown objects presenting a significant obstacle for robots. This paper presents a comprehensive approach to unknown object robotic grasping, with a specific focus on top-down grasping actions. The process encompasses image preprocessing, the application of the Straight Skeleton (StSkel) method, and the systematic generation of grasp keypoints when applied to a selection of objects from the Dex-Net dataset. During the evaluation, the analysis incorporated crucial metrics, including the number of detected grasp keypoints for each object, the count of successfully generated grasp pairs, the overall success rate, and the success rate when considering the top 5 ranked grasp pairs. One of the notable strengths of this approach lies in its adaptability to a wide array of objects, ranging from simple shapes to complex ones. The proposed approach in this paper paves the way for automatically lab.ling grasping datasets, providing a valuable asset for developing auto-generating Deep-RL models. The StSkel method effectively captures essential structural information, making it suitable for real-world applications involving diverse objects. The experimental outcomes affirm the robustness and adaptability of the approach, as it most often achieved a high success rate in generating viable pairs of grasping points for most tested objects. Even complex objects yield impressive results, demonstrating the method’s potential for real-world applications in grasping unknown objects.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Autonomous Unmanned Aerial Vehicle Indoor Navigation Based on Deep Learning Approaches

Autonomous Unmanned Aerial Vehicle Indoor Navigation Based o...

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

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

This article presents two approaches based on deep learning for achieving autonomous navigation and subsequently, conducts an in-depth comparative analysis to elab.rate 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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimal Path Planning of Mobile robots Using IsoCost-Based Dynamic Programming

Optimal Path Planning of Mobile Robots Using IsoCost-Based D...

引用

Iranian Conference on electrical engineering (ICEE)

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

This paper proposes an optimal local path planning algorithm for mobile robots by means of the so-called IsoCost-based dynamic programming. This algorithm explores and finds the optimal paths to the goal point from different start points in the environment of the robot while avoiding arbitrary numbers and forms of obstacles. The concept of IsoCost HyperSurface (ICHS) is first explained and it is shown that with a certain cost value, the ICHS corresponding to optimal path planning surrounds all other ICHSs corresponding to non-optimal path planning algorithms. Based on this geometric property, agents are initiated and planned to explore and reveal the optimal ICHSs using dynamic programming. The optimality of the proposed method is proved and the results of this method are compared with those of Modified Potential Field and Bug2 algorithms, indicating that the proposed method surpasses the other two algorithms in terms of the optimality of path length and execution time.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Designing Controller for the 2-DoF Cable-Driven Serial robot with Variable Configuration based on Adaptive Neuro-Fuzzy Systems

Designing Controller for the 2-DoF Cable-Driven Serial Robot...

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

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

ISBN: (纸本)9781665454537

This paper addresses the problem of actuation and design of a controller for a 2-DoF Cable-Driven Serial Chain (CDSC) with variable configuration based on adaptive neuro-fuzzy systems. The mentioned structure uses several light cables to actuate rigid links of a passive planar serial robot, performing a 2-DoF motion pattern, which can apply tensile forces for moving the links and thus produce the necessary torque to track the desired reference model by the robot’s end-effector. Additionally, cable attachment point coordinates of the CDSC are considered variable configurations. So, a novel concept is expressed in the calculation of the CDSC’s Jacobian matrix. In this mechanism, upon formulating the sliding mode error dynamics, an Adaptive Neural Network (ANN) is used to cope with complex mathematical calculations of the CDSC dynamics. Then, the coordinates of cables are considered as variables that depend on the main states of the plant. These co-states are adaptively determined by applying an appropriate fuzzy system. As a result, an Adaptive Neuro-Fuzzy Controller (ANFC) algorithm is introduced for the mentioned redundant mechanism. Finally, by defining a new Cost of Redundancy (CoR), an optimal configuration of the cables is found, so the maximum Degrees of Redundancy (DoR) is obtained. The simulation results indicate that using the ANFC algorithm leads to a reduction of 2.23% in the CoR for the 2-DoF CDSC.

关键词： Adaptive systems Costs Torque Tracking robot kinematics Heuristic algorithms Simulation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：