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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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SPAR-Leg: Serial-Parallel Anthropomorphic robotic Leg 11

SPAR-Leg: Serial-Parallel Anthropomorphic Robotic Leg

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

作者： Namazian, Parsa 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 Eletrical Computer Engineenng Tehran Iran University of Tehran School of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798350308105

This paper aims at a preliminary research on designing a hybrid mechanism inspired by human leg's musculoskeletal architecture and locomotion capabilities. Complexity of the workspace solution in serial-parallel mechanisms limits the design approach. In order to have an estimation from the human leg geometry and reachable workspace, a dataset is collected from the human leg of 10 females and 10 males. In addition to the measurements done on the dimensions of this population, the reachable workspace of their leg is observed using MediaPipe pose estimation model. This is done by capturing full movement of the human leg for each person in front of two cameras. On the other hand, the musculoskeletal system of the human leg reveals that two serially attached parallel mechanisms are well-suited for the design. The upper mechanism, which is the thigh mechanism, includes 6 UPS links as the actuators, and a US constraint link to avoid elongation in the leg. Similarly, 3 UPS and a S constraint link form the lower mechanism. Two mathematical models based on geometric approaches are derived for the mechanisms. In order to reach a workspace fitted to that of human leg, an optimization process is performed on the mechanism. Due to the complexity of the whole model's workspace, the optimization process is done on the upper mechanism to reach its maximum workspace. Based on the ratios between thigh and calf dimensions, the lower mechanism's design parameters are obtained, and its workspace is verified to be optimal. Afterwards, a simple geometric calculation of the whole mechanism is done and compared with the results of workspace observation using MediaPipe. A brief simulation is done on the upper mechanism in two different movements in order to select suitable actuators based on the required forces. Finally, forward kinematics problem of the mechanism is solved and validated by performing simulations. © 2023 IEEE.

关键词： Actuators

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Synergy of Deep Learning and Artificial Potential Field Methods for robot Path Planning in the Presence of Static and Dynamic Obstacles 30

Synergy of Deep Learning and Artificial Potential Field Meth...

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30th International Conference on electrical engineering, ICEE 2022

作者： Basiri, Mohammad Amin Chehelgami, Shirin Ashtari, Erfan Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665480871

In a fast-changing world we are in today, unmanned vehicles are displacing old, obsolete and frustrating tasks. Since unmanned vehicles are intended to work in an environment without any conduction, finding a collision-free path of movement is of paramount importance and a definite asset in practice. The objective of a global path planner consists in generating an optimized path without considering unknown obstacles. In contrast, a local path planner properly avoids the unknown obstacles while getting close to the goal. This results in an inefficient movement, particularly in complex circumstances. In this paper, a complete global and local path planning method is proposed for avoiding both static and dynamic obstacles. The proposed method benefits the advantages of both approaches while covering each other's weaknesses. First, a method for generating numerous obstacle-free paths from random pairs of start and goal points is introduced. Next, a novel deep-learning approach is proposed in order to train the robot in an environment free of moving obstacles. After extracting the desired via points, an efficient Artificial Potential Field (APF) approach for attaining the local path planning is introduced with the aim of avoiding dynamic obstacles while the robot travels through the aforementioned via points. The proposed method can be well extended to different platforms such as mobile robots, arm robots, quadrotors, etc;in this paper, both local and global path planning methods are implemented on a simulated quadrotor to examine the robot's performance for both approaches. Furthermore, it has been revealed that implementing both approaches should be implemented seamlessly in order to attain a complete efficient path planning with the presence of both static and dynamic obstacles. Finally, both local and global path planning methods are implemented on a detailed simulated quadrotor to evaluate the robot's performance for the aforementioned approaches. © 2022 IEEE.

关键词： Motion planning

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User Identification Based on Hand Geometrical Biometrics Using Media-Pipe 30

User Identification Based on Hand Geometrical Biometrics Usi...

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30th International Conference on electrical engineering, ICEE 2022

作者： Ghanbari, Sara Ashtyani, Zahra Parvin Masouleh, Mehdi Tale University of Tehran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9781665480871

The main objective of a hand-geometry-based user identification model consists in designing a method that could be implemented with a satisfactory trade-off between cost, accuracy, and ease of utilizing. In this paper, an efficient, peg-free hand geometry-based approach for user identification is presented, which can be easily implemented with low-cost pieces of equipment and results up to a 98.7% accuracy in user identification. Three different feature-based methods, namely, circular representation, FPL, and FPLKW, are designed and examined. In order to extract required landmarks to the end of generating intended features, the so-called Media-Pipe is used. The first method is the circular representation, in which the radius ratio of the circles formed by the three middle finger straps is taken as model features. The second method extracts fingers length and phalanges length of each finger and uses their ratio as features, which is called Finger Phalanges Length (FPL) method. The third method extracts Fingers Length and Phalanges Length of each finger and Knuckles Width, abbreviated as (FPLKW), and uses their ratios for extracting features. Two datasets are collected, one for examining the efficiency of the proposed methods and finding the promising one, and the second one for validating the performance of the selected method. The first dataset contains images of the back-side of both hands in spread fingers mode and picked altogether fingers mode, taken from 21 individuals on a black background. The second dataset contains images of 100 individual hands in the front-side of both hands and the back-side of both hands in spread fingers mode. Different classifications are used to obtain maximum accuracy, including SVM SVC, SVM NuSVC, the decision tree classifier, the extra tree classifier, the random forest classifier, and one hidden layer MLP. Obtained results reveal that the extra tree classifier leads to better performance with respect to the methods mentioned abov

关键词： Biometrics

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition 8

SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera...

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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Siahkali, Farbod Alavi, Seyed Amirmahdi Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Islamic Azad University Human and Robot Interaction Laboratory School of Computer Science Central Tehran Branch Tehran Iran

ISBN: (纸本)9781665476232

In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks.1https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition © 2022 IEEE.

关键词： Model automobiles

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State Definition in Deep Reinforcement Learning: A Smoothness Index Approach 11

State Definition in Deep Reinforcement Learning: A Smoothnes...

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

作者： Dehdasthian, Soroosh Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Intraction Laboratory School of Electrical and Computer Engineering Department Iran

ISBN: (纸本)9798350308105

In the pursuit of developing autonomous agents capable of learning optimal behaviors, reinforcement learning has emerged as a foundational frame-work. This paper introduces a novel approach for state definition in deep reinforcement learning systems, with a focus on solving pixel-based control scenarios. The proposed frame-work harnesses the capabilities of Convolutional Variational Auto-Encoders to extract compact and meaningful latent representations from high-dimensional pixel observations. These representations serve as the foundation for constructing an efficient state space for reinforcement learning agents. To further enhance the state construction process, the concept of the Smoothness Index is introduced. Smoothness index acts as a criterion for feature selection within the latent space, facilitating the identification of the most relevant and informative states. Demonstrating the effectiveness of iterative smoothness index-based forward selection, this process notably compresses the observation space while retaining essential information. This compression enables the creation of more efficient reinforcement learning agents. The procedure involves collecting and preprocessing observation data given by the OpenAI Gym car racing environment, encoding observations using a convolutional variational auto-encoder, and iteratively selecting features based on smoothness index. Subsequently, the Proximal Policy Optimization algorithm is employed as a policy-based controller to evaluate the effectiveness of the proposed frame-work. Results indicate that this approach yields a remarkably compact state space that either maintains or enhances reinforcement learning agent performance. This innovative frame-work paves the way for the development of more efficient and adaptive reinforcement learning agents in real-world image-based applications. © 2023 IEEE.

关键词： Reinforcement learning

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Dynamic Modeling, Parameter Identification and Nonlinear Model Predictive Control of a Cable-Suspended Parallel robot with Four Cables 8

Dynamic Modeling, Parameter Identification and Nonlinear Mod...

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8th International Conference on Control, Instrumentation and Automation, ICCIA 2022

作者： Ghanatian, Mohammad Hairi Yazdi, Mohammad Reza Masouleh, Mehdi Tale Human Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran Human Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665495691

In this research, a nonlinear model predictive controller is designed for the trajectory tracking of spatial cable-suspended parallel robots with four cables. The dynamic model of this robot is derived firstly using the Newton-Euler approach and is subsequently verified using the Simscape environment. Moreover, an identification method based on the Genetic Algorithm is presented to identify the unknown inertial and frictional parameters in the model. It is observed that the Interior-Point optimization method is able to solve the optimization problem resulting from the proposed model predictive controller in real-time. Also, the effectiveness of the designed controller, combined with the presented identification approach, is investigated by assessing the controller performance when the estimated values are used in the model, compared to the cases where the precise values are utilized. Three groups of various inertial and frictional parameter values are considered in these assessments, and it is observed that when the estimated values are utilized in the model, the control of the robot is performed with an average RMSE of 1.1073mm, which is only 0.75716mm more than the average RMSE when the precise values are utilized. The results obtained from the simulations reveal the promising performance of the proposed model predictive controller when combined with the suggested identification method. © 2022 IEEE.

关键词： Genetic algorithms

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Experimental Study on Motion Controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel robot 11

Experimental Study on Motion Controller based on NN-ARX and ...

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

作者： Jalali, Hasan Damavandi, Hossein Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Sadjad University Faculty of Computer Engineering and Information Technology Mashhad Iran University of Tehran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers. © 2023 IEEE.

关键词： Controllers

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Experimental Study on Autonomous Food Packaging with Delta Parallel robot and Two Fingered Gripper

Experimental Study on Autonomous Food Packaging with Delta P...

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Iranian Conference on electrical engineering (ICEE)

作者： Mona Mohades Mojtahedi Arvin Mohammadi Mehdi Tale Masouleh 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 University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

In response to the increasing supply and demand in the food industry, automated food packaging has emerged as a substitute for the manual labor that was previously required. The surge in automation is evident, with robotics playing a pivotal role in tasks ranging from sorting and packaging to labeling, and notably, pick-and-place operations which is the specific focus of this study. Leveraging robots for these tasks not only improves precision and speed but also takes packaging hygiene into account, resulting in a substantial cost reduction and heightened efficiency. Delta Parallel robots are among the robotic systems well-suited for high-speed pick-and-place tasks. In this experimental study, the focus is on packaging several scattered chocolates, where both the container box and the chocolates will be randomly positioned and oriented. This is achieved by employing a Delta Parallel robot paired with a two-fingered gripper, designed to incur minimal damage to the product when compared to other types of end-effectors. The positioning of the box and products will be determined through a classical image processing approach, specifically Edge Detection and Hough Transform, implemented using the OpenCV Python library. Following identification, the objects are localized through camera-robot calibration, and the resulting coordinates are transmitted to the robot as target points. Extensive testing has demonstrated that this study, employing visual perception for pick-and-place tasks, achieved a success rate of 82%. This study showcases the potential of automating packaging tasks in the food industry through the integration of robotic systems and artificial intelligence. The approach proves effective across diverse tasks, encompassing the packaging of variously shaped food items and drugs, applicable across different industries.

关键词： Parallel robots Accuracy Service robots Shape Image color analysis robot kinematics Food industry

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Mapping human Grasping to 3-Finger Grippers: A Deep Learning Perspective

Mapping Human Grasping to 3-Finger Grippers: A Deep Learning...

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Iranian Conference on electrical engineering (ICEE)

作者： Fatemeh Naeinian Elnaz Balazadeh 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

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

Grasping is a fundamental aspect of human interaction with the environment, and replicating this ability in robotic systems is challenging due to the complexity of hand-object interactions. This paper explores the mapping of human grasping behavior to a 3-finger robotic gripper using a deep learning approach. The study focuses on the development of a model that predicts grasping points based on RGB images of objects and finger coordinates during the approaching state. The research contributes a novel dataset of human-like grasping with 3 fingers, capturing various object orientations and scenarios. Finger coordinates are extracted in both the approaching and grasping states by using Mediapipe. The proposed model employs a pretrained Faster R-CNN for object detection, coupled with fully connected networks for regression of grasping points. Evaluation metrics include Intersection over Union (IoU), grasp angle, and a proposed metric, Grasp Configurational Accuracy (GCA). The trained network achieves compelling results, demonstrating 88.2% GCA, 96.5% in grasp angle, and a 45.7% IoU in grasp rectangle representation. Results indicate the efficacy of the approach, offering a promising avenue for advancing robotic grasping capabilities.

关键词： Measurement Deep learning Three-dimensional displays robot kinematics Pipelines Grasping Predictive models

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