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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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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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Experimental Study on the Pick-and-Place Operation of a 3-DOF Delta Parallel robot 31

Experimental Study on the Pick-and-Place Operation of a 3-DO...

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31st International Conference on Electrical engineering, ICEE 2023

作者： Nouri, Amirreza Davoudi, Soheil Vali, Sina Mohammadi, Arvin Ravari, Ali Akhavan Saraf, Ehsan Tale Masouleh, Mehdi Amirkabir Univerisity of Technology Department of Mechanical Engineering Tehran Iran Tavan Resan Co. Tehran Iran University of Tehran Human and Robot Interaction Laboratory Tehran Iran University of Tehran Department of Mechanical Engineering Tehran Iran School of Ece University of Tehran Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798350312560

This paper presents kinematic analysis, verification, and workspace determination for the trajectory planning purpose of a 3-DOF Delta parallel robot. The under study Delta robot is the result of an academic collaboration between the human and robot interaction laboratory and the Tavan Resan Company. The dimension of the robot is obtained by using an algorithm for obtaining the optimal dimension of a Delta robot for a prescribed workspace defined from the outset. Analytical solutions for the forward and inverse kinematic problems of the under study robot are provided and verified in Simscape simulation. The workspace is determined using a discretization algorithm, in which joint limits and mechanical interference are taken into account to the end of obtaining a practical workspace. The robot's singular loci are determined, and visual inspection reveals that the robot entails a singularity-free workspace by considering joint limits, which can be regarded as a definite asset in practice. Finally, a 4-5-6-7 trajectory planning technique is implemented in order to make the manipulator's end-effector movement as smooth as possible for pick-and-place operations. Furthermore, experimental tests were executed to observe the robot's behavior and validate the kinematic equations experimentally. © 2023 IEEE.

关键词： Inverse problems

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

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

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

作者： Alireza Kamali Ardakani Hossein Akbari Parsa Namazian Mehdi Tale Masouleh Arash Bahrami Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

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.

关键词： Accuracy Mechatronics robot kinematics Motion segmentation Dynamics Force Kinematics Mathematical models Complexity theory Manipulator dynamics

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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger robot Hand 31

A 2D Geometry Based Grasping Pose Generation Algorithm for a...

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31st International Conference on Electrical engineering, ICEE 2023

作者： Akbari, Arash Akbari, Arman Masouleh, Mehdi Tale University of Tehran Department of Computer Science Tehran Iran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798350312560

robot grasping is of paramount importance in industrial and service robotics. In recent years, various data-driven algorithms have been proposed to solve the problem of grasp detection and a part of them are based on reinforcement learning (RL) approaches. In a variety of proposed algorithms, random key points are being employed which will make the learning process inefficient and time-consuming. In this paper, a geometry-based algorithm is presented which can find grasp poses based on the geometry of the unknown object and propose the ones which may lead to successful grasping. For the grasp contacts computation part, the presented algorithm produces a finite number of key points based on the 2D shape of the object from a specific point of view. Afterward, it will narrow down the candidate points and output a finite number of successful grasp poses based on three grasp quality metrics for various unknown objects. Three approaches are proposed in order to achieve center points which can describe different parts of a 2D shape. Then, the obtained points are used as the center of circles which are tangent to the 2D shape contour. Also, a new grasp quality metric is proposed. The time of the grasp and the amount of object disorientation after grasping are considered as a metric to evaluate the successfulness of the grasp. Simulation results demonstrate that the proposed algorithm for unknown object grasping can find a finite number of successful grasp poses for different seen or unseen objects without using any random point © 2023 IEEE.

关键词： Geometry

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Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel robot 13

Practical Implementation of Real-Time Waste Detection and Re...

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13th International Conference on Computer and Knowledge engineering, ICCKE 2023

作者： Jalali, Hasan Garjani, Shaya Kalhor, Ahmad Masouleh, Mehdi Tale Yousefi, Parisa University of Tehran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory Tehran Iran Imam Reza International University School of Computer Engineering Mashhad Iran

ISBN: (纸本)9798350330151

Intelligent robots play an essential role in waste management and recycling due to their high speed and a wide variety of applications. In this paper, two methods for waste detection and accurate pick-and-place based on computer vision and neural networks are presented. The suggested methods have been put into practical application on a 3-DOF Delta parallel robot to show the accuracy and fastness of the foregoing method for real intelligence systems. The first method, Multi-Stage Detection, consists of two stages to detect the waste objects, namely, object localization and segmentation, and classification. The second method, known as One-Stage object detectors, such as YOLOv5, has the capability to simultaneously localize and classify the waste objects. The dataset utilized in this paper relies on the TrashNet dataset as its foundation. In order to improve the classification capabilities in the multi-stage method, a larger dataset was created by utilizing data augmentation. Also, for the one-stage method, a new multi-label dataset is constructed based on the TrashNet dataset. Additionally, the results of the experimental implementation were compared based on time and evaluation metrics for detection and classification. The ResNet50 model achieved the highest accuracy in the multi-stage method, with 99.31% accuracy. In the one-stage detection method, the YOLOv5x model achieved the best mAP (@IoU = 0.75) of 97.4%, which outperformed the YOLOv5s model by 0.8 percent;however, the inference speed of the YOLOv5x in comparison with the YOLOv5s models was six times as slow. Therefore, the YOLOv5s model was employed in real-time online waste detection, which resulted in 82.1% mAP (@IoU = 0.5) after being trained on real images from the waste-sorting platform. © 2023 IEEE.

关键词： Classification (of information)

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AGILE: Approach-based Grasp Inference Learned from Element Decomposition

AGILE: Approach-based Grasp Inference Learned from Element D...

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

作者： MohammadHossein Koosheshi Hamed Hosseini Mehdi Tale Masouleh Ahmad Kalhor Mohammad Reza Hairi Yazdi Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran School of Mechanical Engineering College of Engineering University of Tehran

humans, this species expert in grasp detection, can grasp objects by taking into account hand-object positioning information. This work proposes a method to enable a robot manipulator to learn the same, grasping objects in the most optimal way according to how the gripper has approached the object. Built on deep learning, the proposed method consists of two main stages. In order to generalize the network on unseen objects, the proposed Approach-based Grasping Inference involves an element decomposition stage to split an object into its main parts, each with one or more annotated grasps for a particular approach of the gripper. Subsequently, a grasp detection network utilizes the decomposed elements by Mask R-CNN and the information on the approach of the gripper in order to detect the element the gripper has approached and the most optimal grasp. In order to train the networks, the study introduces a robotic grasping dataset collected in the Coppeliasim simulation environment. The dataset involves 10 different objects with annotated element decomposition masks and grasp rectangles. The proposed method acquires a 90% grasp success rate on seen objects and 78% on unseen objects in the Coppeliasim simulation environment. Lastly, simulation-to-reality domain adaptation is performed by applying transformations on the training set collected in simulation and augmenting the dataset, which results in a 70% physical grasp success performance using a Delta parallel robot and a 2 -fingered gripper.

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