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检索条件"机构=Human and Robot Interaction Laboratory Electrical and Computer Engineering Department"

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Optimal Path Planning of Mobile robots Using IsoCost-Based Dynamic Programming 31

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

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31st International Conference on electrical engineering, ICEE 2023

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

ISBN: (纸本)9798350312560

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. © 2023 IEEE.

关键词： Dynamic programming

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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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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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AGILE: Approach-based Grasp Inference Learned from Element Decomposition 11

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

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

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

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： Grippers

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Experimental Study on Chess Board Setup Using Delta Parallel robot Based on Deep Learning 11

Experimental Study on Chess Board Setup Using Delta Parallel...

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

作者： Yarmohammadi, Parsa Khomami, Navid Asadi 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 Electrical and Computer Engineering Tehran Iran University of Tehran School of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798350308105

In recent years, robotics and object detection applications have seen remarkable improvements in executing routine tasks. In the near future, it is expected that most of the tasks can be performed by means of these two foregoing tools. This paper presents a system that combines these two approaches in order to automate the setup of a chessboard using a Delta parallel robot equipped with a two-finger gripper designed for grasping small objects. This system utilizes the so-called YOLO model for object detection in order to detect the chess pieces. The system begins by taking a picture of a scene, where the chess pieces are placed randomly around a board. The location of the chessboard is predefined and YOLO is used to detect and locate chess pieces in the given picture. The final object detection model used to execute this task is YOLOv5m, although this model encountered some challenges in detecting four types of chess pieces from a top view, these challenges were addressed by increasing the instances of those objects in the dataset. Upon extracting the location of each piece, the coordinates are sent to the robot, and the Delta parallel robot arranges the initial setup of a chess game. The model achieved an overall mAP of 0.99, indicating excellent precision and the grasp success rate in the process of grasping the chess pieces was 95%. This study demonstrates the potential of automating everyday tasks by exploring the intersection of robotics and artificial intelligence and can be well extended to tasks, namely, sorting medications and pills in hospitals, sorting different wastes like plastic and papers for recycling, identifying and removing spoiled fruits in agricultural industries, etc. © 2023 IEEE.

关键词： Object detection

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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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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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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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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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