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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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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 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. © 2023 IEEE.

关键词： Drones

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Grasp the Graph (GtG): A Super Light Graph-RL Framework for robotic Grasping 11

Grasp the Graph (GtG): A Super Light Graph-RL Framework for ...

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

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

ISBN: (纸本)9798350308105

This paper introduces Grasp the Graph, an innovative framework addressing challenges posed by processing point clouds with conventional neural networks for robotic grasping. Such challenges often lead to the conversion of point clouds to multi-view images or voxel grids, requiring large networks with millions of parameters. Grasp the Graph formulates robotic grasping as a one-step reinforcement learning problem and leverages graph representation and graph neural networks to efficiently process geometric relationships in point clouds. The lightweight design with only 67k parameters facilitates swift training and strong generalization to novel objects, offering the capability to propose multiple valid grasping candidates. Empirical testing demonstrates the effectiveness of the proposed approach, achieving a 90.4% success rate on test objects while being trained with only 10 objects. Even with a three-camera setup during training, reducing the cameras during evaluation minimally impacts performance, resulting in less than a 3% decrease.1 © 2023 IEEE.

关键词： Reinforcement learning

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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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2D Skeleton-Based Keypoint Generation Method for Grasping Objects with Roughly Uniform Height Variation 11

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

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

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

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： robotics

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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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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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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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HUGGA: human-like Grasp Generation with Gripper's Approach State Using Deep Learning 11

HUGGA: Human-like Grasp Generation with Gripper's Approach S...

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

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

ISBN: (纸本)9798350308105

In this study, an attempt has been made to develop a new method for producing a successful grasp based on human behavior, by considering the gripper's approach state in the final grasp of an object. The dataset collected for the purpose of this paper consists of 14 different daily usage objects with different orientations (In total 2526 images) which a human approaches and grasps them. Data collection has been done based on a protocol shown in TableI to cover all achievable states. From Fig1, it can be seen that the dataset includes one image from a single object without the user's hand. The second image contains the human hand approach and its configuration regarding the object which is subject to be grasped and the third image represents the object which is grasped by the human hand and thus evidently contains both object and human hand grasp configuration. It is important to acknowledge that providing a static hand image does not suffice to facilitate the acquisition of human-like grasping capabilities by robot grippers, so, a mapping from hand fingers to a two-finger gripper should be implemented to fill this gap. In the collected dataset, all grasps are performed by the index finger and thumb to make it possible to map the coordinates of each finger on a gripper finger. Using MediaPipe, which operates a single shot detection network, with only an RGB image containing a human hand the index finger and thumb landmarks were extracted. Finally, a detailed human approach and grasp information were availab.e, and with the collected data, a two-input and one-output deep neural network was trained. The designed model produced a grasp with a human pattern by receiving only the RGB image of the object and the approach vector to the object. Ultimately, through testing and evaluation based on existing criteria, the trained network successfully produced a grasp with 61% accuracy in grasp rectangle overlap, 97% in grasp angle and overall 60.5% met two established grasp crite

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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