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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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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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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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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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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-lab.l 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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