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Efficient 3D object tracking approach based on convolutional neural network and Monte Carlo algorithms used for a pick and place robot
2019
Photonics and Education in Measurement Science 2019
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Considering such limitations, this paper presents a robot vision system based on Convolutional Neural Networks (CNN) and Monte Carlo algorithms. ...
Currently, Deep Learning (DL) shows us powerful capabilities for image processing. But it cannot output the exact photometric process parameters and shows non-interpretable results. ...
METHOD OVERVIEW In this section we explain the 3D object detection system based on deep learning approach and classic method. This system is to support a pick and place robot. ...
doi:10.1117/12.2530333
fatcat:6xo343h7wbgidgq5hzntzc4d4y
Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
[article]
2020
arXiv
pre-print
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Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deep-learned distribution ...
This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. ...
ACKNOWLEDGMENT We thank NVIDIA Co. for donating a high-power GPU on which this work was performed. ...
arXiv:2003.01875v2
fatcat:md6fxm4bfveevhlj233bs3di6i
Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
2021
Applied Sciences
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To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system ...
In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. ...
In Section 3, the improved Pure Pursuit algorithm using turning prediction-based speed adjustment is introduced. the deep learning-based selection strategy using 2D LiDAR point-cloud features for localization ...
doi:10.3390/app11135963
fatcat:j2dz7pvvgbehfi5n3pukluzfxa
Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications
2020
Complexity
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SDL is a type of deep learning framework, designed for optimal inference, combining the Rigid Body Dynamics (RBD) model and Nonparametric Deep Learning (NDL) model. ...
In smart cities and factories, robotic applications require high accuracy and security, which depends on precise inverse dynamics modeling. ...
In this work, a method that is based on deep learning and semiparametric approach is presented. e method is formalized in the framework of what is called Semiparametric Deep Learning (SDL), designed for ...
doi:10.1155/2020/9053715
fatcat:vcise27nsfgdtnibwrt4p37o7e
YOLOv5 Model Application in Real-Time Robotic Eggplant Harvesting
2024
Journal of Agricultural Science
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YOLOv5 (nano-small-medium and large models) was used for the deep learning method. All training and test metric values of the models were analyzed. ...
Deep learning is a powerful tool for automation in agriculture with applications ranging from disease identification and crop yield detection to fruit ripeness classification. ...
Another area where deep learning finds wide application is the automation of industries. ...
doi:10.5539/jas.v16n2p9
fatcat:azaz3hypg5h6fk5jiahapx4i3a
Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs
2019
Sensors
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Different from other research that has mainly focused on 3D information, this study first applies an instance segmentation-based deep learning approach using 2D image data for classifying and localizing ...
Random bin-picking is a prominent, useful, and challenging industrial robotics application. ...
Acknowledgments: The authors wish to thank Joel Vidal Verdaguer, a member of AIRLab, for providing useful information and the code for communicating with the robot, which served as the foundation for the ...
doi:10.3390/s19163602
fatcat:ttef24pmq5elboxekxrvgu2oa4
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach
[article]
2020
arXiv
pre-print
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Our proposed learning framework for position-controlled robots was extensively evaluated on contact-rich insertion tasks on a variety of environments. ...
The main contribution of this work is a learning-based method to solve peg-in-hole tasks with position uncertainty of the hole. ...
Therefore, we focus on industrial robot manipulators, which are mainly position-based-controlled. Abu-Dakka et al. [23] proposed a learning method based on iterative learning control (ILC). ...
arXiv:2008.10224v2
fatcat:qqsyum7m6falxdjsuiw662676a
The Simultaneous Localization and Mapping (SLAM)-An Overview
2021
Surveying and Geospatial Engineering Journal
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One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. ...
Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation ...
DEEP LEARNING FOR SLAM In recent years, research regarding SLAM is applied using deep learning techniques to replace the traditional visual odometry approach which is based on geometric processing. ...
doi:10.38094/sgej1027
fatcat:o6s4zqia3rbe5fhu73nnpithwu
Vision-Based Intelligent Perceiving and Planning System of a 7-DoF Collaborative Robot
2021
Computational Intelligence and Neuroscience
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In this paper, an intelligent perceiving and planning system based on deep learning is proposed for a collaborative robot consisting of a 7-DoF (7-degree-of-freedom) manipulator, a three-finger robot hand ...
Besides, a faster and more precise planning process was proposed. Deep learning based on a new CNN (convolution neural network) was designed to realize intelligent grasping planning for robot hand. ...
In this research, the performance of the new intelligent planning method for robot hand based on deep learning and trajectory planning for manipulator based on the combination of RRT and interpolation ...
doi:10.1155/2021/5810371
pmid:34630547
pmcid:PMC8497130
fatcat:4kskk7glmresvonw7namb4pk7a
Analysis of Current Situation, Demand and Development Trend of Casting Grinding Technology
2022
Micromachines
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Although applications for online detection and constant grinding contact force control exist in industry, there are challenges in material removal prediction and three-dimensional high-precision matching ...
The research direction of casting polishing has mainly focused on online robot detection, material removal prediction, constant grinding contact force control, and high-precision matching. ...
In view of the small workspace and poor flexibility, industrial robot grinding based on the compliant control theory adopts force and position control for grinding. ...
doi:10.3390/mi13101577
fatcat:5kw2vzw6cngklbwyon6xfj4ucm
Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in AI, Machine Learning, and Digital Twin
[article]
2024
arXiv
pre-print
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The overarching goal of this article is to present a comprehensive perspective on the current state of fault diagnosis and fault-tolerant control within the context of robotic manipulators, positioning ...
This journey encompasses the transition from model-based and signal-based schemes to the role of sensors, setting the stage for an exploration of the present-day paradigm shift enabled by AI, ML, and DTT ...
This allows the DT to detect and mitigate positioning errors by adjusting motor angles. While in [266] a DT-enabled system for 3D positioning and error compensation in robotic arms is designed. ...
arXiv:2402.02980v1
fatcat:yoltttvtdvcqth6z6odppenhj4
Research on grasping model based on visual recognition robot arm
2024
Applied and Computational Engineering
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elements on the robotic arm for use. ...
This article mainly systematically describes the research based on the visual recognition robotic arm. ...
CNN model is one type of neural model for deep learning, which is based on convolution, padding, and pooling. ...
doi:10.54254/2755-2721/41/20230703
fatcat:oedn5efqmbatzh36j3qtjt7mnu
2020 Index IEEE Transactions on Automation Science and Engineering Vol. 17
2020
IEEE Transactions on Automation Science and Engineering
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Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning
and Two-Stage Pyramidal Feature Prediction. ...
., +, TASE April 2020 847-857 Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction. ...
., +, TASE Jan. 2020 41-55 PROLOG A System Architecture for CAD-Based Robotic Assembly With Sensor-Based Skills. ...
doi:10.1109/tase.2020.3037603
fatcat:kyt63444lfc45amrjebyjw34qu
An Overview of Perception and Decision-Making in Autonomous Systems in the Era of Learning
[article]
2020
arXiv
pre-print
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First, we delineate the existing classical simultaneous localization and mapping (SLAM) solutions and review the environmental perception and understanding methods based on deep learning, including deep ...
With the applications of deep learning and reinforcement learning, the perception and decision-making abilities of autonomous systems are being efficiently addressed, and many new learning-based algorithms ...
are proposed based on deep learning. ...
arXiv:2001.02319v3
fatcat:z3zhp2cyonfqtlttl2y57572uy
AMID: Accurate Magnetic Indoor Localization Using Deep Learning
2018
Sensors
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Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics ...
AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. ...
Data Collection and Noise Reduction Deep learning usually requires a large amount of training data. ...
doi:10.3390/s18051598
pmid:29772794
pmcid:PMC5982601
fatcat:sbnjhrdj2vct3dl7bm3r3dy2ya
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