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Manufacturing Knowledge Graph-Based Disentangled Prediction Framework and Industrial Practices

Manufacturing Knowledge Graph-Based Disentangled Prediction ...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Qingkai Sui Yalin Wang Chenliang Liu Central South University School of Automation Changsha China

Quality prediction assumes a pivotal role in manufacturing processes, serving as a linchpin to uphold product uniformity while mitigating the incidence of defects. However, the wealth of prior knowledge inherent in the manufacturing process is often underutilized, leading to limited predictive performance. Simultaneously, the integration of fragmented knowledge in the manufacturing industry often overlooks the ubiquitous presence of time series data, resulting in incomplete knowledge. To address these issues, this paper proposes a manufacturing knowledge graph-based disentangled prediction framework (MFKG-DP Framework). First, a comprehensive manufacturing knowledge graph construction method is proposed, which completes graph relationships through Bayesian network analysis of time series. The second part is the knowledge graph-based disentangled prediction method, which aims to design disentangled mapping matrixes according to the variable relationship provided by the knowledge graph. This facilitates the segmentation and disentanglement of hidden layer features, contributing to enhanced prediction capabilities. Furthermore, this paper incorporates the proposed disentanglement method into deep state space models, creating a disentangled deep state space model called DisSSM. Finally, the effectiveness of the proposed MFKG-DP Framework and DisSSM is validated in industrial processes within manufacturing enterprises.

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Locating Screw Grooves in Non-Visual Environments Based on Force Feedback Control

Locating Screw Grooves in Non-Visual Environments Based on F...

引用

robotics, Control and automation (ICRCA), international conference on

作者： Jia Quan Liu Zhi Feng Zhou Hong Wei Ren School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

To address the challenge of quickly and accurately locating screw grooves in visionless environments using a robotic arm, this paper proposes a low-cost improved scanning-based search method. The method eliminates the influence of force sensor bias and load to obtain precise environmental force data. Subsequently, Fourier Transform is employed to convert time-domain signals into the frequency domain, identifying and recording specific frequency changes corresponding to contact with the screw groove. The torque variation signals generated when the screwdriver contacts the groove are primarily determined by the physical characteristics of the groove, such as its depth and width. This specific frequency can be used as a reference standard to rapidly locate grooves of screws with identical specifications. To validate the effectiveness of the proposed scanning-based search method, repeated simulations were conducted. The simulation results demonstrate that once the dominant frequency is stabilized through multiple scans, it is no longer necessary to make incremental adjustments or validations, enabling rapid detection and precise localization by directly utilizing the dominant frequency.

关键词： Time-frequency analysis Fourier transforms Torque Search methods Simulation Fasteners Robot sensing systems Recording Time-domain analysis Standards

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A Comparative Analysis in Using Deep Learning models Which Results in Efficient Image Data Augmentation 4

A Comparative Analysis in Using Deep Learning Models Which R...

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4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Kumar Nassa, Vinay Kumar Satpathy, Susanta Pathak, Mrunal K. Takale, Dattatray G. Rawat, Swati Rana, Samir Rajarambapu Institute of Technology Rajaramnagar Sakharale Tel Walwa Dist. Maharshtra Sangli415414 India Vignan Foundation of Science Technology and Research Vadlamudi Computer Science and Engineering Andhra Pradesh Guntur India Aissms Institute of Information Technology Savitribai Phule Pune University Department of Information Technology Pune India Vishwakarma Institute of Information Technology Sppu Computer Engineering Pune India Ambala Mullana India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350393248

'Deep learning' is now the speediest field in the 'Machine Learning (ML)' and 'Deep Neural Networks fields (DNN)'. 'Convolution Neural Networks (CNN)' are probably the most popular used technique for image classification and prediction among many DNN architectures. 'Deep neural networks' as well as their accompanying machine learning, despite their achievements and promises, face a number of serious challenges. CNNs have achieved attempting to cut results in a variety of multiclass classification, and they still face a number of challenges, despite their broad perspectives. They are driven primarily by the vast scale of the models, which can exceed millions of features, as well as a lack of valid training data sets, and they have issues with overfitting and generalization abilities. This research study focuses on the most commonly discussed difficulty in the area of 'machine learning', which is the absence of adequate 'training data' or an 'unequal class imbalance' within databases. The use of such data augmentation is one method of addressing this issue. Moreover, researchers have evaluated and analysed several data augmentation approaches throughout this research. Secondary method is used for this paper to gather in depth information about deep learning methods to get results in Image data augmentation. © 2023 IEEE.

关键词： Deep neural networks

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TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Data

TBD Pedestrian Data Collection: Towards Rich, Portable, and ...

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IEEE international conference on robotics and automation (ICRA)

作者： Allan Wang Daisuke Sato Yasser Corzo Sonya Simkin Abhijat Biswas Aaron Steinfeld Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Social navigation and pedestrian behavior research has shifted towards machine learning-based methods and converged on the topic of modeling inter-pedestrian interactions and pedestrian-robot interactions. For this, large-scale datasets that contain rich information are needed. We describe a portable data collection system, coupled with a semi-autonomous labeling pipeline. As part of the pipeline, we designed a label correction web application that facilitates human verification of automated pedestrian tracking outcomes. Our system enables large-scale data collection in diverse environments and fast trajectory label production. Compared with existing pedestrian data collection methods, our system contains three components: a combination of top-down and ego-centric views, natural human behavior in the presence of a socially appropriate "robot", and human-verified labels grounded in the metric space. To the best of our knowledge, no prior data collection system has a combination of all three components. We further introduce our ever-expanding dataset from the ongoing data collection effort – the TBD Pedestrian Dataset and show that our collected data is larger in scale, contains richer information when compared to prior datasets with human-verified labels, and supports new research opportunities.

关键词： Pedestrians Pipelines Robot vision systems Production Data collection Trajectory Behavioral sciences

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Depth Recognition of Hard Inclusions in Tissue Phantoms for Robotic Palpation

Depth Recognition of Hard Inclusions in Tissue Phantoms for ...

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2022 IEEE international conference on Real-Time Computing and robotics, RCAR 2022

作者： Zhou, Zhenning Fang, Senlin Ye, Chaoxiang Mi, Tingting Huang, Binhua Li, Xiaoyu Yi, Zhengkun Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT Branch Shenzhen518055 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Medical palpation is an effective diagnosis method in which physicians use tactile sensation to diagnose a patient's pathology. Robotic palpation is a novel technique that leverages robots to assist medical diagnosis. The problem of tactile information loss in Robot-assisted Minimally Invasive Surgery (RMIS) has limited the development of the robot-assisted surgical system. Meanwhile, surgeons are difficult to acquire some key information about lesions only via visual feedback, such as tumor depth. To address the issue, we propose a tactile perception algorithm on the basis of the CNN-LSTM network, which achieves the depth recognition of hard inclusions in tissue phantoms. The method realizes the classification of twelve depths of hard inclusions. In addition, due to using hypergeometric distribution encoding, the proposed method can exploit the ordinal information of the labels to significantly improve the recognition accuracy rate. The experimental results on 720 real tactile data show that the average recognition rate is 96.45%. Compared with other state-of-the-art methods, the recognition accuracy rate of the proposed algorithm is the highest. © 2022 IEEE.

关键词： Tumors

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Research on Grasping Detection Method of Manipulator Based on SOLOV2

Research on Grasping Detection Method of Manipulator Based o...

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international conference on Autonomous Unmanned Systems (ICAUS)

作者： Yuan, Shudong Zuo, Zhen Sun, Bei Liu, Yaopeng Natl Univ Def Technol Sch Intelligent Sci Changsha Peoples R China

ISBN: (纸本)9789819904815;9789819904792;9789819904785

The steps of the robotic arm to grasp the target are mainly divided into recognition and grasping, the current main methods of target recognition can be divided into two, one is to use the traditional method to complete the target extraction using the feature information of the object, and the other is to use the deep learning method, that is, by making and training the data set in the deep learning model, the data indicator iterative convergence and then the recognition of the target. This paper mainly describes how to improve the SOLOV2 deep learning model. Firstly, through the comparative experiment of the backbone network, Resnet18 network [1] with good example segmentation effect is selected as the backbone network. Finally, a visual attention mechanism is added to effectively improve the detection accuracy and complete the target identification and detection. The precision of target recognition and detection and the grasping efficiency of manipulator are improved effectively. The research results show that: the image segmentation and object segmentation experiments on the homemade robotic arm grasping dataset achieve the highest MAP50 value of 98.8%;at the same time, this paper obtains some threshold parameters with better detection degree through comparison experiments, instead of constantly adjusting the a priori parameters, and achieves better detection accuracy and stronger generalization ability compared with other deep learning-based detection models, thus effectively improving the grasping efficiency of the robotic arm.

关键词： Robotic arm SOLOV2 Gripping efficiency Deep learning Artificial intelligence robotics

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Collaborative Path Planning for Agricultural Mobile Robots: A Review

Collaborative Path Planning for Agricultural Mobile Robots: ...

引用

international conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Wang, Xinyuan Yang, Liying Huang, Zhaoxiong Ji, Zhenping He, Yuqing School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9789811694912

In the past decade, Chinese agriculture has been transforming from a small-scale fragmented smallholder economy to large-scale modern agriculture, which has prompted the development of mobile robot systems in agriculture. Multi-robot autonomous navigation has become the most fundamental and daunting challenge in this development process, involving autonomous control, path planning and other aspects. In this paper, the cooperative control strategies and path planning methods for agricultural mobile robots are described, and the application of path planning methods in different agricultural scenarios is analyzed in detail from the perspectives of both online and offline planning. The conclusions show that multi-robot collaborative path planning has been successfully applied to the navigation of agricultural robotic operation scenarios, and the future development should be towards the direction of scale, autonomy, and intelligence. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mobile robots

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Efficient solution method based on inverse dynamics for optimal control problems of rigid body systems

Efficient solution method based on inverse dynamics for opti...

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IEEE international conference on robotics and automation (ICRA)

作者： Katayama, Sotaro Ohtsuka, Toshiyuki Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto Japan

ISBN: (纸本)9781728190778

We propose an efficient way of solving optimal control problems for rigid-body systems on the basis of inverse dynamics and the multiple-shooting method. We treat all variables, including the state, acceleration, and control input torques, as optimization variables and treat the inverse dynamics as an equality constraint. We eliminate the update of the control input torques from the linear equation of Newton's method by applying condensing for inverse dynamics. The size of the resultant linear equation is the same as that of the multiple-shooting method based on forward dynamics except for the variables related to the passive joints and contacts. Compared with the conventional methods based on forward dynamics, the proposed method reduces the computational cost of the dynamics and their sensitivities by utilizing the recursive Newton-Euler algorithm (RNEA) and its partial derivatives. In addition, it increases the sparsity of the Hessian of the Karush-Kuhn-Tucker conditions, which reduces the computational cost, e.g., of Riccati recursion. Numerical experiments show that the proposed method outperforms state-of-the-art implementations of differential dynamic programming based on forward dynamics in terms of computational time and numerical robustness.

关键词： Sensitivity automation Heuristic algorithms conferences Optimal control Robustness Computational efficiency

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Learning Low-Dimensional Strain models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control

Learning Low-Dimensional Strain Models of Soft Robots by Loo...

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IEEE international conference on Soft robotics (RoboSoft)

作者： Ricardo Valadas Maximilian Stölzle Jingyue Liu Cosimo Della Santina Cognitive Robotics Department Delft University of Technology Delft Netherlands Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge USA

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

Obtaining dynamic models of continuum soft robots is central to the analysis and control of soft robots, and researchers have devoted much attention to the challenge of proposing both data-driven and first-principle solutions. Both avenues have, however, shown their limitations; the former lacks structure and performs poorly outside training data, while the latter requires significant simplifications and extensive expert knowledge to be used in practice. This paper introduces a streamlined method for learning low-dimensional, physicsbased models that are both accurate and easy to interpret. We start with an algorithm that uses image data (i.e., shape evolutions) to determine the minimal necessary segments for describing a soft robot’s movement. Following this, we apply a dynamic regression and strain sparsification algorithm to identify relevant strains and define the model’s dynamics. We validate our approach through simulations with various planar soft manipulators, comparing its performance against other learning strategies, showing that our models are both computationally efficient and 25x more accurate on out-of-training distribution inputs. Finally, we demonstrate that thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.

关键词： Training Accuracy Transmission line matrix methods Shape Computational modeling Heuristic algorithms Training data Soft robotics Aerodynamics Strain

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YOLOv10-Based Object Detection Method with Optimized Defogging Algorithm for Autonomous Driving in Foggy Environments

YOLOv10-Based Object Detection Method with Optimized Defoggi...

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Intelligent robotics and Automatic Control (IRAC), international conference on

作者： Yuanchuan Wang Yanfeng Lu Xi'an Jiaotong University Xi'an China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

In foggy driving environments, the precision of object detection by autonomous driving systems is significantly diminished compared to clear weather conditions. To date, there is an abundance of work on object detection, yet research focusing on object detection in foggy scenarios for autonomous driving remains scarce. Firstly, due to the scarcity of publicly available datasets for vehicles in foggy conditions, this experiment constructs a foggy environment vehicle dataset. Subsequently, I integrated an enhancement module based on the NLD (Non-Local Image Dehazing) algorithm into the YOLOv10 model, yielding a new model capable of vehicle detection in foggy environments. Furthermore, I performed the same operation on various other YOLO models and conducted model evaluations. The results demonstrate that the new model, incorporating NLD, demonstrates a marked enhancement in object detection accuracy compared to the original models, enhancing the precision and robustness of vehicle detection in foggy conditions, thus offering greater application value in foggy autonomous driving scenarios. The foggy dataset for the experiment is available on GitHub: https://***/yiming676/Hazed-VehicleDataset.

关键词： YOLO Measurement Analytical models Robustness Real-time systems Autonomous vehicles Standards Robots Software development management Meteorology

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