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Simultaneously Calibration of Multi Hand-Eye Robot system Based on Graph

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arXiv 2023年

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi The Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Electrical Engineering and Computer Science Australian National University Canberra2601 Australia The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method. © 2023, CC BY-SA.

关键词： Multipurpose robots

Retraction Note: An integrated 3D model based face recognition method using synthesized facial expressions and poses for single image applications

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Applied Nanoscience 2024年第4期14卷 727-727页

作者： Milad, Ali Yurtkan, Kamil Management Information System Department Cyprus International University Nicosia Turkey Computer Engineering Department Faculty of Engineering Cyprus International University Lefkosa Turkey Artificial Intelligence Application and Research Center Cyprus International University Lefkosa Turkey

AESC-TransUnet:Attention Enhanced Selective Channel Transformer U-Net for Medical Image Segmentation

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Signal, Image and Video Processing 2025年第9期19卷

作者： Huang, Wenlei Xiao, Hongxiang College of Computer Science and Engineering Guilin University of Technology Guangxi Guilin541006 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guangxi Guilin541004 China

Deep learning has greatly advanced medical image segmentation, especially with the integration of U-Net and Transformer architectures. However, challenges remain in clinical practice, such as low resolution, blurred edges, and difficulty in accurately segmenting small lesions. Existing methods, including various TransUNet variants, struggle with these issues. For instance, while TransUNet leverages the Transformer to capture global context, it suffers from the inability to preserve fine-grained local details. Similarly, Swin-Unet, which uses the Swin Transformer, excels in global feature extraction but often loses precision in complex backgrounds and small structures. The classic U-Net model, despite its strong performance in extracting local features, struggles with segmentation accuracy in low-contrast or complex areas due to its upsampling and downsampling *** address these limitations, we propose AESC-TransUNet, a novel network combining Efficient Selective Channel Attention (ESCA) and Convolution-Transformer fusion (ConvFormer). ESCA improves feature representation by using parallel processing and selective channel enhancement, effectively addressing the low resolution and blurred edges problem while preserving local details. ConvFormer optimizes the integration of both global and local information by combining convolution with self-attention mechanisms, reducing positional loss during upsampling and improving edge segmentation. Experimental results show that AESC-TransUNet significantly outperforms existing methods, achieving higher segmentation accuracy, particularly for small lesions and complex structures. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

Tactile-Based Object Pose Estimation Employing Extended Kalman Filter

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Tactile-Based Object Pose Estimation Employing Extended Kalm...

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.

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Prediction of methane levels in underground coal mines using artificial neural networks 2

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Prediction of methane levels in underground coal mines using...

2nd International Conference on Advances in Big Data, Computing and Data Communication systems, icABCD 2019

作者： Mathatho, Sello Owolawi, Pius Adewale Tu, Chunling Dept. Computer System Engineering Tshwane University of Technology TUT Tshwane South Africa

ISBN: (纸本)9781538692363

Underground coal mines are known for having extremely volatile environments that are gas prone, especially to methane that can unexpectedly erupt or spike. In this paper, a methane levels forecast system is proposed for underground coal mining areas. The system trains an artificial neural network (ANN) using past data to predict methane levels. Supervised learning method is used to train the ANN with four algorithms (Levenberg-Marquardt, Gradient descent, Scaled Conjugate Gradient, and Resilient back propagation) were compared. The Levenberg-Marquardt (LM) proved to be the most reliable algorithm among the considered methods. The prediction performance is also compared by considering the mean square error (MSE) and root mean square error (RMSE), where the LM training method performs the best. The selection of hidden layer neuron numbers is also discussed to balance the training performance and overfitting problems. The study shows that ANN techniques can be used to predict methane levels with acceptable accuracy. The paper concludes that the accuracy of ANN model is dependent on the combination of input parameters and the training algorithm. © 2019 IEEE.

关键词： Methane

A quantitative pairwise comparison-based constraint handling technique for constrained optimization

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arXiv 2023年

作者： Huang, Ting Zhang, Qiang Pedrycz, Witold Yang, Shanlin School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei Box 270 Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Anhui Hefei230009 China Department of Electrical & Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada

This study proposes a new constraint handling technique for assisting metaheuristic optimization algorithms to solve constrained optimization problems more effectively and efficiently. Given any two solutions of any constrained optimization problems, they are first mapped into a two-dimensional Cartesian coordinate system with their objective function value differences and constraint violation differences as the two axes. To the best of our knowledge, we are the first to deal with constraints by building such a Cartesian coordinate system. Then, the Cartesian coordinate system is divided into a series of grids by assigning ranks to different intervals of differences. In this way, a pairwise comparison criterion is derived with the use of the fused ranks, which achieves non-hierarchical comparison neither preferring objective function values nor constraint violations, resulting in more accurate evaluation compared with existing techniques. Moreover, an evaluation function that is equivalent to the pairwise comparison criterion is proposed, which further improves computational efficiency. The effectiveness and efficiency of the proposed constraint handling technique are verified on two well-known public datasets, that is, CEC 2006 and CEC 2017. The results demonstrate that metaheuristic optimization algorithms with using the proposed constraint handling technique can converge to a feasible optimal solution faster and more reliably. Experimental analysis on the parameters involved reveal guidance for their optimal *** Codes 90-08 (Primary) Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

BiofilmScanner: A Computational Intelligence Approach to Obtain Bacterial Cell Morphological Attributes from Biofilm Image

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arXiv 2023年

作者： Rahman, Md Hafizur Azam, Md Ali Hossen, Md Abir Ragi, Shankarachary Gadhamshetty, Venkataramana GM Core System Tools & Automation 29755 Louis Chevrolet Road WarrenMI48093 United States GE HealthCare 9900 W Innovation Dr WauwatosaWI53226 United States Department of Computer Science University of South Carolina ColumbiaSC29208 United States Department of Electrical Engineering South Dakota Mines Rapid CitySD57701 United States Department of Civil and Environmental Engineering South Dakota Mines Rapid CitySD57701 United States

Desulfovibrio alaskensis G20 (DA-G20) is utilized as a model for sulfate-reducing bacteria (SRB) that are associated with corrosion issues caused by microorganisms. SRB-based biofilms are thought to be responsible for the billion-dollar-per-year bio-corrosion of metal infrastructure. Understanding the extraction of the bacterial cells’ shape and size properties in the SRB-biofilm at different growth stages will assist with the design of anti-corrosion techniques. However, numerous issues affect current approaches, including time-consuming geometric property extraction, low efficiency, and high error rates. This paper proposes BiofilScanner, a Yolact-based deep learning method integrated with invariant moments to address these problems. Our approach efficiently detects and segments bacterial cells in an SRB image while simultaneously invariant moments measure the geometric characteristics of the segmented cells with low errors. The numerical experiments of the proposed method demonstrate that the BiofilmScanner is 2.1x and 6.8x faster than our earlier Mask-RCNN and DLv3+ methods for detecting, segmenting, and measuring the geometric properties of the cell. Furthermore, the BiofilmScanner achieved an F1-score of 85.28% while Mask-RCNN and DLv3+ obtained F1-scores of 77.67% and 75.18%, respectively. © 2023, CC BY.

关键词： Biofilms

Experimental Realization of Entangled Coherent States in Two-dimensional Harmonic Oscillators of a Trapped Ion

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arXiv 2023年

作者： Jeon, Honggi Kang, Jiyong Kim, Jaeun Choi, Wonhyeong Kim, Kyunghye Kim, Taehyun Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Automation and System Research Institute Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Inter-university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

Entangled coherent states play pivotal roles in various fields such as quantum computation, quantum communication, and quantum sensing. We experimentally demonstrate the generation of entangled coherent states with the two-dimensional motion of a trapped ion system. Using Raman transitions with appropriate detunings, we simultaneously drive the red and blue sidebands of the two transverse axes of a single trapped ion and observe multi-periodic entanglement and disentanglement of its spin and two-dimensional motion. Then, by measuring the spin state, we herald entangled coherent states of the transverse motions of the trapped ion and observe the corresponding modulation in the parity of the phonon distribution of one of the harmonic oscillators. Lastly, we trap two ions in a linear chain and realize Mølmer-Sørensen gate using two-dimensional motion. © 2023, CC BY.

关键词： Trapped ions

Super-resolution Reconstruction of Single Image for Latent features

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arXiv 2022年

作者： Wang, Xin Yan, Jing-Ke Cai, Jing-Ye Deng, Jian-Hua Qin, Qin Cheng, Yao School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu610000 China School of Marine Engineering Guilin University of Electronic Technology Guilin541000 China School of Computer and Information Security Guilin University of Electronic Technology Guilin541000 China

Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks. © 2022, CC0.

关键词： Image reconstruction