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A Novel Approach for Solving the Time-Varying Complex-Valued Linear Matrix Inequality Based on Fuzzy-Parameter Zeroing Neural Network

A Novel Approach for Solving the Time-Varying Complex-Valued...

引用

IEEE conference on Cybernetics and Intelligent Systems

作者： Jiajie Luo Jichun Li William Holderbaum Jiguang Li School of Computing Newcastle University Newcastle Upon Tyne UK School of Science Engineering and Environment University of Salford Manchester United Kingdom The North of England Robotics Innovation Centre University of Salford Manchester United Kingdom

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Solving linear matrix inequality (LMI) is crucial across diverse fields, and the emergence of zeroing neural networks (ZNN) presents a novel solution for the time-varying LMI (TV-LMI) challenge. However, the application of ZNN to solve the time-varying complex-valued LMI (TVCV-LMI) problem remains unexplored. Therefore, we introduce a novel fuzzy-parameter ZNN (FP-ZNN) model in this study to tackle the TVCV-LMI problem. With the introduction of fuzzy logic system (FLS), the FP-ZNN model is able to adjust the fuzzy convergence parameter (FCP) in a real-time manner, responding to any change in the system error and achieving the best performance. We also use an exponential activation function (EAF) in our study, which makes the FP-ZNN model fixed-time stable. To verify and illustrate the superior features of the elegant FPZNN model, detailed theoretical analysis, together with numerical experiments, are provided, and the results emphasize the fixed-time stability and adaptiveness of the FP-ZNN model further. As a novel approach, we provide an elegant solution to the TVCV-LMI problem in this paper.

关键词： Fuzzy logic Adaptation models Analytical models Computational modeling Neural networks Stability analysis Real-time systems

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Overcoming Hand and Arm Occlusion in Human-to-Robot Handovers: Predicting Safe Poses with a Multimodal DNN Regression Model

Overcoming Hand and Arm Occlusion in Human-to-Robot Handover...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Catherine Lollett Advaith Sriram Mitsuhiro Kamezaki Shigeki Sugano Future Robotics Organization Waseda University Tokyo Japan Department of Modern Mechanical Engineering Waseda University Tokyo Japan Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Handovers play a key role in human-robot interactions. However, current research focuses on visible-hand handovers, thereby heavily relying on hand detection. Large objects in human-robot interactions present a unique challenge: they inherently block the person’s hands and arms from the robot's view. This occlusion raises the robot’s risk of unintended physical contact with the person, leading to discomfort and safety concerns. This study aims to develop a model that can determine a pose for the robot that ensures a handover that avoids physical contact with the person, especially in scenarios when hands and arms are occluded. Toward this goal, a three-branch multimodal Deep Neural Network (DNN) regression model was implemented. First, a robust human-pose keypoints detection to calculate shoulder-elbow angles is applied. Secondly, we extract the refined object’s segmented mask. Thirdly, we compute two intrinsic object properties. The concatenated outputs from these branches pass through extra dense layers, resulting in the prediction of the robot's 14 arms-joint angles. Compared to an only keypoint data processed-based model, our multimodal approach made a 17.7% accuracy improvement. The experiments highlight each pipeline step’s significance, showing important results even when hands and arms were heavily occluded, adjusting to different variations.

关键词： Accuracy Computational modeling Pipelines Human-robot interaction Artificial neural networks Handover Predictive models

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Pedestrian Re-identification Based on Spatial Transformation and Multi-methods Feature Fusion

Pedestrian Re-identification Based on Spatial Transformation...

引用

international conference on robotics and automation Engineering (ICRAE)

作者： Zijian Diao Shuai Cao Wenwei Li Yongjun Cao Lei Zhou Weixi Huang Hongan He Jianan Liang School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Robotic Laboratory South China Robotics Innovation Research Institute Foshan China Guangdong Academy of Science Institute of Intelligent Manufacturing Guangzhou China South Glass Technology Co. LTD Foshan China

Aiming at the problem that the spatial features of pedestrian images are not aligned in current pedestrian re-identification and the network model cannot fully express the pedestrian information due to pose changes and occlusions, a method based on spatial transformation and multi-methods feature fusion is proposed. Firstly, for the pedestrian re-identification system, a processing method for enhancing the retrieval of pedestrians is provided, and the pedestrian images with more noise to be retrieved are denoised by means of side-window filtering; secondly, the spatial transformation network is improved. Channel attention and self-constrained branches are introduced to automatically align pedestrian spatial features to solve the problem of inconsistency in spatial semantic information caused by unaligned pedestrian image regions; then, multi-scale features are extracted from different deep layers of the backbone network, and coordinates attention and batch normalization of instances are integrated into different deep branches. Finally, the features of each branch are fused to obtain feature information with high representation ability. During the network training process, the dual loss function strategy is used to jointly optimize the model. Multiple experiments show that the proposed method has a higher recognition rate than other existing methods.

关键词： Training Adaptation models automation Adaptive systems Filtering Semantics Feature extraction

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Residual Denoising Diffusion models

Residual Denoising Diffusion Models

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jiawei Liu Qiang Wang Huijie Fan Yinong Wang Yandong Tang Liangqiong Qu State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Science Institutes for Robotics and Intelligent Manufacturing University of Chinese Academy of Sciences Shenyang University The University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose residual denoising diffusion models (RDDM), a novel dual diffusion process that decouples the traditional single denoising diffusion process into residual diffusion and noise diffusion. This dual diffusion framework expands the denoising-based diffusion models, initially uninterpretable for image restoration, into a unified and interpretable model for both image generation and restoration by introducing residuals. Specifically, our residual diffusion represents directional diffusion from the target image to the degraded input image and explicitly guides the reverse generation process for image restoration, while noise diffusion represents random perturbations in the diffusion process. The residual prioritizes certainty, while the noise emphasizes diversity, enabling RDDM to effectively unify tasks with varying certainty or diversity requirements, such as image generation and restoration. We demonstrate that our sampling process is consistent with that of DDPM and DDIM through coefficient transformation, and propose a partially path-independent generation process to better understand the reverse process. Notably, our RDDM enables a generic UNet, trained with only an L1 loss and a batch size of 1, to compete with state-of-the-art image restoration methods. We provide code and pre-trained models to encourage further exploration, application, and development of our innovative framework (https://***/nachifurlRDDM).

关键词： Schedules Computer vision Image synthesis Perturbation methods Noise reduction Noise Diffusion processes

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Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors

Sim2Real Bilevel Adaptation for Object Surface Classificatio...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Gabriele M. Caddeo Andrea Maracani Paolo Didier Alfano Nicola A. Piga Lorenzo Rosasco Lorenzo Natale Humanoid Sensing and Perception Istituto Italiano di Tecnologia Genoa Italy DIBRIS Universitá di Genova Genoa Italy MaLGa Center DIBRIS Universitá di Genova Genoa Italy IIT@MIT Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we address the Sim2Real gap in the field of vision-based tactile sensors for classifying object surfaces. We train a Diffusion Model to bridge this gap using a relatively small dataset of real-world images randomly collected from unlabeled everyday objects via the DIGIT sensor. Subsequently, we employ a simulator to generate images by uniformly sampling the surface of objects from the YCB Model Set. These simulated images are then translated into the real domain using the Diffusion Model and automatically labeled to train a classifier. During this training, we further align features of the two domains using an adversarial procedure. Our evaluation is conducted on a dataset of tactile images obtained from a set of ten 3D-printed YCB objects. The results reveal a total accuracy of 81.9%, a significant improvement compared to the 34.7% achieved by the classifier trained solely on simulated images. This demonstrates the effectiveness of our approach. We further validate our approach using the classifier on a 6D object pose estimation task from tactile data.

关键词： Training Bridges Accuracy Pose estimation Tactile sensors Manuals Diffusion models

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A simple data augmentation method for automatic modulation recognition via mixing signals

A simple data augmentation method for automatic modulation r...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Weiguo Shen Dongwei Xu Xinjie Xu Zhuangzhi Chen Qi Xuan Wei Wang Yun Lin Xiaoniu Yang The National Key Laboratory of Electromagnetic Space Security Jiaxing China The Binjiang Artificial Intelligence Institute of ZJUT Zhejiang University of Technology Hangzhou China The Binjiang Artificial Intelligence Institute of ZJUT Harbin Engineering University Harbin China The National Key Laboratory of Electromagnetic Space Security Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Automatic Modulation Recognition(AMR) is crucial in cognitive radio, transitioning from traditional methods to deep learning-based classification. However, deep learning requires large datasets for effective training, making data augmentation vital for enhancing model generalization and accuracy. It helps to expand the dataset, thereby improving the models’ generalization ability and accuracy. We proposes a novel data augmentation approach for AMR task based on signal mixing, with four specific methods designed to improve the performance of Mixed Signals. The experimental results demonstrate that the proposed methods lead to a notable improvement in the classification accuracy of deep learning-based AMR models in diverse scenarios. Notably, experiments conducted with datasets at a single signal-to-noise ratio show a significant increase in classification accuracy, confirming the effectiveness of the signal mixing method.

关键词： Deep learning Training Solid modeling Accuracy Modulation Data augmentation Data models Robustness Robots Signal to noise ratio

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Reinforcement Learning Control for a Flexible Joint Manipulator and Its Verification

Reinforcement Learning Control for a Flexible Joint Manipula...

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IEEE international conference on Mechatronics, robotics and automation (ICMRA)

作者： HeJia Gao ChuanFeng He JuQi Hu Yi Tong Wu School of Artificial Intelligence Anhui University Hefei China

ISBN: (数字)9798350307535

ISBN: (纸本)9798350307542

1 Flexible Joint Manipulator (FJM) is a system that can control its joints with high precision. This paper proposes a reinforcement learning (RL) method to control the FJM and achieve output constraint. The RL method can learn from the feedback of the system and adjust the control parameters accordingly. The paper also proves the stability of the system by using Lyapunov stability analysis. Finally, we verify the superiority of RL control method by comparative simulation.

关键词： Uncertainty Mechatronics Trajectory tracking Collaboration Reinforcement learning Manipulators Control systems Stability analysis Robustness Lyapunov methods

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Proposed Technique for Data Security with the AES Algorithm in Robot Operating System (ROS) 27

Proposed Technique for Data Security with the AES Algorithm ...

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27th international Computer Science and Engineering conference, ICSEC 2023

作者： Tanadechopon, Teerapong Kasemsontitum, Boontariga Institute of Field Robotics King Mongkut's Institute of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9798350342109

Currently, the Robot Operating System (ROS), is extensively used in various fields such as education, research, commercial and industry. This is due to the fact that ROS offers a range of packages that handle communication and hardware operation. However, it is widely known that ROS's security is still not robust. Transitioning from ROS to ROS2, which has enhanced security, might be somewhat challenging because some tasks might be in actual use in the industry or ongoing research projects. The issue of data security is also of paramount importance. There are currently various methods to hack into systems or intercept data in computer networks. After studying numerous related research works, we propose an encryption method for transmitted and received data using the AES algorithm, known as Layer-2. This method has been experimented with streaming images of four resolutions: 320× 240, 640× 480, 1280× 720, and 1920× 1080. By comparing the frame rate and CPU usage during both unencrypted and encrypted streaming, it was found that the software developed according to the concept of this research can stream in an encrypted format at the same frame rate as unencrypted streaming, with an average maximum increase in CPU usage of 19.98% on the publishing side. The encryption concept in this research is designed based on a text-based protocol combined with the function overloading feature of object-oriented programming. This allows for a quick add-on to operating ROS software with minimal modifications. Furthermore, it can be adapted for use with other data transmission methods, such as Publish/Subscribe models like NATS, MQTT, etc. © 2023 IEEE.

关键词： Cryptography

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Deep Learning-Based Deflection Correction and End-Point Control of Heavy-Duty Vertical Single-Link Flexible Manipulators

Deep Learning-Based Deflection Correction and End-Point Cont...

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IEEE international conference on robotics and Biomimetics

作者： Amir Hossein Barjini Sadeq Yaqubi S. Mohammad Tahamipour-Z Jouni Mattila Department of Automation Technology and Mechanical Engineering Tampere University Finland

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Flexible link manipulators (FLM) have gained significant importance due to their applications in lightweight robots, energy-efficient systems, and humanoid robots. In this paper, we propose a novel approach to modeling and controlling a single-link flexible manipulator (SLFM). First, a Vertical Single-Link Manipulator (VSLFM) is modeled, taking gravity effects into account, using the Hamilton principle. Data on the link's properties, payload mass, and target angle are used as features to predict the deflection as output, based on numerical analysis of partial differential equation model. For the first time, a Deep Neural Network (DNN) is proposed and trained offline to predict the static deflection of the payload in a set of VSLFMs, using the data obtained from the numerical analysis. Utilizing the predicted deflection, a modified PID controller is developed to control the arc position of the payload. This controller does not require deflection feedback, making it ideal for industrial applications with limited sensors. The method is experimentally validated using an SLFM, which is validated based on a ground truth system consisting of inertial measurement unit-based sensor network.

关键词： Technological innovation Numerical analysis Artificial neural networks Mathematical models Energy efficiency Sensors Numerical models Manipulator dynamics Payloads Gravity

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Galaxy Morphology Semantic Segmentation Based on K-Net

Galaxy Morphology Semantic Segmentation Based on K-Net

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Tengyu Li Tao Hu Dashu Zhang Songtian Yu Wentao Li School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

At present, modern large-scale astronomical observation equipment can collect a large number of high-resolution images. Astronomy has entered the era of big data. Deep learning has shown great potential in astronomical image analysis due to its powerful feature extraction and segmentation capabilities. In the study of galaxy evolution, image segmentation can separate different parts of galaxies, help identify the types of galaxies and analyze structural details, which is crucial to understanding the formation and evolution of galaxies. Therefore, this paper proposes a galaxy morphology segmentation method based on K-Net. The experimental data comes from the Galaxy Zoo project, and the model is trained using strategies such as transfer learning. The trained model can be used to segment four common galaxy images. The experimental results show that the overall accuracy reaches 97.11%, and the mean intersection over union is also 80.87%, indicating that the model performs well in the segmentation of astronomical images. The mean accuracy and the mean dice coefficient are both higher than 87%, indicating that the model is stable in distinguishing different categories. Therefore, K-Net has great application potential in astronomical image segmentation tasks.

关键词： Training Adaptation models Accuracy Spirals Semantic segmentation Transfer learning Morphology Stars Training data Data models

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