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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Ziqi Gao Yingli Li Yang Luo Yunxiang Jiang Bi Zhang Xingang Zhao Lingkai Chen Yiwen Zhao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China

The accuracy of TCP (tool center point) calibration is vital to the operation accuracy of the robot. In this paper, we analyzed the accuracy analysis of TCP calibration of the regression matrix via condition number and minimum eigenvalue. It is theoretically proved that a regression matrix with a large condition number and a small eigenvalue will aggravate the influence of the observation error of the measurement point on TCP calibration accuracy and increase the TCP calibration error. To verify our analysis, simulation calibration experiments were carried out and statistical analysis was carried out on the calibration results. The result shows that the mathematical expectation and the variance of calibration error at minimum, median, and maximum matrix CA condition number are 1.36mm (±0.60mm), 1.57mm (± 0.75mm) and 2.94mm (±2.26mm) respectively, and the mathematical expectation and the variance of calibration error at minimum, median, and maximum eigenvalue are 3.33mm (± 2.32mm), 1.54mm (±0.72mm) and 1.16mm (± 0.49mm), respectively.

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Meta-Learning-Based Optimal Control for Soft Robotic Manipulators to Interact with Unknown Environments

Meta-Learning-Based Optimal Control for Soft Robotic Manipul...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zhiqiang Tang Peiyi Wang Wenci Xin Zhexin Xie Longxin Kan Muralidharan Mohanakrishnan Cecilia Laschi Department of Mechanical Engineering National University of Singapore Singapore Robotics Research Center Beijing Jiaotong University Beijing China

Safe and efficient robot-environment interaction is a critical but challenging problem as robots are being increasingly employed to operate in unstructured and unpredictable environments. Soft robots are inherently compliant to safely interact with environments but their high nonlinearity exacerbates control difficulties. Meta-learning provides a powerful tool for fast online model adaptation because it can learn an efficient model from data across different environments. Thus, this work applies the idea of meta-learning for the control of soft robotics. In particular, a target-oriented proactive search strategy is firstly performed to collect environment-specific data efficiently when a new interaction environment occurs. Then meta-learning exploits past experience to train a data-driven probabilistic model prior, and the model prior is online updated to be fast adapted to the new environment. Lastly, a model-based optimal control policy is utilized to drive the robot to desired performance. Our approach controls a soft robotic manipulator to achieve the desired position and contact force simultaneously when interacting with unknown changing environments. Overall, this work provides a viable control approach for soft robots to interact with unknown environments.

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Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

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Computer Systems Science & engineering 2023年第12期47卷 2989-3010页

作者： Chia-Wei Jan Yu-Jhih Chiu Kuan-Lin Chen Ting-Chun Yao Ping-Huan Kuo Department of Mechanical Engineering National Chung Cheng UniversityChiayi62102Taiwan Department of Intelligent Robotics National Pingtung UniversityPingtung900392Taiwan School of Medicine College of MedicineTaipei Medical UniversityTaipei CityTaiwan Advanced Institute of Manufacturing with High-Tech Innovations(AIM-HI) National Chung Cheng UniversityChiayi62102Taiwan

Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory ***,hospital medical resources are limited,and sometimes the workload of physicians is too high,which can affect their ***,a good medical assistance system is of great significance for improving the quality of medical *** study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping(Grad-CAM).Pneumonia is a common lung disease that is generally diagnosed using ***,in areaswith limited medical resources,a shortage of medical personnel may result in delayed diagnosis and treatment during the critical ***,overworked physicians may make diagnostic ***,having an X-ray pneumonia diagnosis assistance system is a significant tool for improving the quality of medical *** result indicates that the best results were obtained by a ResNet50 pretrained model combined with a fully connected classification layer.A retraining procedure was designed to improve accuracy by using gradient-weighted class activation mapping(Grad-CAM),which detects the misclassified images and adds weights to *** the evaluation tests,the final combined model is named Grad-CAM Based Pneumonia Network(GCPNet)out performed its counterparts in terms of accuracy,precision,and F1 score and reached 97.2%*** integrated system is proposed to increase model performance where Grad-CAM and transfer learning are ***-CAM is used to generate the heatmap,which shows the region that the model is focusing *** outcomes of this research can aid in diagnosing pneumonia symptoms,as themodel can accurately classify chest X-ray images,and the heatmap can assist doctors in observing the crucial areas.

关键词： Data augmentation deep neural network image classification medical imaging transfer learning Grad-CAM

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Model Predictive Control with Model Error Compensation by Koopman Approach 48

Model Predictive Control with Model Error Compensation by Ko...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Kanai, Masaki Yamakita, Masaki R&d Group Hitachi Ltd. Controls and Robotics Innovation Center Ibaraki Japan Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This study proposes a method for high-performance model predictive control (MPC) by using a lifted bilinear model error model (MEM) based on the Koopman approach to compensate for errors in the prediction model. In MPC, more accurate prediction models can be expected to provide better control performance for the real plant. However, there often exist modeling errors in the prediction model, which may prevent MPC from achieving good performance. In this study, we identify the errors in the nominal model as an MEM, and formulate an MPC that integrates the nominal model and the MEM into an "error-compensated model"as a prediction model. We model the MEM with a lifted bilinear model that can represent nonlinear characteristics based on Koopman's theory. In addition, an efficient linear MPC algorithm is constructed by linearizing the MEM while maintaining high prediction accuracy. Numerical simulations demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Model predictive control

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Empirical Line-of-Sight Probability Modeling for UAVs in Random Urban Layouts

Empirical Line-of-Sight Probability Modeling for UAVs in Ran...

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IEEE Conference on Wireless Communications and Networking

作者： Abdul Saboor Zhuangzhuang Cui Evgenii Vinogradov Sofie Pollin Department of Electrical Engineering (ESAT) KU Leuven WaveCoRE Leuven Belgium Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi UAE

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Accurate Probability of Line-of-Sight $(P_{\text{LoS}})$ modeling is important in evaluating the performance of Unmanned Aerial Vehicle (UAV)-based communication systems in urban environments, where real-time communication and low latency are often major requirements. Existing $P_{L o S}$ models often rely on simplified Manhattan grid layouts using International Telecommunication Union (ITU)-defined builtup parameters, which may not reflect the randomness of real cities. Therefore, this paper introduces the Urban Line-ofSight Simulator (ULS) to model $P_{\text{LoS}}$ for three random city layouts with varying building sizes and shapes constructed using ITU built-up parameters. Based on the ULS simulated data, we obtained the empirical $P_{L o S}$ for four standard urban environments across three different city layouts. Finally, we analyze how well Manhattan grid-based models replicate $P_{L o S}$ results from random and real-world layouts, providing insights into their applicability for time-critical communication systems in urban IoT networks.

关键词： Wireless communication Analytical models Shape Layout Urban areas Buildings Line-of-sight propagation Autonomous aerial vehicles ITU Standards

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Blockchain Technology and Artificial Intelligence's Future Applications in Cyber Security 3

Blockchain Technology and Artificial Intelligence's Future A...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Gupta, Richa Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applications, such as healthcare, a bottleneck develops. This issue would be resolved by blockchain's decentralized database architecture, safe data storage, data exchange, and authentication. Additionally, AI can be present at the very top of the blockchain and produce insights from the shared data that is created and applied to forecasts. Blockchain is a cutting-edge Cybersecurity technology that creates chains by mutual consent between nodes that chronologically link new blocks to those previously stored in nodes. A number of industries, including banking, insurance, cybersecurity, forecasting, healthcare, and cryptocurrencies, among others, are growing more quickly as a result of technology convergence. The likelihood of these systems being hacked increases as more digital technologies are used and these businesses provide services. Blockchain technology and artificial intelligence can work together to create a strong defense against these dangers and security problems. We'll examine how blockchain and AI are merged in cybersecurity in this chapter. We'll go into more detail about how they help secure cyber-physical systems. © 2023 IEEE.

关键词： Blockchain

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Logarithmic Sliding-Mode Control for High-Precision Rigid-Body Attitude Tracking 49

Logarithmic Sliding-Mode Control for High-Precision Rigid-Bo...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Dong, Hanlin Yang, Xuebo Chen, Zhongbo Liu, Xiyao School of Automation Northwestern Polytechnical University Xi'an China Research Institute of Intelligent Control Systems Harbin Institute of Technology Harbin China College of Control Science and Engineering Bohai University Jinzhou China Northwestern Polytechnical University Research Center for Intelligent Robotics Xi'an China

ISBN: (纸本)9798350331820

The problem of high-precision attitude tracking is studied in this paper under model uncertainty and external disturbance risk. First of all, the kinematics and dynamics model of rigid body is derived based on the quaternion theory. Then, a standard logarithmic sliding-mode function is constructed using the attitude quaternion and its derivatives of the rigid spacecraft, and the corresponding logarithmic sliding-mode controller is also derived. Finally, the stability of the controlled closed-loop system is demonstrated. Consistenting with theoretical proof, the numerical simulation results showed that the controlled rigid body can quickly track its desired attitude command, even if there are model uncertainty and external disturbance risks. © 2023 IEEE.

关键词： Disturbance rejection

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Autonomous inverse encoding guides 4D nanoprinting for highly programmable shape morphing

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International Journal of Extreme Manufacturing 2025年第3期7卷 467-482页

作者： Shuaiqi Ren Zhiang Zhang Ruokun He Jiahao Fan Guangming Wang Hesheng Wang Bing Han Yong-Lai Zhang Zhuo-Chen Ma Department of Automation Shanghai Jiao Tong UniversityShanghai 200240People’s Republic of China Institute of Medical Robotics School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012People’s Republic of China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240People’s Republic of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240People’s Republic of China Department of Engineering University of CambridgeCambridge CB21PZUnited Kingdom

Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent ***,due to the lack of autonomous holistic strategies throughout the target shape input,optimal material distribution generation,and fabrication program output,4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual *** this study,we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape *** tuning the laser power of each voxel at the nanoscale,the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding *** this strategy,the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under ***,as a proof-of-concept,a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial *** strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting,leading to cutting-edge applications in microfluidics,micro-robotics,minimally invasive robotic surgery,and tissue engineering.

关键词： femtosecond laser fabrication 4D printing two-photon polymerization autonomous inverse encoding stimuli-responsive materials

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Crime Prediction by Detecting Violent Objects and Activity Using Pre-Trained YOLOv8n and MoViNet A0 Models

Crime Prediction by Detecting Violent Objects and Activity U...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Islam, Md. Rahatul Rokon Uz Zaman, A.B.M. Ferdib-Al-Islam Tabassum, Faria Israk, Md. Araf Mahmud, Md. Shakir Majumder, Jimmy Kyushu Institute of Technology Fukuoka Japan Bangladesh Advance Robotics Research Center Dhaka Bangladesh Northern University of Business and Technology Khulna Khulna Bangladesh Bangladesh University of Engineering and Technology Dhaka Bangladesh Canadian University of Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798350369359

Crime prediction is crucial to contemporary law enforcement and policies ensuring public safety. This study introduces a novel method for crime prediction by using deep learning. Specifically, the methodology involves the detection of violent objects and activities via the use of pre-trained YOLOv8n and MoViNet a0 models. To achieve the goal, two deep learning models have been considered. YOLOv8n, the initial model, has been fine-tuned for violent object detection using a weapon detection dataset. The second model, MoViNet a0 base, has been fine-tuned for violence activity detection on a hockey battle dataset. This paper explores the fusion of these models to enhance crime prediction by identifying violent objects and activities in real-world scenarios. With an accuracy of 91.7% for recognizing violent objects and 94% for detecting violent activity, the suggested approach provides an efficient solution for crime prediction by detecting violent objects and activity. Experimental findings reveal that the models can reliably recognize violent items and behaviours. Through object detection and action recognition, this technique provides law enforcement and security professionals with a comprehensive view of possible risks, enabling proactive crime prevention. © 2023 IEEE.

关键词： Object detection

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Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps

arXiv

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arXiv 2025年

作者： Gupta, Saurabh Guadagnino, Tiziano Mersch, Benedikt Trekel, Niklas Malladi, Meher V.R. Stachniss, Cyrill Center for Robotics University of Bonn Germany Department of Engineering Science University of Oxford United Kingdom Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Consistent maps are key for most autonomous mobile robots. They often use SLAM approaches to build such maps. Loop closures via place recognition help maintain accurate pose estimates by mitigating global drift. This paper presents a robust loop closure detection pipeline for outdoor SLAM with LiDAR-equipped robots. The method handles various LiDAR sensors with different scanning patterns, field of views and resolutions. It generates local maps from LiDAR scans and aligns them using a ground alignment module to handle both planar and non-planar motion of the LiDAR, ensuring applicability across platforms. The method uses density-preserving bird’s eye view projections of these local maps and extracts ORB feature descriptors from them for place recognition. It stores the feature descriptors in a binary search tree for efficient retrieval, and self-similarity pruning addresses perceptual aliasing in repetitive environments. Extensive experiments on public and self-recorded datasets demonstrate accurate loop closure detection, long-term localization, and cross-platform multi-map alignment, agnostic to the LiDAR scanning patterns, fields of view, and motion profiles. Copyright © 2025, The Authors. All rights reserved.

关键词： SLAM robotics

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