IET Cybersystems and Robotics最新文献_第5页

Triangular lattice formation in robot swarms with minimal local sensing 具有最小局部感知的机器人群体中的三角形晶格形成

IET Cybersystems and Robotics Pub Date : 2023-04-13 DOI: 10.1049/csy2.12087

Zisen Nie, Qingrui Zhang, Xiaohan Wang, Fakui Wang, Tianjiang Hu

{"title":"Triangular lattice formation in robot swarms with minimal local sensing","authors":"Zisen Nie, Qingrui Zhang, Xiaohan Wang, Fakui Wang, Tianjiang Hu","doi":"10.1049/csy2.12087","DOIUrl":"10.1049/csy2.12087","url":null,"abstract":"The problem of triangular lattice formation in robot swarms has been investigated extensively in the literature, but the existing algorithms can hardly keep comparative performance from swarm simulation to real multi-robot scenarios, due to the limited computation power or the restricted field of view (FOV) of robot sensors. Eventually, a distributed solution for triangular lattice formation in robot swarms with minimal sensing and computation is proposed and developed in this study. Each robot is equipped with a sensor with a limited FOV providing only a ternary digit of information about its neighbouring environment. At each time step, the motion command is directly determined by using only the ternary sensing result. The circular motions with a certain level of randomness lead the robot swarms to stable triangular lattice formation with high quality and robustness. Extensive numerical simulations and multi-robot experiments are conducted. The results have demonstrated and validated the efficiency of the proposed approach. The minimised sensing and computation requirements pave the way for massive deployment at a low cost and implementation within swarms of miniature robots.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45620616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-shot object detection via class encoding and multi-target decoding 基于类编码和多目标解码的少镜头目标检测

IET Cybersystems and Robotics Pub Date : 2023-04-11 DOI: 10.1049/csy2.12088

Xueqiang Guo, Hanqing Yang, Mohan Wei, Xiaotong Ye, Yu Zhang

{"title":"Few-shot object detection via class encoding and multi-target decoding","authors":"Xueqiang Guo, Hanqing Yang, Mohan Wei, Xiaotong Ye, Yu Zhang","doi":"10.1049/csy2.12088","DOIUrl":"10.1049/csy2.12088","url":null,"abstract":"The task of few-shot object detection is to classify and locate objects through a few annotated samples. Although many studies have tried to solve this problem, the results are still not satisfactory. Recent studies have found that the class margin significantly impacts the classification and representation of the targets to be detected. Most methods use the loss function to balance the class margin, but the results show that the loss-based methods only have a tiny improvement on the few-shot object detection problem. In this study, the authors propose a class encoding method based on the transformer to balance the class margin, which can make the model pay more attention to the essential information of the features, thus increasing the recognition ability of the sample. Besides, the authors propose a multi-target decoding method to aggregate RoI vectors generated from multi-target images with multiple support vectors, which can significantly improve the detection ability of the detector for multi-target images. Experiments on Pascal visual object classes (VOC) and Microsoft Common Objects in Context datasets show that our proposed Few-Shot Object Detection via Class Encoding and Multi-Target Decoding significantly improves upon baseline detectors (average accuracy improvement is up to 10.8% on VOC and 2.1% on COCO), achieving competitive performance. In general, we propose a new way to regulate the class margin between support set vectors and a way of feature aggregation for images containing multiple objects and achieve remarkable results. Our method is implemented on mmfewshot, and the code will be available later.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43372924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Robust model predictive tracking control for the wheeled mobile robot with boundary uncertain based on linear matrix inequalities 基于线性矩阵不等式的边界不确定轮式移动机器人鲁棒模型预测跟踪控制

IET Cybersystems and Robotics Pub Date : 2023-03-26 DOI: 10.1049/csy2.12086

Xing Gao, Xin Su, Aimin An, Haochen Zhang

引用次数: 0

SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet-of-Things SOPA - GA - CNN:基于卷积神经网络块的遗传算法的参数和架构同步优化，以确保工业物联网的安全

IET Cybersystems and Robotics Pub Date : 2023-03-24 DOI: 10.1049/csy2.12085

Jia-Cheng Huang, Guo-Qiang Zeng, Guang-Gang Geng, Jian Weng, Kang-Di Lu

{"title":"SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet-of-Things","authors":"Jia-Cheng Huang, Guo-Qiang Zeng, Guang-Gang Geng, Jian Weng, Kang-Di Lu","doi":"10.1049/csy2.12085","DOIUrl":"10.1049/csy2.12085","url":null,"abstract":"In recent years, deep learning has been applied to a variety of scenarios in Industrial Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the designers. The authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT. A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks (SOPA-GA-CNN) is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks (CNNs) by genetic algorithms (GA) for the intrusion detection issue of IIoT. An efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters, including batch size, learning rate, weight optimiser and weight regularisation, and the architectures, such as the block-based network topology and the parameters of each CNN block. The experimental results on five intrusion detection datasets in IIoT, including secure water treatment, water distribution, Gas Pipeline, Botnet in Internet of Things and Power System Attack Dataset, have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy, precision, recall, F1-score, and the number of parameters of the deep learning models.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46597324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Solving multiple travelling salesman problem through deep convolutional neural network 利用深度卷积神经网络求解多重旅行商问题

IET Cybersystems and Robotics Pub Date : 2023-03-22 DOI: 10.1049/csy2.12084

Zhengxuan Ling, Yueling Zhou, Yu Zhang

{"title":"Solving multiple travelling salesman problem through deep convolutional neural network","authors":"Zhengxuan Ling, Yueling Zhou, Yu Zhang","doi":"10.1049/csy2.12084","DOIUrl":"10.1049/csy2.12084","url":null,"abstract":"The multiple travelling salesman problem (mTSP) is a classical optimisation problem that is widely applied in various fields. Although the mTSP was solved using both classical algorithms and artificial neural networks, reiteration is inevitable for these methods when presented with new samples. To meet the online and high-speed logistics requirements deploying new information technology, the iterative algorithm may not be reliable and timely. In this study, a deep convolutional neural network (DCNN)-based solution method for mTSP is proposed, which can establish the mapping between the parameters and the optimal solutions directly and avoid the use of iterations. To facilitate the DCNN in establishing a mapping, an image representation that can transfer the mTSP from an optimisation problem into a computer vision problem is presented. While maintaining the excellent quality of the results, the efficiency of the solution achieved by the proposed method is much higher than that of the traditional optimisation method after training. Meanwhile, the method can be applied to solve the mTSP under different constraints after transfer learning.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48095183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A novel distributed architecture for unmanned aircraft systems based on Robot Operating System 2 一种基于机器人操作系统2的新型分布式无人飞行器系统结构

IET Cybersystems and Robotics Pub Date : 2023-03-02 DOI: 10.1049/csy2.12083

Lorenzo Bianchi, Daniele Carnevale, Fabio Del Frate, Roberto Masocco, Simone Mattogno, Fabrizio Romanelli, Alessandro Tenaglia

{"title":"A novel distributed architecture for unmanned aircraft systems based on Robot Operating System 2","authors":"Lorenzo Bianchi, Daniele Carnevale, Fabio Del Frate, Roberto Masocco, Simone Mattogno, Fabrizio Romanelli, Alessandro Tenaglia","doi":"10.1049/csy2.12083","DOIUrl":"10.1049/csy2.12083","url":null,"abstract":"A novel distributed control architecture for unmanned aircraft system (UASs) based on the new Robot Operating System (ROS) 2 middleware is proposed, endowed with industrial-grade tools that establish a novel standard for high-reliability distributed systems. The architecture has been developed for an autonomous quadcopter to design an inclusive solution ranging from low-level sensor management and soft real-time operating system setup and tuning to perception, exploration, and navigation modules orchestrated by a finite-state machine. The architecture proposed in this study builds on ROS 2 with its scalability and soft real-time communication functionalities, while including security and safety features, optimised implementations of localisation algorithms, and integrating an innovative and flexible path planner for UASs. Finally, experimental results have been collected during tests carried out both in the laboratory and in a realistic environment, showing the effectiveness of the proposed architecture in terms of reliability, scalability, and flexibility.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48897911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robust state estimation for uncertain linear discrete systems with d-step measurement delay and deterministic input signals 具有d步测量延迟和确定性输入信号的不确定线性离散系统的鲁棒状态估计

IET Cybersystems and Robotics Pub Date : 2023-02-20 DOI: 10.1049/csy2.12080

Yu Tian, Fanli Meng, Yao Mao, Junwei Gao, Huabo Liu

引用次数: 0

3D semantic map construction based on point cloud and image fusion 基于点云和图像融合的三维语义地图构建

IET Cybersystems and Robotics Pub Date : 2023-02-16 DOI: 10.1049/csy2.12078

Huijun Li, Hailong Zhao, Bin Ye, Yu Zhang

{"title":"3D semantic map construction based on point cloud and image fusion","authors":"Huijun Li, Hailong Zhao, Bin Ye, Yu Zhang","doi":"10.1049/csy2.12078","DOIUrl":"10.1049/csy2.12078","url":null,"abstract":"Accurate and robust positioning and mapping are the core functions of autonomous mobile robots, and the ability to analyse and understand scenes is also an important criterion for the intelligence of autonomous mobile robots. In the outdoor environment, most robots rely on GPS positioning. When the signal is weak, the positioning error will interfere with the mapping results, making the semantic map construction less robust. This research mainly designs a semantic map construction system that does not rely on GPS signals for large outdoor scenes. It mainly designs a feature extraction scheme based on the sampling characteristics of Livox-AVIA solid-state LiDAR. The factor graph optimisation model of frame pose and inertial measurement unit (IMU) pre-integrated pose, using a sliding window to fuse solid-state LiDAR and IMU data, fuse laser inertial odometry and camera target detection results, refer to the closest point distance and curvature for semantic information. The point cloud is used for semantic segmentation to realise the construction of a 3D semantic map in outdoor scenes. The experiment verifies that laser inertial navigation odometry based on factor map optimisation has better positioning accuracy and lower overall cumulative error at turning, and the 3D semantic map obtained on this basis performs well.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45538935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Drift-free localisation using prior cross-source map for indoor low-light environments 使用室内低光环境的先前交叉源地图进行无漂移定位

IET Cybersystems and Robotics Pub Date : 2023-02-16 DOI: 10.1049/csy2.12081

Junyi Tao, Weichen Dai, Da Kong, Jiayan Wan, Bin He, Yu Zhang

引用次数: 0

Impedance learning adaptive super-twisting control of a robotic exoskeleton for physical human-robot interaction 用于物理人机交互的机器人外骨骼的阻抗学习自适应超扭曲控制

IET Cybersystems and Robotics Pub Date : 2023-02-16 DOI: 10.1049/csy2.12077

Brahim Brahmi, Mohammad Habibur Rahman, Maarouf Saad

{"title":"Impedance learning adaptive super-twisting control of a robotic exoskeleton for physical human-robot interaction","authors":"Brahim Brahmi, Mohammad Habibur Rahman, Maarouf Saad","doi":"10.1049/csy2.12077","DOIUrl":"10.1049/csy2.12077","url":null,"abstract":"This study addresses two issues about the interaction of the upper limb rehabilitation robot with individuals who have disabilities. The first step is to estimate the human's target position (also known as TPH). The second step is to develop a robust adaptive impedance control mechanism. A novel Non-singular Terminal Sliding Mode Control combined with an adaptive super-twisting controller is being developed to achieve this goal. This combination's purpose is to provide high reliability, continuous performance tracking of the system's trajectories. The proposed adaptive control strategy reduces matched dynamic uncertainty while also lowering chattering, which is the sliding mode's most glaring issue. The proposed TPH is coupled with adaptive impedance control with the use of a Radial Basis Function Neural Network, which allows a robotic exoskeleton to simply track the desired impedance model. To validate the approach in real-time, an exoskeleton robot was deployed in controlled experimental circumstances. A comparison study has been set up to show how the adaptive impedance approach proposed is better than other traditional controllers.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.12077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47399228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2