IET Cybersystems and Robotics最新文献_第8页

Hybrid Dynamic Point Removal and Ellipsoid Modelling of Object-Based Semantic SLAM 基于对象语义SLAM的混合动态点移除和椭球建模

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-08-21 DOI: 10.1049/csy2.70020

Qingyang Xu, Siwei Huang, Yong Song, Bao Pang, Chengjin Zhang

引用次数: 0

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-08-21

引用次数: 0

Dynamic Cooperative Optimisation With Differential Evolution for Trajectory Planning of Robotic Arms 基于差分进化的机械臂轨迹规划动态协同优化

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-07-18 DOI: 10.1049/csy2.70016

Yongzhe Luo, Zhenfeng Xue, Xu Song, Zhongyuan Miao, Yong Hu

{"title":"Dynamic Cooperative Optimisation With Differential Evolution for Trajectory Planning of Robotic Arms","authors":"Yongzhe Luo, Zhenfeng Xue, Xu Song, Zhongyuan Miao, Yong Hu","doi":"10.1049/csy2.70016","DOIUrl":"10.1049/csy2.70016","url":null,"abstract":"Trajectory planning of a robotic arm has a significant impact on its operational efficiency and success rate. However, due to the complexity of the environment and the vastness of the search space, it often ends up falling into local optima. In this paper, we propose a novel algorithm that combines particle swarm optimisation (PSO) with differential evolution (DE), namely the PSO-DE algorithm, to alleviate the problem. Firstly, the initial path of the robotic arm is represented by spline curves in the joint space. Then, the trajectory optimisation problem of the robotic arm is established, including constraints such as obstacle cost, acceleration cost, torque cost etc. Finally, the PSO-DE algorithm is proposed for optimisation, from which the PSO ensures the search space range through individual collaboration, whereas the DE generates new solutions through individual differentiation with local search. The combination of the two algorithms can fully leverage their respective advantages, ensuring the global optima within a large search space. Experiments are conducted in a simulation environment using the Python Robotics Toolbox and the PyBullet simulation platform. The results demonstrate that the proposed algorithm can effectively plan the trajectory of the robotic arm with significant improvements in success rates compared to the PSO algorithm.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657612","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

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-07-18

引用次数: 0

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-07-18

引用次数: 0

Multi-Robot Autonomous Exploration in Unknown Environments With Dynamic Obstacles 具有动态障碍物的未知环境下多机器人自主探索

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-07-18 DOI: 10.1049/csy2.70019

Jing Chu, Xiaodie Lv, Qi Yue, Yong Huang, Xueke Huangfu

{"title":"Multi-Robot Autonomous Exploration in Unknown Environments With Dynamic Obstacles","authors":"Jing Chu, Xiaodie Lv, Qi Yue, Yong Huang, Xueke Huangfu","doi":"10.1049/csy2.70019","DOIUrl":"10.1049/csy2.70019","url":null,"abstract":"Exploring unknown environments by multiple robots is promising but challenging. The challenges are posed not only by the coordination among multiple robots to improve exploration efficiency, but also by dynamic obstacles that suddenly appear on planned paths. To address those two challenges, this paper proposes a two-layer architecture where the high-level layer generates target locations for each robot to explore the unknown environment, while the low-level layer plans paths in the dynamic environment for each robot. Specifically, in the high-level design, a novel auction algorithm is proposed, which considers both the distance of robots to target locations and the number of frontiers within the clustering domain of target locations. This approach enables robots to explore different target locations while reducing redundant exploration compared to traditional exploration algorithms. In the low-level design, a neural network-based Q-learning algorithm is employed for path planning to achieve dynamic obstacle avoidance. Robots can dynamically adjust their actions through interaction with the external environment, thus avoid obstacles and reach the target position. To validate our methods, a series of simulation experiments are conducted. The experimental results demonstrate that robots can not only efficiently accomplish exploration tasks in unknown environments, but also achieve effective obstacle avoidance when faced with suddenly appearing dynamic obstacles.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657611","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

An Automatic Sleep Apnoea Detection Method Based on Multi-Context-Scale CNN-LSTM and Contrastive Learning With ECG 基于多上下文尺度CNN-LSTM和心电对比学习的睡眠呼吸暂停自动检测方法

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-07-16 DOI: 10.1049/csy2.70017

Lijuan Duan, Zikang Song, Yourong Xu, Yanzhao Wang, Zhiling Zhao

{"title":"An Automatic Sleep Apnoea Detection Method Based on Multi-Context-Scale CNN-LSTM and Contrastive Learning With ECG","authors":"Lijuan Duan, Zikang Song, Yourong Xu, Yanzhao Wang, Zhiling Zhao","doi":"10.1049/csy2.70017","DOIUrl":"10.1049/csy2.70017","url":null,"abstract":"Obstructive sleep apnoea (OSA) is a prevalent condition that can lead to various cardiovascular and cerebrovascular diseases, such as coronary heart disease, hypertension, and stroke, posing significant health risks. Polysomnography (PSG) is widely regarded as the most reliable method for detecting sleep apnoea (SA), but it is limited by a complex acquisition process and high costs. To address these issues, some studies have explored the use of single-lead signals, although they often result in lower accuracy due to noise-related information loss. Time context information has been applied to mitigate this issue, but it can lead to overfitting and category confusion. This paper introduces a novel approach utilising time sequence contrastive learning with single-lead electrocardiogram (ECG) signals to detect SA events and assess OSA severity. The proposed method features a Transformer encoder fusion module and a contrastive classification module. First, a multi-branch architecture is employed to extract features from different time scales of the ECG signal, which aids in SA detection. To further enhance the network's focus on the most relevant extracted features, a channel attention mechanism is incorporated to fuse features from different branches. Finally, contrastive learning is utilised to constrain the features, resulting in improved detection performance. A series of experiments were conducted on a public dataset to validate the effectiveness of the proposed method. The method achieved an accuracy of 91.50%, a precision of 92.06%, a sensitivity of 94.37%, a specificity of 86.89%, and an F1 score of 93.20%, outperforming state-of-the-art detection techniques.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647348","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

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-07-16

引用次数: 0

Deep Reinforcement Learning for Localisability-Aware Mapless Navigation 基于可定位性感知的无地图导航深度强化学习

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-07-01 DOI: 10.1049/csy2.70018

Yan Gao, Jing Wu, Changyun Wei, Raphael Grech, Ze Ji

{"title":"Deep Reinforcement Learning for Localisability-Aware Mapless Navigation","authors":"Yan Gao, Jing Wu, Changyun Wei, Raphael Grech, Ze Ji","doi":"10.1049/csy2.70018","DOIUrl":"10.1049/csy2.70018","url":null,"abstract":"Mapless navigation refers to the task of searching for a collision free path without relying on a pre-defined map. Most current works of mapless navigation assume accurate ground-truth localisation is available. However, this is not true, especially for indoor environments, where simultaneous localisation and mapping (SLAM) is needed for location estimation, which highly relies on the richness of environment features. In this work, we propose a novel deep reinforcement learning (DRL) based mapless navigation method without relying on the assumption of the availability of localisation. Our method utilises RGB-D based Oriented FAST and Rotated BRIEF (ORB) SLAM2 for robot localisation. Our policy effectively guides the robot's movement towards the target while enhancing robot pose estimation by considering the quality of the observed features along the selected paths. To facilitate policy training, we propose a compact state representation based on the spatial distributions of map points, which enhances the robot's awareness of areas with reliable map points. Furthermore, we suggest incorporating the relative pose error into the reward function. In this way, the policy will be more responsive to each single action. In addition, rather than utilising a pre-set threshold, we adopt a dynamic threshold to improve the policy's adaptability to variations in SLAM performance across different environments. The experiments in localisation challenging environments have demonstrated the remarkable performance of our proposed method. It outperforms the related DRL based methods in terms of success rate.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524979","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

Visual Simultaneous Localization and Mapping for Highly Dynamic Environments 高度动态环境的视觉同步定位和映射

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-05-27 DOI: 10.1049/csy2.70014

Yuxin Zheng, Weichen Dai, Yu Zhang, Wenhao Guan, Chengfei Liu

引用次数: 0