IET Cybersystems and Robotics最新文献_第4页

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-07

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Hybrid-Linear Inverted Pendulum Gait Planning With H-Infinity Uncertainty Compensation for Robust Biped Robot Walking 鲁棒双足机器人h -∞不确定性补偿的混合-线性倒摆步态规划

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-07 DOI: 10.1049/csy2.70042

Jia Li, Yan Liu

{"title":"Hybrid-Linear Inverted Pendulum Gait Planning With H-Infinity Uncertainty Compensation for Robust Biped Robot Walking","authors":"Jia Li, Yan Liu","doi":"10.1049/csy2.70042","DOIUrl":"https://doi.org/10.1049/csy2.70042","url":null,"abstract":"Reduced-order models (ROMs) are widely employed in biped robot control due to their computational efficiency, but their simplified representations often neglect critical nonlinear dynamics, leading to limited robustness under real-world disturbances. To overcome this limitation, this paper introduces a robust hierarchical control framework that explicitly compensates for unmodelled dynamics and provides theoretical stability guarantees. The proposed architecture consists of two layers. At the high level, a hybrid-linear inverted pendulum (HLIP) model generates real-time gait commands, whereas an <math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mi>∞</mi>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${H}_{infty }$</annotation>\u0000 </semantics></math> feedback law accounts for dynamic uncertainties introduced by model simplification. A Lyapunov-based analysis is used to rigorously establish the stability of each planned foothold. At the low level, a whole-body controller tracks both swing-leg and centre-of-mass trajectories by solving a quadratic programme that maps task-space accelerations to joint torques. The framework is validated in simulation and hardware experiments on the BRUCE platform. On flat ground, BRUCE maintains a steady walking speed of 0.3 m/s. When confronted with uneven terrain—simulated by randomly distributed 2.5-cm planks as unmodelled disturbances—the robot preserves balance and velocity tracking. Comparative evaluations against the divergent component of motion (DCM) and reinforcement learning (RL)-based methods demonstrate superior velocity tracking performance of the proposed approach, confirming its ability to reconcile the computational tractability of ROMs with the robustness missing in many traditional and learning-based controllers.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147626357","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}

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IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-05

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Single-Shot Initial Mutual Localization for Micro Aerial Swarms 微型空中蜂群的单次初始相互定位

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-05 DOI: 10.1049/csy2.70043

Xiangyong Wen, Yunfeng Li, Tianyu Zhao, Chao Xu, Fei Gao

{"title":"Single-Shot Initial Mutual Localization for Micro Aerial Swarms","authors":"Xiangyong Wen, Yunfeng Li, Tianyu Zhao, Chao Xu, Fei Gao","doi":"10.1049/csy2.70043","DOIUrl":"https://doi.org/10.1049/csy2.70043","url":null,"abstract":"Initial mutual localization of micro aerial swarms remains a challenging problem and is essential for establishing a common reference frame before coordinated flight. This task is difficult due to the limited scene overlap between nonadjacent drones, as well as sparse and low-quality feature correspondences in environments with insufficient structural texture. To address these challenges, we propose a single-shot dual-view pose estimation method that performs initial mutual localization using a single-view capture from each camera, without relying on multi-view information. This method formulates feature matching and relative pose estimation within a unified optimization framework, which suppresses outliers and low-quality matches while enabling the recovery of a larger set of high-quality correspondences under the same visual conditions. As a result, the proposed approach significantly improves pose estimation accuracy and robustness. Extensive benchmark evaluations demonstrate that our method consistently outperforms the standard dual-view pipeline based on feature matching and pose estimation across diverse scenarios, with particularly strong performance in challenging field environments. We further validate the proposed initial mutual localization method on a real micro aerial swarm platform, and release an open-source implementation (https://github.com/lyf-FATAS/rpe) for reproducibility and future research.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147618064","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}

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Research on a Dual-Mode Human–Robot Interaction Method Based on a Large Language Model 基于大语言模型的双模人机交互方法研究

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-01-08 DOI: 10.1049/csy2.70037

Jingjing Guo, Xi Han

{"title":"Research on a Dual-Mode Human–Robot Interaction Method Based on a Large Language Model","authors":"Jingjing Guo, Xi Han","doi":"10.1049/csy2.70037","DOIUrl":"https://doi.org/10.1049/csy2.70037","url":null,"abstract":"Current guide robot systems have two main issues: (1) they only support a single mode of interaction (proactive or reactive) and lack a coordination mechanism and (2) they rely heavily on predefined content, which hinders the realisation of a natural and flexible human-like interaction experience. To address these issues, this paper proposes a dual-mode human–robot interaction (HRI) method based on a large language model (LLM). This method includes the following: (1) proactive interaction module. This module uses the robot's own sensors to perceive environmental information in real time, enabling it to provide various human-like services, such as safety alerts, situational announcements, and personalised recommendations. (2) Reactive interaction module. This integrates a query router with retrieval-augmented generation (RAG) method to build an adaptive response mechanism, which aims to provide more accurate responses while optimising response efficiency. Validation in guided tour scenarios confirms the efficiency of the proposed method. Results demonstrate that the proposed method achieves a 92% F1-score (improving 8 percentage points [PPs] over pure LLM and 6 PPs over traditional RAG), has a 48.4% improvement in response latency compared to the standard retrieval-cosine method (the fastest baseline among static RAG approaches) and achieves higher Likert-scale ratings in naturalness (4.35), intelligence (4.05), dependability (4.48) and stimulation (4.45) than other evaluated methods. This study proposes a scalable technical pathway for advancing human–robot interaction systems towards more natural and anthropomorphic interaction paradigms.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969782","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 : 2026-01-08

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Tiny Foreign Object Debris Detection Considering Multi-Channel Information Fusion and Gaussian Distribution of Pixels 基于多通道信息融合和高斯分布的微小异物碎片检测

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-12-31 DOI: 10.1049/csy2.70039

Zhicong Lu, Guoliang Liu, Changteng Shi, Yichao Cao, Dongxuan Li, Guohui Tian

{"title":"Tiny Foreign Object Debris Detection Considering Multi-Channel Information Fusion and Gaussian Distribution of Pixels","authors":"Zhicong Lu, Guoliang Liu, Changteng Shi, Yichao Cao, Dongxuan Li, Guohui Tian","doi":"10.1049/csy2.70039","DOIUrl":"https://doi.org/10.1049/csy2.70039","url":null,"abstract":"Foreign object debris (FOD) detection is critical to aircraft safety, but existing visual algorithms have difficulty in detecting tiny objects and in low-light conditions. FOD detection in low-light conditions can be achieved using laser line-scan cameras, but there is still a lot of room for research on how to better use the multi-channel images obtained by the camera. To address these issues, this paper proposes a tiny FOD detection algorithm (TFD-Net) suitable for laser line-scan cameras and visible light cameras, along with a new multi-channel information fusion (NMIF) method based on laser line-scan camera image features. The proposed TFD-Net is designed specifically for tiny FOD with three key parts: a loss function based on two-dimensional (2D) Gaussian distribution, a multi-scale detection head and an improved pooling module. These designs can effectively extract tiny FOD features and achieve high-precision detection. The proposed NMIF makes better use of the three-channel image features acquired by the laser line-scan camera, improving the effectiveness of the laser line-scan camera in FOD detection significantly.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145887935","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}

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IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-12-31

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IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-11-30

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Slip Detection and Stable Grasping With Multi-Fingered Robotic Hand Using Deep Learning Approach 基于深度学习方法的多指机械手滑移检测与稳定抓取

IF 1.2

IET Cybersystems and Robotics Pub Date : 2025-11-30 DOI: 10.1049/csy2.70036

Haoliang Xu, Syed Muhammad Nashit Arshad, Shichi Peng, Han Xu, Hang Yin, Qiang Li

{"title":"Slip Detection and Stable Grasping With Multi-Fingered Robotic Hand Using Deep Learning Approach","authors":"Haoliang Xu, Syed Muhammad Nashit Arshad, Shichi Peng, Han Xu, Hang Yin, Qiang Li","doi":"10.1049/csy2.70036","DOIUrl":"10.1049/csy2.70036","url":null,"abstract":"Dexterous robotic hands are essential for various tasks in dynamic environments, but challenges such as slip detection and grasp stability affect real-time performance. Traditional grasping methods often fail to detect subtle slip events, leading to unstable grasps. This paper proposes a real-time slip detection and force compensation system using a hybrid convolutional neural networks and long short-term memory (CNN-LSTM) architecture to detect slip to enhance grasp stability. The system combines tactile sensing with deep learning to detect slips and dynamically adjust individual finger grasping forces, ensuring precise and stable object grasping. The proposed system leverages a hybrid CNN-LSTM architecture to effectively capture both spatial and temporal features of slip dynamics, enabling robust slip detection and grasp stabilisation. By employing data augmentation techniques, the system generates a comprehensive dataset from limited experimental data, enhancing training efficiency and model generalisation. The approach extends slip detection to individual fingers, allowing real-time monitoring and targeted force compensation when a slip is detected on a specific finger. This ensures adaptive and stable grasping, even in dynamic environments. Experimental results demonstrate significant improvements, with the CNN-LSTM model achieving an 82% grasp success rate, outperforming traditional CNN (70%), LSTM (72%), and only traditional proportional–integral–derivative PID (54%) methods. The system's real-time force adjustment capability prevents object drops and enhances overall grasp stability, making it highly scalable for applications in industrial automation, healthcare, and service robots. Despite the CNN-LSTM architecture being a well-established approach, it demonstrates exceptional performance in this task, achieving high accuracy and robustness in slip detection and grasp stabilisation.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686457","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