IET Cybersystems and Robotics最新文献_第3页

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-04-07

引用次数: 0

An Articulated Continuum Robot for Turbine Blade Inspection With Kinematics Enabling Constraint-Aware Motion Planning and Execution 基于约束感知运动规划与执行的涡轮叶片检测关节连续机器人

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-04-07 DOI: 10.1049/csy2.70046

Caixia Cui, Jingyuan Wang, Haozhe Zhang, Haiyu Wu, Dan Wang, Xinge Miao, Chao Xu, Chunlin Zhou

{"title":"An Articulated Continuum Robot for Turbine Blade Inspection With Kinematics Enabling Constraint-Aware Motion Planning and Execution","authors":"Caixia Cui, Jingyuan Wang, Haozhe Zhang, Haiyu Wu, Dan Wang, Xinge Miao, Chao Xu, Chunlin Zhou","doi":"10.1049/csy2.70046","DOIUrl":"https://doi.org/10.1049/csy2.70046","url":null,"abstract":"In typical operational scenarios such as turbine blade inspection, robots are subject to complex spatial constraints and are prone to mechanism—environment interference, which restricts their motion and may even render them inoperative. To address this issue, this paper proposes a novel 10-degree-of-freedom (10-DOF) continuum robot configuration and, on this basis, develops a bounded nonlinear least-squares (NLS) inverse kinematics (IK) framework for precise motion control under joint limits and tube-shaped workspace constraints. Specifically, a tube-aware rapidly-exploring random tree connect (RRT-Connect) planner is first employed to compute a coarse joint-space path with edge-wise feasibility checking; then, sequential least squares programming (SLSQP) refines it into a smooth skeleton; finally, follow-the-leader (FTL) performs dense Cartesian micro-stepping via bounded NLS, while enforcing segmented virtual-tube soft constraints along link-sampled points. Simulation results in CoppeliaSim demonstrate that the proposed method generates smooth and safety-compliant trajectories in confined environments and robustly tracks the target blade edge curve, effectively mitigating branch jumping and orientation discontinuities. Quantitative metrics, including tracking root-mean-square error (RMSE), tube-margin, violation statistics, and per-step computation time, indicate favourable feasibility and stability. Overall, this work provides an effective joint solution for global–local trajectory planning and constrained IK of redundant continuum robots in strongly constrained cavity inspection tasks.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696352","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

A Reinforcement Learning-Based Nonlinear Trajectory Tracking Control Strategy for Space Flexible Manipulator With LuGre Friction Compensation 基于强化学习的LuGre摩擦补偿空间柔性机械臂非线性轨迹跟踪控制策略

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-21 DOI: 10.1049/csy2.70040

Yongquan Li, Bo Yu, Yanbo Jian, Yuwei Qin, Aimin An

{"title":"A Reinforcement Learning-Based Nonlinear Trajectory Tracking Control Strategy for Space Flexible Manipulator With LuGre Friction Compensation","authors":"Yongquan Li, Bo Yu, Yanbo Jian, Yuwei Qin, Aimin An","doi":"10.1049/csy2.70040","DOIUrl":"https://doi.org/10.1049/csy2.70040","url":null,"abstract":"High-precision trajectory tracking control of space flexible manipulator represents a significant research focus of contemporary research and poses great challenges in both academia and engineering. To address the issue of low control precision in space flexible manipulator, which arises from highly nonlinear dynamics in complex spacecraft environments, the LuGre friction model is incorporated into the dynamic equation to improve the accuracy of frictional dynamic behaviour modelling. Subsequently, a reinforcement learning-based sliding mode control (RL-SMC) method is developed to achieve precise approximation and compensation of uncertain nonlinearities within the space flexible manipulator system. The employed RL framework is based on the actor–critic architecture, where the actor neural network generates the control policy, whereas the critic neural network evaluates the policy and continuously provides feedback regarding the system state. This control method uses a radial basis function neural network (RBFNN) combined with the SMC to minimise approximation error. In complex space environments, the actor–critic framework enhances the approximation of nonlinear dynamics for a space flexible manipulator and facilitates more efficient adaptation to variations in system dynamics. In addition, joint angle output constraints are implemented to manage the restricted motion of the space flexible manipulator in confined workspaces, aiming to prevent collisions during operation and avoid structural damage. Finally, the stability of the closed-loop system is rigorously established using the Lyapunov stability theory. Numerical simulations demonstrate the efficacy of the proposed approach in improving both control precision and environmental adaptability of the space manipulator.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567496","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 : 2026-03-21

引用次数: 0

Unifying Hierarchical Sliding Mode Control and Control Barrier Function for Tilt Angle Constraint of a Ball-Balancing Robot 球平衡机器人倾斜角约束的统一层次滑模控制和控制屏障函数

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-11 DOI: 10.1049/csy2.70045

Thi Thuy Hang Nguyen, Duc Cuong Vu, Minh Duc Pham, Tung Lam Nguyen, Thi-Van-Anh Nguyen

{"title":"Unifying Hierarchical Sliding Mode Control and Control Barrier Function for Tilt Angle Constraint of a Ball-Balancing Robot","authors":"Thi Thuy Hang Nguyen, Duc Cuong Vu, Minh Duc Pham, Tung Lam Nguyen, Thi-Van-Anh Nguyen","doi":"10.1049/csy2.70045","DOIUrl":"https://doi.org/10.1049/csy2.70045","url":null,"abstract":"This paper presents a novel control methodology that combines control barrier functions (CBFs) and hierarchical sliding mode control (HSMC) for the ball-balancing robot. The motivation arises from the need to achieve stable balancing and position tracking while guaranteeing physical safety under tilt angle constraints. The proposed approach aims to achieve stability objectives, including position tracking and balancing, and ensure compliance with safety constraints defined as an invariant set. To ensure the satisfaction of these safety constraints, CBFs are employed. Based on the construction of a suitable CBF, a nonempty set of control signals satisfying the CBF-dependent inequality is given. The integration of CBFs and HSMC is facilitated through quadratic programming (QP), enabling the unification of stability objectives and safety constraints. The applied nominal control law is HSMC, an effective solution for the underactuated system. The safety constraint is considered to guarantee that the tilt angle of the body never exceeds a predetermined value. Simulation results demonstrate that the proposed controller maintains the deviation angle within safe bounds while achieving robust tracking performance. These findings confirm the potential of combining HSMC with CBFs to ensure both performance and safety, paving the way for future experimental validation on physical platforms.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653259","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

Industrial Inspection Robot With Large Configuration Space Based on Geometry-Changing Tracked Robot 基于变几何履带机器人的大构形空间工业检测机器人

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-11 DOI: 10.1049/csy2.70041

Ge Wan, Mengke Zhang, Yin He, Dehua Chen, Chao Xu, Jie Sun, Yanjun Cao

{"title":"Industrial Inspection Robot With Large Configuration Space Based on Geometry-Changing Tracked Robot","authors":"Ge Wan, Mengke Zhang, Yin He, Dehua Chen, Chao Xu, Jie Sun, Yanjun Cao","doi":"10.1049/csy2.70041","DOIUrl":"https://doi.org/10.1049/csy2.70041","url":null,"abstract":"With the increasing demand for automated inspection solutions in complex industrial environments, existing robotic platforms face significant limitations in terms of endurance, payload capacity and obstacle-crossing capabilities. In this paper, we present a novel inspection robot system based on the CubeTrack tracked platform, featuring a large configuration space achieved through the integration of a manipulator and advanced mobility mechanisms. Our system incorporates a quad-slider elliptical trammel mechanism (Qs-ETM) that enables geometry-changing tracks for enhanced terrain adaptability while maintaining track tension stability. To address multi-layer navigation challenges, we propose an efficient trajectory planning algorithm that extracts traversable planes from three-dimensional (3D) point clouds and constructs a lightweight plane graph for path optimisation. Additionally, we develop a flipper control algorithm that uses only low-cost local sensor measurement (time-of-flight [TOF] sensors and inertial measurement unit [IMU]) to enable autonomous stair navigation without pre-mapped environments. The inspection system integrates multiple sensors, including light detection and ranging (LiDAR) sensor, RGB cameras, gas sensors and thermal cameras, providing comprehensive monitoring capabilities for industrial inspection demands. Extensive real-world experiments demonstrate the system's effectiveness in navigating complex environments with stairs, multiple layers and narrow passages, validating both the mechanical design and algorithmic approaches for practical industrial inspection tasks.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653378","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 : 2026-03-11

引用次数: 0

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-11

引用次数: 0

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-11

引用次数: 0

A Vision-Based Motion Compensation Method for Unmanned Aerial Vehicles 基于视觉的无人机运动补偿方法

IF 1.2

IET Cybersystems and Robotics Pub Date : 2026-03-11 DOI: 10.1049/csy2.70044

Yiwei Wang, Weifeng Liu, Meng Zhou

{"title":"A Vision-Based Motion Compensation Method for Unmanned Aerial Vehicles","authors":"Yiwei Wang, Weifeng Liu, Meng Zhou","doi":"10.1049/csy2.70044","DOIUrl":"https://doi.org/10.1049/csy2.70044","url":null,"abstract":"In target tracking under background motion scenarios, background movement often significantly compromises detection and tracking accuracy. This paper proposes a visual motion compensation algorithm for unmanned aerial vehicles, integrating feature point detection, matching and robust geometric estimation methods. First, the SuperPoint network is employed for end-to-end point detection and description, ensuring robust feature extraction in complex environments. Subsequently, the SuperGlue graph neural network is introduced. It optimises feature matching relationships through self-attention and cross-attention mechanisms, whereas the optimal transport layer yields high-confidence matching pairs. Building upon this, MAGSAC++ sampling is employed for outlier rejection and homography matrix estimation of matched points. This enables modelling and compensation for global background motion, effectively isolating the true motion trajectory of the foreground target. This approach balances local precision with global robustness, maintaining high matching accuracy and stability under background interference, noise and partial occlusion. It provides reliable background motion compensation support for infrared small target detection.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/csy2.70044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147653377","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