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Rethinking iterative stereo matching from a diffusion bridge model perspective

Pattern Recognition 2025年 167卷

作者： Shi, Yuguang Zhou, Sifan Wang, Wei Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

Stereo matching is vital for 3D perception in vision and robotics. Although iteration-based stereo matching has demonstrated competitive performance, it also has inherent limitations, such as the discrete nature of the iteration process, which can lose geometric structure information, and the dependence on a large number of iterations. In this paper, we present a novel iterative-based framework, named Diffusion Models for Iterative Optimization (DMIO). DMIO introduces a Time-based Update Network (T-UN) that embeds time encoding into sub-modules within the update network. This approach correlates temporal and disparity outputs and effectively integrates diffusion models into the iterative framework. This design enables DMIO to allow features containing fine details to be transmitted during the update process and can be seamlessly embedded into most iterative methods. We further design a Motion Agent Attention (MAA) module in the update network, which generates a set of attention weights for the local cost volume to filter redundant information. In addition, we introduce channel attention into the context network, which can generate more expressive features to improve accuracy. Experiments on several public benchmarks for stereo matching show that DMIO achieves over an 8.5% improvement over competing method and requires only 8 iterations to achieve state-of-the-art performance. © 2025 Elsevier Ltd

关键词： Stereo image processing

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Composite-mask GAN based on refined optical flow and disparity map for SLAM visual odometry

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Optoelectronics Letters 2025年

作者： JI Yuehui JIANG Jingwei LIU Junjie SONG Yu GAO Qiang School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory of Control Theory and Application for Complex Systems

Although deep learning methods have been widely applied in slam visual odometry over the past decade with impressive improvements， the accuracy remains limited in complex dynamic environments. In this paper， a composite mask-based generative adversarial network is introduced to predict camera motion and binocular depth maps. Specifically， a perceptual generator is constructed to obtain the corresponding parallax map and optical flow from between two neighboring frames. Then， an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation. Finally， a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image， thereby increasing the overall structural constraints of the network model， improving the accuracy of camera pose estimation， and reducing drift issues in the Visual Odometer. Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional， supervised learning and unsupervised depth VO methods， providing better results in both pose estimation and depth estimation.

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Noncollocated Output Feedback control for the Semilinear Parabolic PDEs Under Input and Output Quantization

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IEEE Transactions on Automatic control 2025年第5期70卷 2934-2947页

作者： Zhao, Xuena Liu, Zhijie Zhang, Shuang Liu, Yu He, Wei University of Science and Technology Beijing Institute of Artificial Intelligence Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Beijing100083 China School of Automation Science and Engineering Engineering Research Center of Precision Electronic Manufacturing Equipment of Ministry of Education Guangzhou510640 China Institute of Artificial Intelligence Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Beijing100083 China School of Automation Beijing102206 China

In the context of increasingly networked control systems, this article concentrates on the noncollocated control issue of input and output quantization for industrial processes governed by semilinear parabolic partial differential equation (PDE) systems. Both the control and measurement signals are quantized and transmitted through the network, which causes signal errors and reduces the stability and robustness of the system. A Luenberger-type PDE observer is developed to overcome the control difficulty of quantized measurement feedback signals and noncollocated pointwise sensors. Based on the observed value, a feedback controller is proposed, which undergoes quantization and operates on the distributed actuators within the domain. By employing the Lyapunov direct method, it is demonstrated that the observer can accurately track the system state with exponential precision, and the closed-loop system is exponentially converged. The well-posedness analysis of the closed-loop system is discussed via the C0-semigroup method. The performance of the developed control strategy is validated through simulation examples. © 1963-2012 IEEE.

关键词： Feedback control

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Design and Performance Analysis of a Series-Parallel Self-Aligning Index Finger Exoskeleton

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IEEE Robotics and automation Letters 2025年第6期10卷 6456-6463页

作者： Sun, Ning Wang, Zelong Cheng, Long Li, Houcheng Niu, Ben Sun, Xijin Li, Ke Shandong Normal University School of Information Science and Engineering Jinan China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Shandong University Institute of Intelligent Medical Engineering School of Control Science and Engineering Jinan China

Hand exoskeletons have become increasingly crucial for the rehabilitation of hand function, as relevant studies have shown that using the exoskeletons to assist in rehabilitation training can improve hand motor function. However, developing a human-robot kinematic compatibility exoskeleton while providing sufficient assistance torque is still a challenge. This paper develops a series-parallel self-aligning index finger exoskeleton to assist the index finger in flexion/extension and adduction/abduction training. A series-parallel mechanism driven by two motors simultaneously was proposed for the metacarpophalangeal (MCP) joint, which can ensure human-robot joint axes self-alignment and increase the joint assistance torque. A kinematic compatibility analysis method was proposed, and the kinematic compatibility of the series-parallel mechanism was analyzed. The degrees of freedom and forward kinematics of the proposed mechanism were analyzed, and its workspace was calculated, which results show that the motion range of the MCP joint can meet the grasping training requirements. Preliminary experiments were carried out, and the results indicate that the motion trajectory coefficients of the marker points at the MCP, PIP, and DIP joints averaged for all subjects with and without the proposed exoskeleton are 0.993, 0.956, and 0.939, respectively, which can verify the kinematic transparency of the proposed mechanism. The assistance torque generated by the proposed mechanism on the MCP joint can meet the joint assistance torque requirements. In a word, the proposed exoskeleton can achieve flexion/extension and adduction/abduction training, human-robot joint axes self-alignment, and provide sufficient assistance torque. © 2016 IEEE.

关键词： Exoskeleton (Robotics)

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Lyapunov-based stochastic model predictive control of stochastic nonlinear systems with input-delay using multi-dimensional Taylor network

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Journal of the Franklin Institute 2025年第9期362卷

作者： Zheng, Xiao-Yi Yan, Hong-Sen Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Jiangsu Nanjing210096 China School of Automation Southeast University Jiangsu Nanjing210096 China

Most of the existing stability control approaches to stochastic nonlinear systems with input-delay are based on robust mechanisms, which tend to be too conservative. Here, a Lyapunov-based stochastic model predictive control (LSMPC) scheme using the Fokker–Planck equation (FPE) is proposed to solve the problem. In the framework of the LSMPC approach, input-delay is compensated by the predictor feedback strategy, and the LSMPC method is extended to stochastic nonlinear systems with input-delay. FPE is introduced to describe the dynamic evolution of the probability density function (PDF) for stochastic nonlinear systems. The multi-dimensional Taylor network (MTN) is applied for approximating the solution of FPE. The proposed FPE-based LSMPC method proves to be capable of securing the probabilistic feasibility and stability of the closed-loop system. The closed-loop states turn out to succeed in converging to the small region around the origin in probability. The validity of the presented scheme is verified by an example. © 2025 The Franklin Institute

关键词： Predictive control systems

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Dynamic and Heterogeneous Identity-Based Cooperative Co-Evolution for Distributed Lot-Streaming Flowshop Scheduling Problem

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complex System Modeling and Simulation 2025年第1期5卷 86-106页

作者： Wang, Juan Zhang, Guanghui Li, Xiaoling Feng, Yanxiang School of Information Science and Technology Hebei Agricultural University Baoding071001 China Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an710049 China School of Electronics and Control Engineering Chang'an University Xi'an710064 China School of Automation Science and Engineering Xi'an Jiaotong University Xi'an710049 China

In this research, a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm (DHICCA) is proposed for addressing the distributed lot-streaming flowshop scheduling problem (DLSFSP) with the objective to minimize the makespan. A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA. In the evolution of DHICCA, population individuals are endowed with heterogeneous identities according to their quality, including superior individuals, ordinary individuals, and inferior individuals, which serve local exploitation, global exploration, and diversified restart, respectively. Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials, identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way. This is important to use limited population resources to solve complex optimization problems. Specifically, exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood, destruction-construction, and gene targeting. Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy. In addition, restart is performed on inferior individuals to introduce new evolutionary individuals to the population. After the cooperative co-evolution, all individuals with different identities are merged as a population again, and their identities are dynamically adjusted by new evaluation. The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms. The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving

关键词： Vector spaces

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Pipeline Anomaly Detection Based on KPCA and Cosine Distance Prototypical Network 26

Pipeline Anomaly Detection Based on KPCA and Cosine Distance...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Yi, Kang Liu, Xin Hu, Wenkai Li, Yupeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798331521950

Accurately and promptly detecting the pipeline anomaly is crucial to the safe operation of pipeline systems, while a difficulty lies in that many existing methods require massive data for training models. However, pipelines are running under normal state for the most of the time, and labeled pipeline anomaly data is usually scarce. Among the commonly used sensors, vibration sensors are widely utilized in pipeline detection because of their advantages such as easy installation and high sensitivity. However, the vibration signal shows non-stationary characteristics when anomalies occur, and are contaminated by noises, making it difficult to represent the actual state with features extracted from either the time or frequency domain. Accordingly, this paper proposes a pipeline anomaly detection method based on the KPCA (kernel principal component analysis) and cosine distance prototypical network. First, features are extracted from original signals;then, the feature dimension is reduced by KPCA;last, the cosine distance is introduced to the prototypical network for anomaly detection. The effectiveness of the proposed method is demonstrated by case studies involving experimental data. © 2025 IEEE.

关键词： Frequency domain analysis

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Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation and Improved Depthwise Separable Convolution Network 26

Bearing Fault Diagnosis Under Variable Working Conditions Ba...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Kuang, Zehui Mao, Fan Zhao, Xingyu Wan, Xiongbo School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Domain adaptation methods and appropriate feature extractors are usually applied to solve the problem that the variable working conditions of bearings affect the effectiveness of the fault diagnostic framework. However, the existing feature extractors are too complex to be deployed in real industries. In this paper, we propose a lightweight framework called improved depthwise separable convolution network (IDSCN) for the diagnosis of bearing faults in variable operating conditions. IDSCN is built on wavelet convolution and depthwise separable residual convolution (DSRC), where the wavelet convolution is used to extract frequency features from the source and target domains, and DSRC guarantees the performance of feature extraction with reduced parameters and computation. To minimise the distribution difference between the source and target domains, the local maximum mean difference is incorporated into the proposed framework. The performance of the proposed framework is validated on the Paderborn University dataset, involving a total of 6 transfer tasks. The experiment shows that the proposed framework achieves satisfactory results and outperforms other frameworks in bearing fault diagnosis. © 2025 IEEE.

关键词： Bearing fault diagnosis Depthwise separable residual convolution Domain adaptation Wavelet convolution

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Multi-Level Features Fusion for Zero-Shot Object Pose Estimation 26

Multi-Level Features Fusion for Zero-Shot Object Pose Estima...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Huang, Nanxin Xu, Chi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798331521950

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models. © 2025 IEEE.

关键词： Industrial robots

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Robust control Analysis With Model Transformation for Interval Type-2 Fuzzy systems

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IEEE Transactions on Fuzzy systems 2025年

作者： Yang, Jie Gai, Shaoyan Da, Feipeng Xie, Wenbo Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China Shanghai University and Shanghai Artificial Intelligence Laboratory School of Mechatronic Engineering and Automation Shanghai200444 China

To address the problem of robust stability analysis for interval type-2 fuzzy systems (IT2FSs), this paper proposes an innovative analysis approach based on model transformation. Firstly, a classical piecewise linear approximation method is utilized to process the upper boundary membership functions (UMFs) and lower boundary membership functions (LMFs) of the footprint of uncertainty (FOU) in IT2FSs, resulting in linear boundary membership functions (MFs) that are more convenient for analysis, along with the corresponding approximation error functions. Subsequently, a novel error model transformation method is introduced to handle these error terms. By constructing new fuzzy rules and MFs, the boundary error terms are converted into a new fuzzy model, thereby incorporating more information about the error functions into the stability analysis. Based on this model, a robust stability condition in the form of linear matrix inequalities (LMIs) are derived, achieving improved robustness. Finally, the effectiveness of the proposed method is validated through simulations on real-world systems, and its superiority is demonstrated by comparison with existing methods. © 2025 IEEE.

关键词： Membership functions

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