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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Zhang, Yunfei Bian, Xinyu Sun, Yize Zhang, Zongyu Huang, Hai Yang, Xu National Key Laboratory of Autonomous Marine Vehicle Technology Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350384185

Underwater vehicle manipulator systems (UVMSs) is becoming a research hotspot in the field of marine intelligent equipment. Introducing visual feedback equipped with cameras in the loop to guide UVMS to complete the grasping has comprehensive advantages of response speed, work efficiency and intelligence. In this paper, mapping between the feature error and the motion of the UVMS generalized degrees of freedom is established to realize the suspended grasping under the coordinated motion of the vehicle body and the manipulator. Then, a UVMS motion control algorithm based on time delay estimation (TDE) is constructed for visual servo autonomous grasping process, the TDE of which is used to estimate and compensate the lumped interference in real time. The Lyapunov theory is applied to analyze its stability and its control performance is verified through simulation experiments. The simulation results shew the effectiveness of the proposed visual servo control with TED method based on line features. © 2024 IEEE.

关键词： Visual servoing

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An Optimal control Based Trajectory Planning Method for Cooperative Merging Maneuvers 3

An Optimal Control Based Trajectory Planning Method for Coop...

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3rd International Symposium on Intelligent Robotics and systems, ISoIRS 2023

作者： Wang, Qi Ge, Hao Duan, Xuting Zhou, Jianshan School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control State Key Laboratory of Air Traffic Management System and Technology Beijing China Nanjing Research Institute of Electronic Engineering State Key Laboratory of Air Traffic Management System and Technology Nanjing China School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China

ISBN: (纸本)9798350339963

Due to the prediction errors and diverse driving styles of surrounding vehicles, traffic congestion and even accidents can occur in the traffic bottleneck area where vehicles merge into the main road. To ensure that connected and automated vehicles (CAVs) can quickly and efficiently merge into the main road, this paper proposes a cooperative trajectory planning method based on optimal control for vehicle cut-in scenarios. A optimal control model is constructed, and a quintic polynomial is used to generate the initial solution of the optimal control problem to accelerate the solution process and improve the success rate. Finally, The obtained merging trajectory is then synchronized with the movement of vehicles on the main road to optimize their actions and ensure a safe merging process. Joint simulation with Robot Operating system (ROS) and RVIZ is utilized, and the results show that the proposed algorithm can quickly obtain cooperative results, the locally successful merging trajectory meets the requirements of smoothness and safety, and if the solution does not allow merging, the merging vehicle is instructed to terminate the execution. © 2023 IEEE.

关键词： Trajectories

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Object Detection and Multiple Objective Optimization Manipulation Planning for Underwater Autonomous Capture in Oceanic Natural Aquatic Farm

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Journal of Field Robotics 2025年第0期

作者： Hai, Huang Tao, Jiang Xinyu, Bian Hao, Zhou Xu, Yang Gang, Wang Hongde, Qin Xinyue, Han National Key Laboratory of Science and Technology on Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Bei Jing China

Underwater autonomous capture operations offer significant potential for reducing labor and health risks in sea organism industries. This study presents a comprehensive solution for cross-domain underwater object detection and autonomous capture. A novel unsupervised domain adaptive learning method is proposed, integrating multiscale domain adaptive modules and attention mechanisms into a Faster Region-Convolutional Neural Network framework. This approach enhances feature alignment across diverse aquatic domains without parameter tuning. Additionally, an efficient, parameterless constrained multiobjective optimization algorithm is introduced for underwater autonomous mobile capture, integrating parameterized trajectory planning with innovative features, such as adaptive mutation strategies and constraint violation tolerance. The proposed approaches are extensively validated through simulations, tank experiments, and real-world oceanic trials in the Natural Aquatic Farm of Zhangzidao Island. Results demonstrate the system's robustness in complex underwater environments with varying currents, with experimental outcomes validating the accuracy and reliability of detection and capture capabilities. This research significantly advances autonomous underwater systems' capabilities in object detection and capture tasks, addressing complex challenges in realistic organism capture applications across diverse aquatic environments. © 2025 Wiley Periodicals LLC.

关键词： Health risks

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Joint Optimization and Termination of Sequential Prediction Maintenance and Spare Parts Ordering under Dynamic Remaining Useful Time Prediction 5

Joint Optimization and Termination of Sequential Prediction ...

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5th International Conference on system Reliability and Safety engineering, SRSE 2023

作者： Zhang, Yongfei Zhang, Xiaohong Yang, Tianxiang Li, Yuxin Shen, Juan Sun, Chaoli Taiyuan University of Science & Technology Taiyuan School of Economics & Management Taiyuan China Taiyuan University of Science & Technology Division of Industrial and System Engineering 030024 China Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan030024 China

ISBN: (纸本)9798350305944

In the research of Prognostics and Health management (PHM), prediction is a dynamic process, and the corresponding maintenance decisions are also dynamic. The problem of updating and termination decisions in dynamic sequential decision-making is a rarely studied but worthy of attention problem. To address these problems, a joint decision-making and termination problem for sequential prediction maintenance and spare parts ordering under dynamic remaining life prediction was studied. The Bayesian update and EM algorithm were used in combination to achieve dynamic remaining life and reliability prediction. An adaptive detection and sequential prediction maintenance strategy based on dynamic prediction results was developed, and a judgment criterion for decision termination was given. At the adaptive detection moment, the system balanced the cost of spare parts storage and stockout, preventive maintenance cost, and post-failure maintenance cost, and determined the optimal maintenance and spare parts ordering time. Based on the termination criterion, the truly executed maintenance and spare parts ordering time were selected from the dynamic redundant decision results. Finally, the effectiveness of the proposed strategy and method were verified through experiments. © 2023 IEEE.

关键词： Forecasting

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Line Feature Based Visual Servo control with TDE for Underwater vehicle Manipulator systems Target Grasping

Line Feature Based Visual Servo Control with TDE for Underwa...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Yunfei Zhang Xinyu Bian Yize Sun Zongyu Zhang Hai Huang Xu Yang National Key Laboratory of Autonomous Marine Vehicle Technology Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

关键词： Visualization Heuristic algorithms Dynamics Estimation Grasping Stability analysis Servomotors vehicle dynamics Underwater vehicles Manipulator dynamics

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An Optimal control Based Trajectory Planning Method for Cooperative Merging Maneuvers

An Optimal Control Based Trajectory Planning Method for Coop...

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Intelligent Robotics and systems (ISoIRS), International Symposium on

作者： Qi Wang Hao Ge Xuting Duan Jianshan Zhou State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control State Key Laboratory of Air Traffic Management System and Technology School of Transportation Science and Engineering Beihang University Beijing China State Key Laboratory of Air Traffic Management System and Technology Nanjing Research Institute of Electronic Engineering Nanjing China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China

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A Real-time Framework of Multi-Camera vehicle Tracking system

A Real-time Framework of Multi-Camera Vehicle Tracking Syste...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Zhuang, Hanyang Chen, Xingyuan Lan, Jianglin Wang, Ying Lu, Zhaoxuan Wang, Chunxiang Yang, Ming University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200040 China Shanghai Intelligent and Connected Vehicle R and D Center Co. LTD Shanghai201400 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350372601

Multi-camera vehicle tracking (MCVT) system is a key technology to build an intelligent city and intelligent transportation system. The MCVT system utilizes roadside monitoring devices and computing platforms to achieve city-level road traffic perception at a finer scale by tracking each vehicle on the road to obtain the trajectory. The existing MCVT system mainly focuses on dealing with the algorithm modules separately, without studying the real-time performance and system architecture of the entire pipeline. However, in real-world applications, many of the tricks in improving evaluation metrics are not applicable since there is endless traffic flow running all the time. Therefore, this paper proposes a real-time framework of the MCVT system to streamline all the algorithm modules with proper data flow. Firstly, a multi-threaded multi-vehicle tracking (MVT) module is developed to detect vehicles in each camera. Re-identification is implemented during MVT to provide the feature for tracking in the same camera and across different cameras. Then, after the vehicle leaves the camera field of view, the feature of vehicle trajectory is calculated by averaging the feature at each frame. Meanwhile, a background thread runs continuously to match the incoming feature of vehicle trajectory with past features by conducting a topology searching method. In the end, the vehicle trajectories with well-matched features are considered to be the same vehicle. The entire pipeline has been validated on two different datasets, the precision is 0.64 in the CityFlow dataset which uses 6 cameras simultaneously. In the customized dataset, the same method can still reach real-time when using 16 cameras at the same time with smaller network modules. © 2024 IEEE.

关键词： vehicle locating systems

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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RGB-D Co-attention Network for Semantic Segmentation 15th

RGB-D Co-attention Network for Semantic Segmentation

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhou, Hao Qi, Lu Wan, Zhaoliang Huang, Hai Yang, Xu National Key Laboratory of Science and Technology of Underwater Vehicle Harbin Engineering University Harbin China The Chinese University of Hong Kong Hong Kong China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030695248

Incorporating the depth (D) information for RGB images has proven the effectiveness and robustness in semantic segmentation. However, the fusion between them is still a challenge due to their meaning discrepancy, in which RGB represents the color but D depth information. In this paper, we propose a co-attention Network (CANet) to capture the fine-grained interplay between RGB’ and D’ features. The key part in our CANet is co-attention fusion part. It includes three modules. At first, the position and channel co-attention fusion modules adaptively fuse color and depth features in spatial and channel dimension. Finally, a final fusion module integrates the outputs of the two co-attention fusion modules for forming a more representative feature. Our extensive experiments validate the effectiveness of CANet in fusing RGB and D features, achieving the state-of-the-art performance on two challenging RGB-D semantic segmentation datasets, i.e., NYUDv2, SUN-RGBD. © 2021, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Joint Optimization and Termination of Sequential Prediction Maintenance and Spare Parts Ordering under Dynamic Remaining Useful Time Prediction

Joint Optimization and Termination of Sequential Prediction ...

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system Reliability and Safety engineering (SRSE), International Conference on

作者： Yongfei Zhang Xiaohong Zhang Tianxiang Yang Yuxin Li Juan Shen Chaoli Sun School of Economics & Management Taiyuan University of Science & Technology Taiyuan Taiyuan China Division of Industrial and System Engineering Taiyuan University of Science & Technology China Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan 030024 China Taiyuan China

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