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Bearing-Based Target Entrapping control of Multiple Uncertain Agents With Arbitrary Maneuvers

IFAC-PapersOnLine 2023年第2期56卷 8821-8826页

作者： Haifan Su Ziwen Yang Shanying Zhu Cailian Chen Wenbin Yu Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

This paper is concerned with bearing-based cooperative target entrapping control of multiple uncertain agents with arbitrary maneuvers including shape deformation, rotations, scalings, etc. A leader-follower structure is used, where the leaders move with the predesigned trajectories, and the followers are steered by an estimation-based control method, integrating a distance estimator using bearing measurements and a stress matrix-based formation controller. The signum functions are used to compensate for the uncertainties so that the agents’ accelerations can be piecewise continuous and bounded to track the desired dynamics. With proper design of the leaders’ trajectories and a geometric configuration, an affine matrix is determined so that the inter-agent relative bearings can be persistently exciting since the bearing rates are related to different weighted combinations of the affine matrix vectors. The asymptotic convergence of the estimation and control error is proved using Filipov properties and cascaded system theories. A sufficient condition for inter-agent collision avoidance is also proposed. Finally, simulation results are given to validate the effectiveness of the method.

关键词： Target entrapping control bearing measurement persistent excitation uncertainty

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A Study on Semi-Supervised Semantic Segmentation of Ship Flame Images 29

A Study on Semi-Supervised Semantic Segmentation of Ship Fla...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wan, Junwei Chen, Yaojie Zheng, Xuecong Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350330717

In addressing the challenges of limited semantic labeling data and imprecise segmentation regions prevalent in the semantic segmentation of ship flame images, this study introduces a semi-supervised semantic segmentation method tailored for ship flame images. This approach is grounded in the principles of pairwise similarity with strong-weak perturbations. Adopting a pseudo-label self-learning strategy, the method employs weak-strong perturbation pairwise similarity, ensuring the propagation of high-confidence predictions during label generation, and thereby enhancing performance. Additionally, to tackle the inherent difficulties presented by smoke enveloping and obscuring flame regions in ship images, we have conceived a Vertical Continuity Feature Enhancement module. This module empowers the model to discern more explicit inter-class boundaries. Empirical evaluations on our proprietary dataset substantiate the superiority of our method, registering an improvement of 4.12 percentage points in mIoU over other semi-supervised approaches. Such results attest to the efficacy of the proposed pairwise similarity approach and the Vertical Continuity Feature Enhancement module in flame semantic segmentation. © 2023 IEEE.

关键词： Feature enhancemen Pairwise similarity Semantic segmentation Semi-Supervised learning

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Cyber topology design guaranteed structural controllability for networked systems

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Science China(information Sciences) 2022年第11期65卷 303-304页

作者： Jianbin MU Shaoyuan LI Jing WU Ning LI Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University

Dear editor,Structure analysis is of crucial importance for achieving reliable system functionality. In many real-world systems, the exact parameters of system model are not available owing to modeling uncertainties or measurement noise, and then structural controllability is pursued in control configuration design. As first proposed by Lin in [1],

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High-Resolution Urban Flows Forecasting With Coarse-Grained Spatiotemporal Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 315-327页

作者： Xu, Zhenyi Kang, Yu Cao, Yang Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Department of Automation University of Science and Technology of China Hefei230026 China Chinese Academy of Sciences Key Laboratory of Technology in GeoSpatial Information Processing and Application System Beijing100192 China

Urban flow monitoring and forecasting systems play important roles in smart city management. However, due to the long-lasting and enormous deployment cost of ubiquitous traffic monitoring devices (e.g., loop detectors, traffic video detection), it is very difficult to predict flow in high-resolution (HR) with limited monitoring devices. The existing spatiotemporal network based methods usually predict the urban flow in the same spatial scale without considering the spatial correlations between coarse-grained and fine-grained urban flows. To tackle these issues, we propose a HR spatiotemporal transformer network (HRSTT) to predict fine-grained urban flow. Specifically, residual convolution units are employed to constructs the high-level features of three-view temporal (e.g., closeness, period, and trend) flow data. Then, a transformer block is designed to jointly learn the spatiotemporal dynamic features of each temporal flow with self-attention mechanism. For the external factors (e.g., holidays, weather conditions) are extracted by embedding dense networks, which are fused with high level coarse-grained flow feature maps with gated-fusion scheme. Finally, the coarse-grained fusion feature maps are transferred to the distributional upsampling module, generating the fined-grained flow map of target predicted time. Furthermore, the proposed model is evaluated with several baselines on real-world TaxiBJ datasets, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow forecasting problem. © 2020 IEEE.

关键词： Deep neural networks

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation 8

Cross-Modality Comprehensive Feature Aggregation Network for...

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8th International Symposium on Computer Science and Intelligent control, ISCSIC 2024

作者： Yang, Yale Zhang, Zihao Hou, Huifang Zhang, Wenwen Zhuang, Chunsheng Henan University of Technology College of Information Science and Engineering Zhengzhou China Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control School of Artificial Intelligence and Big Data Zhengzhou China Data Henan University of Technology School of Artificial Intelligence and Big Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (纸本)9798350380286

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences;then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Online-Learning-Based Distributionally Robust Motion control with Collision Avoidance for Mobile Robots

Online-Learning-Based Distributionally Robust Motion Control...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Han Wang Chao Ning Longyan Li Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Collision-free navigation is a critical issue in robotic systems as the environment is often dynamic and uncertain. This paper investigates a data-stream-driven motion control problem for mobile robots to avoid randomly moving obstacles when the probability distribution of the obstacle’s movement is partially observable through data and can be even time-varying. A data-stream-driven ambiguity set is firstly constructed from movement data by leveraging a Dirichlet process mixture model and is updated online using real-time data. Then we propose an Online-Learning-based Distributionally Robust Nonlinear Model Predictive control (OL-DR-NMPC) approach for limiting the risk of collision through considering the worst-case distribution within the ambiguity set. To facilitate solving the OL-DR-NMPC problem, we reformulate it as a finite-dimensional nonlinear optimization problem. To cope with the bilinear matrix inequality constraints in the nonlinear problem, we develop a parabolic relaxation and a sequential algorithm, by which the problem is further transformed into polynomial-time solvable surrogates. The simulations using a quadrotor model are employed to demonstrate the effectiveness and advantages of the proposed method.

关键词： Navigation Simulation Real-time systems Probability distribution Safety Mobile robots Collision avoidance

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Optimal Planning of Zero-Carbon Off-Grid Ammonia-Hydrogen-Based Microgrids Considering Hybrid Types of Electrolyzer Modules

Optimal Planning of Zero-Carbon Off-Grid Ammonia-Hydrogen-Ba...

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Power system and Green Energy Conference (PSGEC)

作者： Junhao Gu Longyan Li Chao Ning Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350365573

ISBN: (纸本)9798350365580

The off-grid ammonia-hydrogen-based microgrid is recognized as a promising paradigm for industrial decarbonization. In this paper, we propose a novel planning model for off-grid ammonia-hydrogen-based microgrids, where the hydrogen is produced in a distributed manner and the ammonia is synthesized in a large-scale energy-intensive plant. In specific, the proposed planning model considers hybrid types of electrolyzer modules and systematically investigates the operational constraints of distribution networks and hydrogen storage equipment, enabling distributed flexible hydrogen production. To seamlessly bridge distributed hydrogen sources and centralized ammonia production site, the delivery of hydrogen through the transportation network is structured as a vehicle routing problem. Eventually, the effectiveness of the proposed model is validated through case studies. Simulation results show that the proposed model lowers the total system cost by 11.8% compared with the simplified scenario which uses one single type of electrolyzer.

关键词： Ammonia Costs Hydrogen Vehicle routing Production Microgrids Distribution networks Planning Numerical models Optimization

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Robust Stability and Stabilization Conditions for Fractional-order systems with Distributed Delays and Polytopic Uncertainties

Robust Stability and Stabilization Conditions for Fractional...

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Chinese control and Decision Conference, CCDC

作者： Yi-Nan Chen Jun-Guo Lu Zhen Zhu Jia-Rui Zhang Qing-Hao Zhang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper investigates the robust stability and stabilization conditions for fractional-order systems with distributed delays (FOSDDs) with polytopic uncertainties. To begin with, two robust stability conditions for FOSDDs with polytopic uncertainties are formulated. Subsequently, the state feedback controllers are put forward to robustly stabilize the FOSDDs with polytopic uncertainties. The obtained results are illustrated utilizing linear matrix inequalities (LMIs). Finally, two numerical instances are provided to showcase the effectiveness of the derived conditions.

关键词： State feedback Uncertainty Robust stability Delays Linear matrix inequalities Numerical stability

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Towards Autonomous Indoor Parking: A Globally Consistent Semantic SLAM system and A Semantic Localization Subsystem

arXiv

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arXiv 2024年

作者： Sha, Yichen Zhu, Siting Guo, Hekui Wang, Zhong Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China Dimensional Robot Shanghai China

We propose a globally consistent semantic SLAM system (GCSLAM) and a semantic-fusion localization subsystem (SF-Loc), which achieves accurate semantic mapping and robust localization in complex parking lots. Visual cameras (front-view and surround-view), IMU, and wheel encoder form the input sensor configuration of our system. The first part of our work is GCSLAM. GCSLAM introduces a novel factor graph for the optimization of poses and semantic map, which incorporates innovative error terms based on multi-sensor data and BEV (bird’s-eye view) semantic information. Additionally, GCSLAM integrates a Global Slot Management module that stores and manages parking slot observations. SF-Loc is the second part of our work, which leverages the semantic map built by GCSLAM to conduct map-based localization. SF-Loc integrates registration results and odometry poses with a novel factor graph. Our system demonstrates superior performance over existing SLAM on two real-world datasets, showing excellent capabilities in robust global localization and precise semantic mapping. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation 4

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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4th International Conference on information Science, Parallel and Distributed systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

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