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Mean square stability for Markov jump Boolean networks

Science China(Information Sciences) 2020年第1期63卷 190-199页

作者： Liqing WANG Mei FANG Zheng-Guang WU State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University College of Automation Harbin Engineering University

In this paper, one of the stability definitions of Markov jump Boolean networks(MJBNs), called mean square stability(MSS), is investigated. Some necessary and sufficient conditions are presented to guarantee the MSS of such MJBNs. Moreover, one of the necessary and sufficient conditions for MSS is obtained in terms of linear programming, which implies that MSS is equivalent to global stability with probability 1 for MJBNs. Furthermore, the construction of Lyapunov function is given and also another theorem based on the Lyapunov function is derived to ensure the MSS of MJBNs. Finally, a numerical example is provided to illustrate the profits of our results.

关键词： mean square stability MSS Boolean network BN stochastic stability Markovian jump parameters semi-tensor product STP Lyapunov function

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Fast Iterative Region Inflation for Computing Large 2-D/3-D Convex Regions of Obstacle-Free Space

arXiv

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arXiv 2024年

作者： Wang, Qianhao Wang, Zhepei Wang, Mingyang Ji, Jialin Han, Zhichao Wu, Tianyue Jin, Rui Gao, Yuman Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China

Convex polytopes have compact representations and exhibit convexity, which makes them suitable for abstracting obstacle-free spaces from various environments. Existing generation methods struggle with balancing high-quality output and efficiency. Moreover, another crucial requirement for convex polytopes to accurately contain certain seed point sets, such as a robot or a front-end path, is proposed in various tasks, which we refer to as manageability. In this paper, we propose Fast Iterative Regional Inflation (FIRI) to generate high-quality convex polytope while ensuring efficiency and manageability simultaneously. FIRI consists of two iteratively executed submodules: Restrictive Inflation (RsI) and Maximum Volume Inscribed Ellipsoid (MVIE) computation. By explicitly incorporating constraints that include the seed point set, RsI guarantees manageability. Meanwhile, iterative MVIE optimization ensures high-quality result through monotonic volume bound improvement. In terms of efficiency, we design methods tailored to the low-dimensional and multi-constrained nature of both modules, resulting in orders of magnitude improvement compared to generic solvers. Notably, in 2-D MVIE, we present the first linear-complexity analytical algorithm for maximum area inscribed ellipse, further enhancing the performance in 2-D cases. Extensive benchmarks conducted against state-of-the-art methods validate the superior performance of FIRI in terms of quality, manageability, and efficiency. Furthermore, various real-world applications showcase the generality and practicality of FIRI. The high-performance code of FIRI will be open-sourced. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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Non-fragile H∞ Filtering for Fuzzy Discrete-time systems with Markovian Jump and Data Loss 10

Non-fragile H∞ Filtering for Fuzzy Discrete-time Systems wi...

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Liang, Lun State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang Hangzhou310027 China

ISBN: (纸本)9781665424233

This paper focuses on the design of the non-fragile H∞ filtering of fuzzy discrete-time systems with Markovian jump and data loss. The system is represented by Takagi and Sugeno (T-S) fuzzy model. The imperfect information transmission from the factory to the filter is modeled by a Bernoulli random binary distribution. A non-fragile and full-order H∞ filter with additive gain variations is constructed. The sufficient conditions are derived based on mode-dependent and fuzzy-basis-dependent Lyapunov functional methods, which ensure the developed filtering error system is stochastically stable with given H∞ performance index. The usefulness and effectiveness of the filter design method is certified by a numerical case. © 2021 IEEE.

关键词： Numerical methods

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ColAG: A Collaborative Air-Ground Framework for Perception-Limited UGVs’ Navigation

ColAG: A Collaborative Air-Ground Framework for Perception-L...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhehan Li Rui Mao Nanhe Chen Chao Xu Fei Gao Yanjun Cao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute of Zhejiang University Huzhou China Dalian University of Technology China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Perception is necessary for autonomous navigation in an unknown area crowded with obstacles. It’s challenging for a robot to navigate safely without any sensors that can sense the environment, resulting in a blind robot, and becomes more difficult when comes to a group of robots. However, it could be costly to equip all robots with expensive perception or SLAM systems. In this paper, we propose a novel system named ColAG, to solve the problem of autonomous navigation for a group of blind UGVs by introducing cooperation with one UAV, which is the only robot that has full perception capabilities in the group. The UAV uses SLAM for its odometry and mapping while sharing this information with UGVs via limited relative pose estimation. The UGVs plan their trajectories in the received map and predict possible failures caused by the uncertainty of its wheel odometry and unknown risky areas. The UAV dynamically schedules waypoints to prevent UGVs from collisions, formulated as a Vehicle Routing Problem with Time Windows to optimize the UAV’s trajectories and minimize time when UGVs have to wait to guarantee safety. We validate our system through extensive simulation with up to 7 UGVs and real-world experiments with 3 UGVs.

关键词： Uncertainty Simultaneous localization and mapping Navigation Pose estimation Autonomous aerial vehicles Trajectory Safety

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An Improved Machine Learning Model for Pure Component Property Estimation

引用

Engineering 2024年第8期39卷 61-73页

作者： Xinyu Cao Ming Gong Anjan Tula Xi Chen Rafiqul Gani Venkat Venkatasubramanian State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Physics Bard College at Simon’s RockGreat BarringtonMA 01230USA PSE for SPEED Company Charlottenlund DK-2920Denmark Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 510000China Department of Applied Sustainability Széchenyi István UniversityGyör 9026Hungary Complex Resilient Intelligent Systems Laboratory Department of Chemical EngineeringColumbia UniversityNew YorkNJ 10027USA

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product ***,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these ***,accuracy and predictive capabilities still limit the scope and applicability of most property estimation *** paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different *** selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research *** framework is assessed using datasets of varying sizes for 20 pure component *** 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

关键词： Group contribution Gaussian process Warping function Prior predictive checking

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Obstacle Avoidance for Unicycle-Modelled Mobile Robots with Time-varying control Barrier Functions

arXiv

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arXiv 2023年

作者： Huang, Jihao Liu, Zhitao Zeng, Jun Chi, Xuemin Su, Hongye State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Cruise LLC United States

In this paper, we propose a safety-critical controller based on time-varying control barrier functions (CBFs) for a robot with an unicycle model in the continuous-time domain to achieve navigation and dynamic collision avoidance. Unlike previous works, our proposed approach can control both linear and angular velocity to avoid collision with obstacles, overcoming the limitation of confined control performance due to the lack of control variable. To ensure that the robot reaches its destination, we also design a control Lyapunov function (CLF). Our safety-critical controller is formulated as a quadratic program (QP) optimization problem that incorporates CLF and CBFs as constraints, enabling real-time application for navigation and dynamic collision avoidance. Numerical simulations are conducted to verify the effectiveness of our proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： controllers

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DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition

DeepRING: Learning Roto-translation Invariant Representation...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Sha Lu Xuecheng Xu Li Tang Rong Xiong Yue Wang State Key Laboratory of Industrial Control and Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Alibaba Group Hangzhou China

LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with a large perspective difference. To address the challenge, we propose DeepRING to learn the roto-translation invariant representation from LiDAR scan, so that robot visiting the same place with a different perspective can have similar representations. There are two keys in DeepRING: the feature is extracted from sinogram, and the feature is aggregated by magnitude spectrum. The two steps keep the final representation with both discrimination and roto-translation invariance. Moreover, we state place recognition as a one-shot learning problem with each place being a class, leveraging relation learning to build representation similarity. Substantial experiments are carried out on public datasets, validating the effectiveness of each proposed component, and showing that DeepRING outperforms the comparative methods, especially in dataset level generalization.

关键词：

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BEV2PR: BEV-Enhanced Visual Place Recognition with Structural Cues

BEV2PR: BEV-Enhanced Visual Place Recognition with Structura...

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Fudong Ge Yiwei Zhang Shuhan Shen Weiming Hu Yue Wang Jin Gao State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) CASIA School of Artificial Intelligence University of Chinese Academy of Sciences School of Information Science and Technology ShanghaiTech University State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we propose a new image-based visual place recognition (VPR) framework by exploiting the structural cues in bird’s-eye view (BEV) from a single monocular camera. The motivation arises from two key observations about place recognition methods based on both appearance and structure: 1) For the methods relying on LiDAR sensors, the integration of LiDAR in robotic systems has led to increased expenses, while the alignment of data between different sensors is also a major challenge. 2) Other image-/camera-based methods, involving integrating RGB images and their derived variants (e.g., pseudo depth images, pseudo 3D point clouds), exhibit several limitations, such as the failure to effectively exploit the explicit spatial relationships between different objects. To tackle the above issues, we design a new BEV-enhanced VPR framework, namely BEV 2 PR, generating a composite descriptor with both visual cues and spatial awareness based on a single camera. The key points lie in: 1) We use BEV features as an explicit source of structural knowledge in constructing global features. 2) The lower layers of the pretrained backbone from BEV generation are shared for visual and structural streams in VPR, facilitating the learning of fine-grained local features in the visual stream. 3) The complementary visual and structural features can jointly enhance VPR performance. Our BEV 2 PR framework enables consistent performance improvements over several popular aggregation modules for RGB global features. The experiments on our collected VPR-NuScenes dataset demonstrate an absolute gain of 2.47% on Recall@1 for the strong Conv-AP baseline to achieve the best performance in our setting, and notably, a 18.06% gain on the hard set. The code and dataset will be available at https://***/FudongGe/BEV2PR.

关键词： Image sensors Visualization Laser radar Three-dimensional displays Robot vision systems Cameras Sensor systems Sensors Streams Visual place recognition

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Adaptive Sliding Mode control for Trajectory Tracking in Four-Wheeled Mobile Robots Under Uncertainties and External Disturbances

Adaptive Sliding Mode Control for Trajectory Tracking in Fou...

引用

Chinese control and Decision Conference, CCDC

作者： Naeem Ajilforoushan Saba Ghorbani Barzegar Yongliang Lin Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In this paper, we propose an adaptive sliding mode control (SMC) design technique to track the desired trajectory of the skid-steering mobile robots in the presence of model uncertainties and perturbations. To enhance the stability of the robot and ensure accurate trajectory tracking, adaptive rules have been employed to estimate the upper bound of perturbations. This estimation, in turn, serves to stabilize the robot and guarantee the desired trajectory. Moreover, we analyze and calculate the convergence time of the sliding surface, ensuring its convergence within finite time. Additionally, efforts have been made to minimize the chattering phenomenon by smoothing the discontinuity of the sliding surface. Simulation results validate the efficacy of the proposed controller, demonstrating its ability to deliver acceptable performance in diverse scenarios.

关键词： Uncertainty Upper bound Trajectory tracking Perturbation methods Estimation Stability analysis Trajectory

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Efficient and High-quality Neural Implicit RGB-D Surface Reconstruction with Volume Feature Rendering

Efficient and High-quality Neural Implicit RGB-D Surface Rec...

引用

Robotics, control and Automation (ICRCA), International Conference on

作者： Jintao Wan Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

Acquiring high-quality 3D surfaces of indoor scenes is essential for applications in VR/ AR and robotics. Recent advancements in neural implicit surface learning and differ-entiable volume rendering methods have significantly improved the accuracy and completeness of geometry. In our work, we propose a volumn feature rendering method for fast and detailed surface reconstruction from muti-view RGB-D frames. We adopt a hybrid scene representation utilizing muti-resolution hash encoding and two shallow MLPs for fast convergence. We suggest improvements by replacing standard volume rendering with vol-ume feature rendering to optimize speed. Additionally, we employ an adaptive sampling strategy guided by dynamic occupancy grids to accelerate training. We introduce a curvature term to promote surface smoothness and hole filling in untexture areas. Experiments on standard benchmarks show that our method can process room-scale scenes in approximately 10 minutes, achieving a 2x speedup compared to Go-Surf [14] in a memory-efficient way, while maintaining comparable or superior reconstruction quality.

关键词： Training Geometry Surface reconstruction Three-dimensional displays Accuracy Rendering (computer graphics) Surface treatment

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