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

Obstacle Avoidance for Unicycle-Modelled Mobile Robots with ...

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Annual Conference of industrial Electronics Society

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

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.

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Adaptive Fuzzy control With Guaranteed Performance For Electric Vehicles

Adaptive Fuzzy Control With Guaranteed Performance For Elect...

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第35届中国控制与决策会议

作者： Weijun Huang Hongye Su Yang Zhu Ningbo Innovation Center Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University

In this paper,we propose a new adaptive fuzzy control scheme with a saturator for a class of constrainted uncertain unparametrizable nonlinear electric vehicle(EV) *** control scheme effectively addresses the nonlinearities and uncertainties in the EV systems without exact knowledge including variable hill climbing angle and other system parameters,and solves the constraints on the amplitude of input *** techniques of adaptive control,fuzzy logic system,and backstepping design are *** the scheme,the speed tracking error of the system converges to a preset interval asymptotically even in the presence of time-varying hill climbing angle and other system *** control algorithm is illustrated by the simulation results.

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Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

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Chinese Journal of Aeronautics 2022年第5期35卷 40-52页

作者： Quanbo GE Zhongcheng MA Jinglan LI Qinmin YANG Zhenyu LU Hong LI School of Electronics and Information Engineering Tongji UniversityShanghai 201804China Institute of Systems Science and Control Engineering School of AutomationHangzhou Dianzi UniversityHangzhou 310018China State Key Laboratory of Industrial Control Technology and the College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and TechnologyNanjing 210044China Chinese Flight Test Establishment Xi’an 710089China

Mobile robots are often subject to multiplicative noise in the target tracking tasks,where the multiplicative measurement noise is correlated with additive measurement *** this paper,first,a correlation multiplicative measurement noise model is *** is able to more accurately represent the measurement error caused by the distance sensor dependence ***,the estimated performance mismatch problem of Cubature Kalman Filter(CKF)under multiplicative noise is *** improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative *** practice,the model parameters are unknown or inaccurate,especially the correlation of noise is difficult to obtain,which can lead to a decrease in filtering accuracy or even *** address this,an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is ***,the estimated performance is analyzed theoretically,and the simulation study is conducted to validate the effectiveness of the proposed algorithm.

关键词： Adaptive CKF Correlated noise Estimation accuracy Multiplicative noise Performance analysis Target tracking

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Mean square stability for Markov jump Boolean networks

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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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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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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

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...

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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.

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CREPES: Cooperative RElative Pose Estimation System

CREPES: Cooperative RElative Pose Estimation System

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

作者： Zhiren Xunt Jian Huang Zhehan Li Zhenjun Ying Yingjian Wang 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

Mutual localization plays a crucial role in multi-robot cooperation. CREPES, a novel system that focuses on six degrees of freedom (DOF) relative pose estimation for multi-robot systems, is proposed in this paper. CREPES has a compact hardware design using active infrared (IR) LEDs, an IR fish-eye camera, an ultra-wideband (UWB) module and an inertial measurement unit (IMU). By leveraging IR light communication, the system solves data association between visual detection and UWB ranging. Ranging measurements from the UWB and directional information from the camera offer relative 3-DOF position estimation. Combining the mutual relative position with neighbors and the gravity constraints provided by IMUs, we can estimate the 6-DOF relative pose from a single frame of sensor measurements. In addition, we design an estimator based on the error-state Kalman filter (ESKF) to enhance system accuracy and robustness. When multiple neighbors are available, a Pose Graph Optimization (PGO) algorithm is applied to further improve system accuracy. We conduct enormous experiments to demonstrate CREPES’ accuracy between robot pairs and a team of robots, as well as performance under challenging conditions.

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BEV2PR: BEV-Enhanced Visual Place Recognition with Structural Cues

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

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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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