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Chinese automation Congress (CAC)

作者： Tianhe Liu Xin Chen Chenyu Hu Xu Jian Shengzu Li Gang Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Equipment Technology Center State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan China Distribution Department State Grid Hubei Electric Power Co. Ltd. Wuhan Power Supply Company China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate identification and position of transmission lines is critical for robots to safely and effectively perform distribution network operations. However, such tasks are often challenged by complex lighting conditions, where depth information can be easily lost. Transmission lines have limited texture features and reflective surfaces, making it difficult to match image feature points. To address these challenges, this paper proposes a visual perception system for weak texture targets under complex lighting, based on an “eye-on-hand” configuration and a depth complementation algorithm. Furthermore, a high-precision identification and localization method for transmission line grasping points is developed, utilizing the fusion of binocular visual color maps and depth maps. The proposed method has been shown to effectively solve the problem of identifying and localizing the grasping points of transmission lines under complex lighting conditions. Experiments demonstrated that the method can improve the localization accuracy to within $\pm 4\text{mm}$ , outperforming other algorithms. The method is characterized as simple and efficient, and it enhances the environmental adaptability of distribution network robots.

关键词： Location awareness Visualization Power transmission lines Accuracy Image color analysis Lighting Distribution networks Grasping Wire Robots

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Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

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Journal of Central South University 2021年第10期28卷 3159-3172页

作者： LIU Wei-heng ZHENG Xin DENG Zhi-hong State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China China Aerospace Science and Industrial Corporation Beijing 100048China

Cooperative path planning is an important area in fixed-wing UAV ***,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic ***,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local ***,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is ***,several simulations are carried out to illustrate superiority and effectiveness.

关键词： fixed-wing UAV swarm cooperative path planning normalized artificial potential field dynamic obstacle avoidance local optimization

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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An-Improved $\alpha-\mathbf{Shapes}$ Algorithm for Solving Collaborative Workspaces of Dual SCARA Robots Based on Monte Carlo Method

An-Improved $\alpha-\mathbf{Shapes}$ Algorithm for Solving C...

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Chinese control Conference (CCC)

作者： Feng-Yu Zhang Gang Yu Shu-Han He Wang-Yong He School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China Huaxin Cement Technology Management (Wuhan) Co. Ltd. Wuhan P. R. China

Given the dual robots play a key role for collaborative work. In this paper, an improved method is proposed to find the kinematic positive solution based on its D-H parameters, model the dual SCARA robot in MATLAB environment, and solve the collaborative workspace point cloud map by Monte Carlo method. The proposed method extracts and plots the boundary curves of the collaboration space by improved the $\alpha-\text{Shapes}$ algorithm, and obtains the shape of the common region of the collaboration space of the dual SCARA robots and the limit position parameters in the collaboration space. It provides a theoretical basis for the research of path planning, trajectory planning, dynamics analysis, mechanism parameter optimization, self-avoidance, and anti-collision problems of the dual-robot collaborative system.

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Motion control Strategy Based on Integrated Trajectory for the Pendubot

Motion Control Strategy Based on Integrated Trajectory for t...

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European control Conference (ECC)

作者： Lejun Wang Xuzhi Lai Pan Zhang Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

The control objective of a Pendubot is always to swing its endpoint up from the vertical downward equilibrium point (VDEP) and to stay it at the vertical upward equilibrium point (VUEP). This paper proposes a motion control approach based on the integrated trajectory to achieve the above control objective with only one controller. First, an integration trajectory with two parts is designed for the Pendubot. We plan the first part trajectory with variable parameters, and optimize these variable parameters by applying the genetic algorithm (GA) to make all links move simultaneously from their starting states to their target states. We plan the second part trajectory to make all links remain at the target states. Then, we build the linear time-varying error system based on the integration trajectory, and design a global tracking controller by using the linear system control method to track the integration trajectory. By this way, the control objective of the Pendubot is realized. Finally, the simulations illustrate the validity and superiority of the proposed method.

关键词： Linear systems Target tracking Torque Simulation Europe control systems Trajectory

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Modeling and Tracking control for Dielectric Elastomer Actuator with Sliding Mode Feedback controller

Modeling and Tracking Control for Dielectric Elastomer Actua...

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IEEE International Conference on control Science and systems Engineering (CCSSE)

作者： Yue Zhang Peng Huang Pan Zhang Yawu Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

This paper proposes the modeling and tracking control methods for the dielectric elastomer actuator (DEA). A dynamic model of the DEA is built, which is composed of an asymmetric hysteresis model, a creep model and a linear system. To compensate the hysteresis and creep nonlinearities of the DEA, an inverse compensation feedforward controller (ICFC) is designed based on the creep model and the inverse of the asymmetric hysteresis model. Besides, an extended state observer (ESO) is designed to estimate the unmeasurable state of the DEA, as well as uncertainties including the modeling errors, parameter perturbations and external disturbances. Then, a sliding mode feedback controller (SMFC) is designed cooperating with the ICFC to realize the high precision tracking control of the DEA. The convergence of the ESO and the whole control system are proved through the Lyapunov method. Finally, some experiments are executed to verify the effectiveness of the proposed modeling and control methods.

关键词： Linear systems Uncertainty Creep control systems Modeling Adaptive control Feedforward systems

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control

Trajectory Tracking for Directional Drilling in Underground ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Jiajun Wu Chengda Lu Hao Li Hongchao Wei Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China China Coal Research Institute Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the $H_{\infty}$ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment.

关键词： Drilling Uncertainty Trajectory tracking Stability criteria Predictive models Taylor series Coal mining Trajectory Thermal stability Predictive control

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Design of Recursive Filtering Scheme via An Improved Weighted Try-Once Discard Protocol

Design of Recursive Filtering Scheme via An Improved Weighte...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Kaiwen Zheng Jun Hu Junhua Du Hui Yu Department of Applied Mathematics Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

This paper investigates the recursive filtering (RF) problem for stochastic multi-rate (MR) systems, where the information transmission is regulated by an improved weighted try-once-discard protocol (IWTODP). In order to reduce communication overhead and mitigate network congestion, the IWTODP is firstly proposed and designed to schedule the order of data transmission from the sensors. The main objective of this paper is to design a new RF scheme to minimize the upper bound (UB) on the filtering error (FE) covariance in each iteration step in the presence of IWTODP and MR sampling. In particular, an iterative way is given to provide the expression form of the gain of RF. Finally, the effectiveness of the proposed RF method is illustrated through simulation example.

关键词： Radio frequency Schedules Protocols Upper bound Filtering Filtering algorithms Iron Sensor systems Data communication State estimation

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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Dual-mode FMF to SOI chip edge coupler

Dual-mode FMF to SOI chip edge coupler

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Weilun Zhang Mengyuan Ye Yunlong Li School of Automation China University of Geosciences Wuhan China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Dual-mode edge coupler for few mode fiber (FMF) to silicon chip is proposed. LP 01 and LP 11 d coupler in simulation.

ISBN: (纸本)9781665410953

Dual-mode edge coupler for few mode fiber (FMF) to silicon chip is proposed. LP 01 and LP 11 d coupler in simulation.

关键词： Multiplexing Couplings Simulation Optical fiber couplers Crosstalk Couplers Silicon

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