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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhen Li Yawen Deng Qiang Ye Weihong Yu Haoxiang Qi Yaliang Liu Zhangguo Yu Gui-Bin Bian School of Electronic and Information Engineering Tongji University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China School of Mechatronic Engineering Beijing Institute of Technology Beijing China Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS),’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99.27% for instruments and macular holes and an average accuracy of 98.97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

关键词： Image segmentation Visualization Accuracy Medical robotics Motion segmentation Surgery Lighting

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TDCNet: Transparent Objects Depth Completion with CNN-Transformer Dual-Branch Parallel Network

arXiv

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arXiv 2024年

作者： Fan, Xianghui Ye, Chao Deng, Anping Wu, Xiaotian Pan, Mengyang Yang, Hang Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China University of Chinese Academy of Sciences Beijing101408 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin Institute of Technology Harbin150001 China College of Physics Northeast Normal University Changchun130024 China

The sensing and manipulation of transparent objects present a critical challenge in industrial and laboratory robotics. Conventional sensors face challenges in obtaining the full depth of transparent objects due to the refraction and reflection of light on their surfaces and their lack of visible texture. Previous research has attempted to obtain complete depth maps of transparent objects from RGB and damaged depth maps (collected by depth sensor) using deep learning models. However, existing methods fail to fully utilize the original depth map, resulting in limited accuracy for deep completion. To solve this problem, we propose TDCNet, a novel dual-branch CNN-Transformer parallel network for transparent object depth completion. The proposed framework consists of two different branches: one extracts features from partial depth maps, while the other processes RGB-D images. Experimental results demonstrate that our model achieves state-of-the-art performance across multiple public datasets. Our code and the pre-trained model are publicly available at https://***/XianghuiFan/TDCNet. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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Adaptive learning controller design for uncertain nonlinear active suspension system with actuator input delay and time-varying state constraints

Adaptive learning controller design for uncertain nonlinear ...

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第33届中国控制与决策会议

作者： Huijun Guo Jintao Liang Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Guilin University of Electronic Technology

This paper investigates the adaptive tracking control problem for active suspension system(ASS) with time-varying constraints via the neural network(NN) *** nonlinear terms in the spring and damper coefficients,input time delay of the actuator and the suspension performances are also considered *** NNs are used to approximate the unknown function caused by the uncertain car-body *** is known to all that ensuring the vertical displacement and speed states in the constraint regions can guarantee the stability and safety of the ***,by introducing the symmetric barrier lyapunov function from the time-varying constraints,an adaptive controller based on the NNs and nonlinear backstepping technique is developed to guarantee the states within *** the stability of the whole system,including the semi-global ultimately uniformly boundedness of the all signals of the ASSs,is analyzed by the Lyapunov stability ***,simulation results are provided to verify the feasibility of the adaptive learning controller.

关键词： Active suspension systems adaptive control input delay neural networks

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Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

arXiv

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arXiv 2021年

作者： Yin, Hui-Ping Ren, Hai-Peng Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China

To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel. Copyright © 2021, The Authors. All rights reserved.

关键词： Bit error rate

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A Cell Potential and Motion Pattern Driven Multi-robot Coverage Path Planning Algorithm 14th

A Cell Potential and Motion Pattern Driven Multi-robot Cover...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Xu, Meng Xin, Bin Dou, Lihua Gao, Guanqiang School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811534249

This paper proposes an intelligent "Cell Potential and Motion Pattern driven Coverage (CPMPC)" algorithm to solve a cooperative coverage path planning problem for multiple robots in two-dimensional target environment. The target environment is divided into cell areas according to the detection range of robot, and the cell matrix is given correspondingly. The values in the cell matrix are defined as cell potential, which represents the number of times each cell is detected by robots. The priority of the robot’s neighbor cell is called the motion pattern. At different moments, robots can choose within different motion patterns. Genetic algorithm (GA) is used to optimize the combination of motion patterns. By taking account obstacle avoidance and collision avoidance into consideration, the CPMPC algorithm adopts a double-layer choice strategy driven by cell potential and motion pattern to generate the next waypoint. Furthermore, this algorithm contains two optimal strategies: avoiding collision and jumping out of the detected area. Compared with the pattern-based genetic algorithm, the results obtained by us show that the CPMPC algorithm could solve the multi-robot coverage path planning (MCPP) problem effectively with guarantee of complete coverage, and improved makespan. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multipurpose robots

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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Chinese control Conference (CCC)

作者： YiFei Wang Tao Wu 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 System Integration and Industrial Robotics Laboratory Wuhan P. R. China

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor; (2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks

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SN Computer Science 2023年第6期4卷 1-12页

作者： Kumar, Amit Krishan Mai, Nguyễn Ngọc Tian, Ke Xia, Yuanqing Institute of Research and Development Duy Tan University Da Nang Vietnam Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Faculty of Chinese Language School of Foreign Language Duy Tan University Da Nang Vietnam State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Machine learning, classification, and clustering techniques use the distance functions to evaluate the proximity between data entries and deduce the best neighbouring element and the closest matching entry. The best neighbour is not only the closest neighbour but a neighbour that is quick to respond. In view of that, a time-based isochronous metric is introduced to evaluate the best neighbours and form linkages by grouping similar entities. The proposed method uses parametric equations of the fastest descent and solves the time variables for attributes localised in curved space–time. The time metric is compared with commonly used distance metrics for accuracy in classification and clustering using benchmark and commonly used datasets. The nearest-neighbour technique is used for evaluating classification accuracy, and an adjusted random index (ARI) is used to evaluate clustering. The proposed method shows better accuracy and ARI in comparison to distance functions. It also assigns better weights to attributes of the dataset and easily identifies repeated patterns in noisy time series data.

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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Chinese control Conference (CCC)

作者： Zixiang Zhou Tao Wu 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 System Integration and Industrial Robotics Laboratory Wuhan P. R. China

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

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Parametric Transformation of Timed Weighted Marked Graphs:Applications in Optimal Resource Allocation

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 179-188页

作者： Zhou He Ziyue Ma Zhiwu Li Alessandro Giua the College of Mechanical and Electrical Engineering Shaanxi University of Science and TechnologyXi’an 710021China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China the School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Institute of Systems Engineering Macao University of Science and TechnologyTaipa 999078MacaoChina the Department of Electrical and Electronic Engineering University of CagliariCagliari 09124Italy

Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing *** to the existence of multiplicities(i.e.,weights)on edges,the performance analysis and resource optimization of such graphs represent a challenging *** this paper,we develop an approach to transform a timed weighted marked graph whose initial marking is not given,into an equivalent parametric timed marked graph where the edges have unitary *** order to explore an optimal resource allocation policy for a system,an analytical method is developed for the resource optimization of timed weighted marked graphs by studying an equivalent ***,we apply the proposed method to a flexible manufacturing system and compare the results with a previous heuristic *** analysis shows that the developed approach is superior to the heuristic approach.

关键词： Discrete event system resource allocation timed Petri net weighted marked graph

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Coordinated Motion control for a Six-wheel-drive Skid-steering Mobile Robot

Coordinated Motion Control for a Six-wheel-drive Skid-steeri...

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Chinese Automation Congress (CAC)

作者： Yongkang Xu Shoukun Wang Hongqiang Zhao Zhihua Chen State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China State Key Laboratory of High Performance and Complex Manufacturing Central South University Changsha China

ISBN: (纸本)9781665426480

A speed consensus control strategy for a six-wheel-drive skid-steering mobile robot was proposed based on a hierarchical controller in a complex environment with unknown disturbances. The effective method of reliable speed consensus control strategy should consider some disturbances, such as the internal-robot assembly error, which including instability of motor control system and external physical interference in robot dynamic system. In this paper, a driving anti-slip control system with PID and the stability control system with sliding mode by variable structure is designed and integrated to elevate the stability of the robot. And, a G-vector control algorithm based on sliding mode control is proposed to reduce the yaw angle as much as possible during the process of differential steering of robots. The effectiveness of the proposed control strategy is verified by an example of a robot. The results show that the method has good control performance in control accuracy and stability.

关键词： Uncertainty Robot kinematics Heuristic algorithms Velocity control Torque control Wheels Consensus control

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