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arXiv 2025年

作者： Yao, Tianliang Ban, Madaoji Lu, Bo Pei, Zhiqiang Qi, Peng Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electrical and Electronic Engineering Faculty of Engineering The University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Jiangsu Suzhou China School of Oriental Pan-Vascular Devices Innovation College University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes. © 2025, CC BY.

关键词： Reinforcement learning

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Stochastic stability of evolutionary prisoner's dilemma

Stochastic stability of evolutionary prisoner's dilemma

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2018 Australian and New Zealand control Conference, ANZCC 2018

作者： Liang, Haili Zhou, Zhao Zhang, Fan Peng, Chen Wang, Yu-Long Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Mathematics Southeast University Nanjing China

ISBN: (纸本)9781538666173

In this paper, we study two-player evolutionary prisoner's dilemma on regular graphs and identify the stochastically stable equilibria for infinite populations. We consider four different update rules: Birth-death(BD), death-birth(DB), imitation(IM) and pairwise comparison(PC). With the same values of cost and benefit of cooperation, we show that there is a unique stochastically stable equilibrium for evolutionary prisoner's dilemma on regular graphs. If the benefit-to-cost ratio is larger than k + 2 (k is the degree of a regular graph), the networked game has a higher fraction of cooperators than that for a well-mixed population. Under certain conditions, the lower graph connectivity can lead to the emergence of more cooperators. Besides theoretical analysis, we demonstrate our results through numerical computations and simulations as well. © 2018 IEEE.

关键词： Stochastic systems

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Development of a Tetris playing robot controlled by KNR 15

Development of a Tetris playing robot controlled by KNR

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15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Wang, Ming Zhang, Yanlu Dong, Huifang Li, Xu Yu, Junzhi Department of Information and Electrical Engineering University of Shandong Jianzhu University Jinan250100 China Department of Intelligent Robotics State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538660720

Robot plays an increasingly important role in daily life. This paper presents a vision-based wheeled robot for playing Tetris. How to control the robot is elaborated at length in this paper. The robot adopted a KNR controller as its core controller. With a CCD sensor, it identified colored blocks, lines and the destination. All algorithms are programed with LabVIEW. The experiments and the contest validated the effectiveness of the proposed vision-based wheeled robot system. © 2018 IEEE.

关键词： controllers

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Human Motion Prediction Based on Visual Tracking 4

Human Motion Prediction Based on Visual Tracking

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4th International Conference on robotics and Automation Engineering, ICRAE 2019

作者： Zou, Juncheng Yin, Weihua Wang, Everett X. Wang, Jiancheng Lu, Yan-Feng School of Mechanical Engineering Beijing University of Science and Technology China Institute of Science and Technology Innovation Dongguan University of Technology China School of Information Engineering Guangdong University of Technology Guangzhou China Cloud Computing Center Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728147406

ISBN: (纸本)9781728147406

Following and moving according to human motion is an important task for mobile robots. To ensure more compliant motion planning and execution of mobile robots, they can not be controlled by real-time visual information. In the tracking process, robots need to recognize and track the target then, plan the motion and execute the motion. In practical applications, the environments are very complex, such as illumination, shadows and occlusion, which the traditional visual tracking algorithms often deviate or lose the target. Therefore, to achieve fast human motion tracking, it is necessary to predict human motions by video prediction. In this paper, we propose a human motion tracking algorithm of mobile robot based on video prediciton. (1) The multi-layer generation adversarial loop network, which trained by off-line video dataset and learn how to predict human motion. (2) We used the pre-trained model to predict the state of the tracking target in the video. (3) This video prediction model was integrated into human-to-robot tracking algorithms of mobile robot following human system. Experiments show that the proposed algorithm can track human motion at a certain speed and precision. © 2019 IEEE.

关键词： Mobile robots

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Walking of Biped Robot with Variable Stiffness at the Ankle Joint*

Walking of Biped Robot with Variable Stiffness at the Ankle ...

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IEEE International Conference on robotics and Biomimetics

作者： Qing Bi Yixiang Liu Xizhe Zang Rui Song Haibin Wang Weimin Li Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan China School of Control Science and Engineering Shandong University Jinan China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Achieving versatile and efficient walking on biped robots is an important research objective. The impact between the foot and the ground has significant effects on the walking stability of biped robots. Research on human walking indicates that the joints in human lower limbs show different stiffness values in different phases of walking. Inspired by humans, this paper presents the realization of biped robot walking with variable stiffness at the ankle joint. A walking control scheme which combines PD control and variable impedance control is designed for the biped robot. Results of dynamic walking simulation verified that variable stiffness at the ankle joint could contribute to the improvement of walking stability and energy efficiency.

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Broad learning system based on maximum correntropy criterion

arXiv

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arXiv 2019年

作者： Zheng, Yunfei Chen, Badong Wang, Shiyuan Wang, Weiqun Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China College of Electronic and Information Engineering Southwest University Chongqing400715 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems. However, the standard BLS is derived under the minimum mean square error (MMSE) criterion, which is, of course, not always a good choice due to its sensitivity to outliers. To enhance the robustness of BLS, we propose in this work to adopt the maximum correntropy criterion (MCC) to train the output weights, obtaining a correntropy based broad learning system (C-BLS). Thanks to the inherent superiorities of MCC, the proposed C-BLS is expected to achieve excellent robustness to outliers while maintaining the original performance of the standard BLS in Gaussian or noise-free environment. In addition, three alternative incremental learning algorithms, derived from a weighted regularized least-squares solution rather than pseudoinverse formula, for C-BLS are developed. With the incremental learning algorithms, the system can be updated quickly without the entire retraining process from the beginning, when some new samples arrive or the network deems to be expanded. Experiments on various regression and classification datasets are reported to demonstrate the desirable performance of the new methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

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An Abnormal state Forecasting Method for Substation Equipment based on Improved LSTM

An Abnormal State Forecasting Method for Substation Equipmen...

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Sustainable Power and Energy Conference (iSPEC)

作者： Tong Li Shimao Yu Chunhe Song Tongwei Yu Peng Zeng Li Shen Liaoning Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China State Grid Liaoning Electric Power Co. Ltd. Shenyang China

Abnormal state accumulation over a long period will cause an electrical equipment fault. Therefore, Substation equipment state forecasting plays a vital role in smart grids. Fault forecasting method based on deep learning have been put forward in recent years, but they have deficiencies dealing with temporal features. This paper proposes an abnormal forecasting method for substation equipment based on improved LSTM, and temporal features of power equipment fault can be extracted effectively for a long-time span. The method reads secondary equipment information through SCADA system, then extracts feature to carry out deep learning training. In this paper, RNN and LSTM are firstly compared, and then the structure and algorithm of improved LSTM are given. This method increases coupling between forget gate and cell state compared with the standard LSTM structure, so as to make better use of information at previous moment. Meanwhile problems of network training and overfitting are discussed. Experiments are conducted to demonstrate that improved LSTM is capable of forecasting accurately the complex electric equipment state with a long forecasting horizon. The accuracy of abnormal state forecasting can reach more than 93% which has a better performance compared with many other methods.

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Robust converter-fed motor control based on active rejection of multiple disturbancesI

arXiv

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arXiv 2020年

作者： Madonskia, Rafal Lakomy, Krzysztof Stankovic, Momir Shao, Sally Yang, Jun Li, Shihua Energy Electricity Research Center International Energy College Jinan University 206 Qianshan Road ZhuhaiGuangdong519070 China Institute of Automation and Robotics Poznan University of Technology Poznan Poland Military Academy University of Defense Belgrade Serbia Department of Mathematics Cleveland State University ClevelandOH United States School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Nanjing China

In this work, an advanced motion controller is proposed for buck converter-fed DC motor systems. The design is based on an idea of active disturbance rejection control (ADRC) with its key component being a custom observer capable of reconstructing various types of disturbances (including complex, harmonic signals). A special formulation of the proposed design allows the control action to be expressed in a concise and practically appealing form reducing its implementation requirements. The obtained experimental results show increased performance of the introduced approach over conventionally used methods in tracking precision and disturbance rejection, while keeping similar level of energy consumption. A stability analysis using theory of singular perturbation further supports the validity of proposed control approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Disturbance rejection

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Obstacle avoidance method of robot based on admittance control

Obstacle avoidance method of robot based on admittance contr...

引用

2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Hu, Hongshuang Jiang, Yong Tan, Xiaowei School of Information and Control Engineering Shenyang Jianzhu University Shenyang Liaoning Province China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning Province China

ISBN: (纸本)9781538680698

Instead of traditional ranging radar and camera, Kinect was used to ensure the robot can avoid obstacles in the process of movement. And an obstacle avoidance method of robot was proposed based on admittance control. The relative distance between the robot and human hand was real-time measured by Kinect sensor and converted into a virtual force, acting as an input of the admittance controller to control the next moment speed of the controller to avoid obstacles. Then a modification algorithm for the robot's speed direction was added on the basis of admittance control, when the operator's arm is moving at a high speed, to control the robot's avoid obstacles urgently. At the same time, a method of singular transition to the joint space was proposed to solve the problem that once the robot encountering strange points in Cartesian space it will out of control. Finally, a physical experiment platform based on Kinect sensor and UR robot was set up to conduct dynamic and static obstacle avoidance experiments. The results verified the effectiveness of the proposed method. © 2018 IEEE.

关键词： Human computer interaction

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A Novel Assist-As-Needed controller Based on Fuzzy-Logic Inference and Human Impedance Identification for Upper-Limb Rehabilitation

A Novel Assist-As-Needed Controller Based on Fuzzy-Logic Inf...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Chen Wang Liang Peng Zeng-Guang Hou Weiqun Wang Tingting Su State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Department of Mechanical and Electrical Engineering North China University of Technology Beijing P.R. China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Upper-limb rehabilitation robots are being increasingly included in the rehabilitation after stroke. Clinical studies suggest that the patient's active participation can maximize effectiveness of robot-assisted rehabilitation and result in fastest possible recovery. In order to promote active involvement of the patient, we develop a subject-adaptive controller within the assist-as-needed (AAN) paradigm. The controller employs Recursive Least Square (RLS) algorithm to identify human arm impedance parameters, with aims to quantify the residual motor capability of the patient. According to the upper-limb impedance of the patient in the direction along movements, the reference trajectory can be generated based on the motion patterns in healthy humans. In addition, the fuzzy logic control strategy is implemented in the direction perpendicular to movements, which is capable of adjusting the robotic assistance level by considering both upper-limb impedance of the patient and the variations of deviation between the reference trajectory and actual trajectory. Furthermore, the proposed subject-adaptive AAN controller was validated in the simulation study, and the results demonstrated the capability of the controller to modulate the assistance level in accordance with the subject's instantaneous requirements. Future works will focus on the implementation of the control scheme with post-stroke patients.

关键词： Impedance Trajectory Fuzzy logic Training Rehabilitation robotics Task analysis

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