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Chinese control Conference (CCC)

作者： Tairen Sun Liangliang Li Yuebang He School of Electrical and Information Engineering Jiangsu University Zhenjiang China School of Computational Science Zhongkai University of Agriculture and Engineering Guangzhou China Key Laboratory of Autonomous Systems and Networked Control Ministry of Eduction South China University of Technology Guangzhou China

ISBN: (纸本)9781467325813

In this paper, we consider the environmental boundary tracking problem where the objective is to steering a nonholo-nomic mobile robot to track the desired environmental boundary. Based on kinematic model of the mobile robot, we design a kinematic control law for the robot to track the desired boundary. Kinematic control law needs perfect velocity tracking, which doesn't always hold for mobile robot. So, dynamic control law is designed for the nonholonomic mobile robot to track a designated reference velocity, so as to track the desired environmental boundary. The stability of the designed control laws is verified by Lyapunov theory and Barbalat's Lemma and illustrated by simulation results.

关键词： Mobile robots Robot kinematics Kinematics Robot sensing systems Vectors Pollution measurement

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Dynamic Multi-Robot Path Planning in Narrow-Lane Environments with One-Way Constraint

Dynamic Multi-Robot Path Planning in Narrow-Lane Environment...

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International Conference on Intelligent Autonomous systems (ICoIAS)

作者： Zhenyu Song Ronghao Zheng Senlin Zhang Meiqin Liu College of Electrical Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xian China

With the rapid development of the logistics industry, the path planning problem for automated guided vehicles (AGVs) in warehousing systems has become a prominent research topic. Traditional multi-robot path planning algorithms often perform poorly in narrow-lane environments found in warehouses. This paper proposes an improved dynamic multi-robot path planning algorithm specifically tailored for narrow-lane environments. By constraining the traversal directions within narrow lanes and dynamically determining the optimal directions, this algorithm achieves efficient multi-robot path planning and enhances the quality of robot paths. The effectiveness of the algorithm is theoretically proven in this paper. Extensive simulation data demonstrates that our algorithm efficiently generates high-quality paths in high-density narrow-lane warehouse environments.

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Dimension Reduction for Dynamic Analysis of Gene Expression Data Using Variational Autoencoder

Dimension Reduction for Dynamic Analysis of Gene Expression ...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Shuai Yuan Shipeng Chen Yongliang Yang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning China

Single-cell RNA sequencing technology captures gene expression at the whole-genome scale and single-cell level in many cells. Thus, the cellular states during a physiological process would be captured in detail. These data provide new opportunities and challenges for systems biology. Previously, the models in systems biology dealt with the dynamics of specific signaling pathways. However, it is almost impossible to directly model the dynamics of the whole genome due to the ultrahigh dimensionality of the data. Here, to model its gene regulatory dynamics, the dimensionality of the whole genome was reduced significantly using the latent space of a variational autoencoder, a generative deep learning model. In the low-dimensional latent space, an ordinary differentiation equation (ODE) system has been established to analyze the dynamic characteristics of the whole genome system. To demonstrate the effectiveness of this method, we generated a low-dimensional representation of a systems biology model we previously developed. The variational autoencoder reduced the dimension from 13 to 2 in latent space. An ODE model was developed to analyze the dynamics of this two-dimensional system. The results indicated that the 2-dimensional model captured the essence of the original systems biology model.

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A Comparison of Differential Kinematics and Machine Learning Approaches in Motion Planning for Intra-operative Cardiac Ultrasound Robots 20

A Comparison of Differential Kinematics and Machine Learning...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Lin, Haichuan Xie, Yiping Hou, Xilong Chen, Chen Wang, Shuangyi Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems 95 Zhongguancun East Road Beijing China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences School of Artificial Intelligence 19 Yuquan Road Beijing China

ISBN: (纸本)9798350320831

Intra-operative cardiac ultrasound robots are emerging as a new tool to assist in cardiac interventional procedures to improve the efficiency of the procedure and reduce the operator's experience. These robots are often based on a continuum mechanism to achieve end position changes. The central issue for the control of these robots is motion planning. With its flexibility, machine learning-based approaches may provide new ideas for this type of problem compared to traditional differential kinematics, although related research is limited. In this paper, we present specific approaches to achieve the corresponding tasks by differential kinematics, supervised learning and reinforcement learning for specific motion planning problems, and thus analyze the feasibility of different approaches and compare their performance. The results show that differential kinematics is able to perform the best considering all tasks, while both supervised learning and reinforcement learning methods can also accomplish the intended task within a certain error range and with different characteristics. To conclude, the work lays the foundation for further flexibility in solving more complex tasks in motion planning using the combination of conventional kinematics and machine learning-based methods. © 2023 IEEE.

关键词： Ultrasonics

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A Novel IMU-Based 6-DOF Pose Tracking Method for Parallel-Mechanism-Based Ultrasound Robot 20

A Novel IMU-Based 6-DOF Pose Tracking Method for Parallel-Me...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Deng, Zhaokun Hou, Xilong Zhang, Pengcheng Hao, Mingrui Chen, Chen Wang, Shuangyi Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems 95 Zhongguancun East Road Beijing China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Science Park Hong Kong University of Chinese Academy of Sciences School of Artificial Intelligence 19 Yuquan Road Beijing China

ISBN: (纸本)9798350320831

Parallel-mechanism-based ultrasound robots bring the benefits of convenient robot-assisted remote ultrasound or automated scanning. During the use of this type of robot, accurate tracking and feedback of 6-DOF posture are critical for potential intelligent control and real-time navigation. However, this presents significant challenges due to the complex forward kinematics and environmental changes, making it difficult to apply in practical settings. To address this issue, we propose a novel 6-DOF pose tracking method based on an inertial measurement unit. This method directly senses the orientation and calculates the position using the intrinsic structural and kinematic characteristics of the parallel mechanisms. Therefore, it is possible to solve all posture information by only partial posture information. We conducted both simulation and physical experiments to evaluate effectiveness and robustness of the proposed method. The maximal tracking errors for the physical experiments are within the 2 mm and 0.6.°. The experimental results show promising performance, demonstrating the method's potential for practical applications. © 2023 IEEE.

关键词： Ultrasonics

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FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting

arXiv

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arXiv 2024年

作者： Li, Ruiqi Jiang, Maowei Liu, Quangao Wang, Kai Feng, Kaiduo Sun, Yue Zhou, Xiufang Key Laboratory of Networked Control Systems Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shenyang University of Technology School of Information Science and Engineering Shenyang110870 China

Time Series Forecasting (TSF) plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. However, despite their considerable advantages over traditional statistical approaches, current deep learning-based predictive models often exhibit a significant deviation between their forecasting outcomes and the ground truth. This discrepancy is largely due to an insufficient emphasis on extracting the sequence’s latent information, particularly its global information within the frequency domain and the relationship between different variables. To address this issue, we propose a novel model—Frequency-domain Attention In Two Horizons (FAITH), which decomposes time series into trend and seasonal components using a multi-scale sequence adaptive decomposition and fusion architecture, and processes them separately. FAITH utilizes Frequency Channel feature Extraction Module (FCEM) and Frequency Temporal feature Extraction Module (FTEM) to capture inter-channel relationships and temporal global information in the sequence, significantly improving its ability to handle long-term dependencies and complex patterns. Furthermore, FAITH achieves theoretically linear complexity by modifying the time-frequency domain transformation method, effectively reducing computational costs. Extensive experiments on 6 benchmarks for long-term forecasting and 5 benchmarks for short-term forecasting demonstrate that FAITH outperforms existing models in many fields, such as electricity, weather and traffic, proving its effectiveness and superiority both in long-term and short-term time series forecasting tasks. © 2024, CC BY.

关键词： Linear transformations

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Cooperative-Manipulation Scheme of Routh-Hurwitz Type for Simultaneous Repetitive Motion Planning of Two-Manipulator Robotic systems

Cooperative-Manipulation Scheme of Routh-Hurwitz Type for Si...

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第28届中国控制与决策会议

作者： Yunong Zhang Maotai Zou Huinan Xiao Jianfeng Wen Ying Wang School of Information Science and Technology Sun Yat-sen University(SYSU) SYSU-CMU Shunde International Joint Research Institute Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Department of Computer Science Guangdong Polytechnic Normal University

ISBN: (纸本)9781467397155

In this paper,a novel task-level cooperative-manipulation scheme is proposed for the simultaneous repetitive motion planning(SRMP) of two-manipulator robotic ***,to remedy the two-manipulators' jointangle drift phenomenon,a Routh-Hurwitz type performance index is designed and exploited for the first *** Routh-Hurwitz type subschemes are thus generated for the left and right robot manipulators ***,the two subschemes are unified as one *** analysis based on Routh-Hurwitz criterion is presented to show the feasibility and effectiveness of the proposed performance ***,the unified scheme is reformulated as a quadratic program(QP) to be solved in real ***,computer simulations based on two-manipulator robotic systems substantiate the effectiveness and accuracy of the proposed cooperative-manipulation scheme for SRMP.

关键词： Simultaneous Repetitive Motion Planning Two-Manipulator Robotic System Performance Indices Routh-Hurwitz Criterion Quadratic Program

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Chaotic oscillator with potentials in TRNG and ADC

Chaotic oscillator with potentials in TRNG and ADC

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Qingdu Li Qifeng Liu Junli Niu Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications China and Institute for Nonlinear Circuits and Systems Chongqing University of Posts and Telecommunications Chongqing Chongqing China Institute for Nonlinear Circuits and Systems Chongqing University of Posts and Telecommunications Chongqing China

We report a novel chaotic oscillator, which not only can generate true random binaries directly, but also give rise to a new potential application of chaotic oscillators working as an analog-to-digital converter (ADC). The result binary sequence and its reference clock can be generated automatically. The effectiveness is verified by simulations with Multisim.

关键词： Chaotic communication Oscillators Clocks Generators Analog-digital conversion Nonlinear circuits

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Learning to Manipulate Tools Using Deep Reinforcement Learning and Anchor Information

Learning to Manipulate Tools Using Deep Reinforcement Learni...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Wei, Junhang Cui, Shaowei Hao, Peng Wang, Shuo The School of Future Technology University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Chinese Academy of Sciences Beijing100049 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

Endowing robots with tool manipulation skills helps them accomplish challenging tasks. While robots manipulate tools to achieve goals, the alignment of tools and targets is a noise-sensitive and contact-rich task. However, it is difficult to access the accurate pose of the tool and the target. When there is unknown noise in the observations, reinforcement learning can't be sure to perform well. In this paper, we define the easier-to-obtain accurate task-related information as anchor information and introduce a tool manipulation method based on reinforcement learning and anchor information, which can perform well when the observations include unknown noise. To evaluate the method, we build a simulated environment ToolGym, which includes four different kinds of tools and different noise sampling functions for each tool. Finally, we compare our method with baseline methods to show the effectiveness of the proposed method. © 2022 IEEE.

关键词： Reinforcement learning

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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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