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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Zhang, Xuyang Luo, Shan The Robot Perception Lab Centre for Robotics Research Department of Engineering King's College London LondonWC2R 2LS United Kingdom The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force labels. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force labels is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

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Vision-based Autonomous Landing of UAV on Dynamic Apron

Vision-based Autonomous Landing of UAV on Dynamic Apron

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IEEE International Conference on Unmanned systems (ICUS)

作者： Pengyu Yue Jing Xin Zhijie Mao Yongchang Lu Youmin Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an P.R.China Department of Mechanical Industrial & Aerospace Engineering Concordia University Montreal QC Canada

Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obtained from the bottom visual sensor of the UAV, and the YOLOV5 object detection method is employed to determine the initial position of the dynamic apron. Subsequently, an improved dynamic apron tracking method based on Spatio-Temporal Transformer is designed. This method updates an initial template through target detection algorithm and utilizes multi-source perceptual data for confidence evaluation and dynamic template updates. It outputs the distribution estimation of the predicted bounding box corner coordinates, and achieves the high reliability continuous positional smoothing estimation and dynamic apron loss re-detection based on UAV vision. Finally, the UAV calculates the horizontal control quantity based on the horizontal relative position between the UAV and the apron obtained. The vertical control quantity is calculated based on the vertical relative position between the UAV and the apron. The vertical relative position is estimated using laser ranging and visual odometry techniques. Based on the results of horizontal tracking control and vertical regulation, velocity commands for the UAV in all three directions are generated, enabling the UAV to achieve autonomous landing on the dynamic apron. This paper achieves precise landing for dynamic apron in real-world environments. The experimental results demonstrate that this method avoids the limitations of labeled data and global information on autonomous landing of UAV. Even under GPS-denied conditions, the UAV can generate control commands based on visual data and achieve autonomous landing for randomly moving apron.

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Single-Photon LiDAR Depth Estimation Based on Time Series Correlation

Single-Photon LiDAR Depth Estimation Based on Time Series Co...

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Academic International Symposium on Optoelectronics and Microelectronics Technology (AISOMT)

作者： Lin Ruobin Sun Jianfeng Jiang Peng Ma Le Zhou Xin Ren Zhen Ding Ji National Key Laboratory of Laser Spatial Information Harbin Institute of Technology Harbin Heilongjiang China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou Henan China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Tianjin Jinhang Technical Physics Institute Tianjin China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

With advancements in science and technology, photon-counting LiDAR applications are growing due to long-range measurement, high-definition imaging, and accuracy. Geiger-mode avalanche photodiodes (Gm-APD) provide single-photon sensitivity and picosecond resolution, but noise in depth estimation affects imaging accuracy. This study introduces a novel depth estimation method using time series correlation to mitigate these noise issues, improving accuracy in LiDAR imaging. Utilizing a photon-counting LiDAR system and time-correlated single photon counting (TCSPC), this study capture target echo photon flight times to maintain depth information. The proposed denoising approach incorporates multiple time series windows and Bayesian estimation to enhance depth accuracy, alongside a pruning algorithm to optimize computational efficiency. Evaluation metrics show significant improvements: RSNR improved by 4% and MAE decreased by 43 % compared to traditional methods. The findings demonstrate the robustness of this method across various scenarios, enhancing depth estimation and noise mitigation in photon-counting LiDAR technology, thereby providing practical improvements for a range of applications.

关键词： Correlation Laser radar Accuracy Filtering Depth measurement Time series analysis Noise reduction Imaging Bayes methods Photonics

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Multiplexed model predictive attitude control for nadir-pointing satellite using only magnetorquers

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control Engineering Practice 2025年 162卷

作者： Yang, Sen Ling, Keck Voon Wang, Zhenhua Li, Huiping School of Astronautic Harbin Institute of Technology Harbin150001 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin150001 China School of Marine Science and Technology Northwestern Polytechnical University Xi'an710072 China

Due to the low failure risk of magnetorquers, the control strategy based on magnetic actuators can significantly enhance the reliability of the satellite attitude control system. In light of this advantage, this paper conducts research on model predictive control (MPC) algorithm for nadir-pointing satellites equipped only with magnetorquers. Firstly, to facilitate controller design, the linear time-varying (LTV) model for nadir-pointing satellites is derived by linearizing the nonlinear equations around the operating point. Then, leveraging this linearized representation, an LTV multiplexed MPC (MMPC) method with exponential weighting is proposed. The MMPC scheme allows for faster sampling rates and quicker responses to disturbances, thereby enhancing the overall performance of the satellite attitude control system while retaining the benefits of MPC. Finally, compared with a state-of-the-art SMPC method, the proposed method is particularly advantageous in situations where computational resources are limited, specifically for satellites that rely only on onboard computing capabilities. © 2025

关键词： Nonlinear equations

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Path Tracking control of Tracked Paver Based on Improved Pure Pursuit Algorithm

Path Tracking Control of Tracked Paver Based on Improved Pur...

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第40届中国控制会议

作者： Shuai Wang Shanshan Fu Bin Li Shoukun Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

This paper investigates the high-precision path tracking control of tracked paver combined with global satellite navigation *** the paver is performing paving operations,it requires high path tracking accuracy and good vehicle ***,considering the influence of road curvature on path tracking accuracy and vehicle stability,and the situation that the vehicle can not move quickly to the expectation path when the lateral position of the vehicle deviates from the expectation path,this paper proposes a lateral path tracking control method based on improved Pure Pursuit *** control method is verified through *** experimental results show that the maximum lateral tracking error of the improved algorithm is 0.04 m,which is 55.56% lower than that of the original algorithm,and the average lateral tracking error is 0.02 m,which is60% lower than that of the original *** purpose of high-precision path tracking of the paver is realized.

关键词： Tracked paver Path tracking Road curvature Pure Pursuit algorithm

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Forecasting Approach in Fuzzy Time Series Based on Information Granules

Forecasting Approach in Fuzzy Time Series Based on Informati...

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第40届中国控制会议

作者： Kaixin Zhao Yaping Dai Ye Ji Jiayi Sun School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Aiming at a large number of ambiguous,imprecise and incomplete data in the real world,fuzzy time series has come into being and developed into an effective forecasting *** the process of modeling and forecasting of fuzzy time series,the prediction performance of fuzzy time series can be effectively improved by partitioning the universe of discourse into different *** this paper,a forecasting approach for fuzzy time series,which introduces the granularity mechanism into interval division and employs differential data for incremental forecasting,is proposed to solve the problem of time series forecasting with high forecasting *** the proposed approach,in order to describe the fuzzy logic relationship and fuzzy trend of historical data,we first do differential processing on the historical ***,Fuzzy C-means(FCM) clustering algorithm is used to generate several partition intervals *** the sequel,we use the principle of justifiable granularity to constantly adjust the width of all the intervals,so that these information granules associated with corresponding intervals become the most"informative" information ***,the boundary of information granules is used as the basis of interval division to complete the forecasting *** illustrative example is provided to demonstrate the essence of the proposed *** comparative experiment with other representative approaches shows that the proposed approach can significantly improve the prediction accuracy of time series.

关键词： Fuzzy Time Series Information Granules Unequal-sized Intervals Forecasting

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Dynamic Resource Scheduling Based on Improved Genetic Algorithm

Dynamic Resource Scheduling Based on Improved Genetic Algori...

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第40届中国控制会议

作者： Gui Yuanyuan School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

Aiming at the problem of how to realize the reasonable distribution of goods and the resource scheduling of route planning under dynamic conditions, a phased refresh method is proposed to deal with the requirements in sections. At the same time, the clustering method is introduced to generate the initial population. And the cross and mutation rate is determined by a step-by-step reduction method, which is used to solve the problem of the traditional genetic algorithm falling into the local optimum caused by the uneven distribution of searchable solutions. Finally, the effectiveness of the improved method is verified by simulation experiments.

关键词： resource scheduling phased refresh improved genetic algorithm

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High-Precision Admittance control of Mechanical Arm Based on Force Sensor Compensation

High-Precision Admittance Control of Mechanical Arm Based on...

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Chinese control and decision Conference, CCDC

作者： Yaohui Wu Jiangbo Zhao Junzheng Wang Xin Zhang Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing China Technical Center Xinxing Cathay International Group Co. Ltd. Beijing China

ISBN: (纸本)9781665478977

Admittance control is one of the important methods of compliance control. It is performed by inputting force signals and outputting position signals. Position control can achieve high accuracy, but the force signals have large errors, which is an important factor affecting its accuracy. When the force sensor collects force data, there are non-negligible initial deviations due to gravity, installation, and other factors, resulting in poor admittance control accuracy. In order to solve this problem, this paper takes the six-degree-of-freedom manipulator admittance control as the research object. According to the mapping relationship between the end attitude and the sensor errors, we propose a method of obtaining the initial offset value of the sensor through data interpolation. This method traverses the initial values of the sensor in each working posture when the sensor has no external force input, and then performs secondary interpolation to obtain the offset values of the sensor in any posture, corrects the output values of the force/torque sensor, and then improve the accuracy of admittance control. The experimental results show that this method can quickly and effectively improve the calculation accuracy of the sensor’s initial deviation values and the admittance control accuracy.

关键词： Interpolation Attitude control Force Dynamics Position control Robot sensing systems Real-time systems

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Deadbeat Predictive Current control of Permanent Magnet Synchronous Motors Based on Disturbance Compensation

Deadbeat Predictive Current Control of Permanent Magnet Sync...

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第33届中国控制与决策会议

作者： Wei Shen Huan Liu Luwei Shao Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology

The deadbeat predictive current control(DPCC) exhibits strong dynamic performance in the current control of permanent magnet synchronous motor(PMSM),whereas the performance of the traditional DPCC depends on the accuracy of model parameters to a great *** cope with this problem,parameter sensitivity of the traditional DPCC has been analyzed in detail,with a disturbance observation compensation method proposed to improve the robustness of the current control of ***,a method of non-singular terminal sliding mode disturbance observer(NTSMDO) which employs a novel reaching law,combined with the DPCC is put forward to enhance the parameters robustness of the current loop.A harmonic compensation(HC) term has been added to the output of the current control for ignoring the harmonic electromagnetic process in the mathematical model of the PMSM to further promote the performance of the current loop *** and experimental results demonstrate that the proposed method offers robustness against parameters mismatch and control precision compared with the traditional DPCC.

关键词： PMSM Predictive current control NTSMDO Parameter mismatch Harmonic compensation

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Q-Learning for Finite-Horizon H∞ Tracking control of Unknown Linear Discrete-Time systems

Q-Learning for Finite-Horizon H∞ Tracking Control of Unknow...

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Chinese control Conference (CCC)

作者： Mingxiang Liu Qianqian Cai Wei Meng Dandan Li Minyue Fu Zhimin Han School of Automation Guangdong University of Technology Guangzhou China Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Automation Hangzhou Dianzi University Hangzhou China

In this paper, we present a Q-learning algorithm for finite-horizon H∞ tracking control of unknown linear discrete-time systems. Finite horizon control is challenging due to its correspondence with a time-varying Riccati equation. The proposed algorithm can determine the solution of the time-varying Riccati equation with unknown system dynamics. We first establish the time-varying Q-function, then propose a corresponding Q-learning algorithm and prove its convergence. Finally, the proposed algorithm is proved to be effective through numerical examples.

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