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Data-driven electrical resistance tomography for robotic large-area tactile sensing

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science China(Information sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG huaping liu Xiaofeng liu fuchun {4. School of Electrical Engineering and Automation Tianjin University of Technology department of computer science and technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace technology (Hohai university) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

Fusion tracking in color and infrared images using joint sparse representation

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science China(Information sciences) 2012年第3期55卷 590-599页

作者： liu huaping1,2,3 & sun fuchun1,2,3 1department of computer science and technology, tsinghua university,{4., China 2State Key Laboratory of Intelligent technology and Systems,{4., China 3tsinghua National Laboratory for Information science and technology,{4., China 1. department of computer science and technology Tsinghua University Beijing 100084 China2. State Key Laboratory of Intelligent Technology and Systems Beijing 100084 China3. Tsinghua National Laboratory for Information Science and Technology Beijing 100084 China

Currently sparse signal reconstruction gains considerable interest and is applied in many fields. In this paper, a similarity induced by joint sparse representation is designed to construct the likelihood function of particle filter tracker so that the color visual spectrum and thermal spectrum images can be fused for object tracking. The proposed fusion scheme performs joint sparse representation calculation on both modalities and the resultant tracking results are fused using min operation on the sparse representation coefficients. In addition, a co-learning approach is proposed to update the reference templates of both modality and enhance the tracking robustness. The proposed fusion scheme outperforms state-of-the-art approaches, and its effectiveness is verified using OTCBVS database.

关键词： joint sparse representation fusion tracking color image infrared image

Self-Aligning Multi-Modal Transformer for Oropharyngeal Swab Point Localization

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tsinghua science and technology 2024年第4期29卷 1082-1091页

作者： Tianyu liu fuchun sun department of computer science and technology Tsinghua UniversityBeijing 100083China

The oropharyngeal swabbing is a pre-diagnostic procedure used to test various respiratory diseases, including COVID and Influenza A (H1N1). To improve the testing efficiency of testing, a real-time, accurate, and robust sampling point localization algorithm is needed for robots. However, current solutions rely heavily on visual input, which is not reliable enough for large-scale deployment. The transformer has significantly improved the performance of image-related tasks and challenged the dominance of traditional convolutional neural networks (CNNs) in the image field. Inspired by its success, we propose a novel self-aligning multi-modal transformer (SAMMT) to dynamically attend to different parts of unaligned feature maps, preventing information loss caused by perspective disparity and simplifying overall implementation. Unlike preexisting multi-modal transformers, our attention mechanism works in image space instead of embedding space, rendering the need for the sensor registration process obsolete. To facilitate the multi-modal task, we collected and annotate an oropharynx localization/segmentation dataset by trained medical personnel. This dataset is open-sourced and can be used for future multi-modal research. Our experiments show that our model improves the performance of the localization task by 4.2% compared to the pure visual model, and reduces the pixel-wise error rate of the segmentation task by 16.7% compared to the CNN baseline.

关键词： segmentation localization transformer multi-modal perception robotic perception

Robust tube-based MPC with smooth computation for dexterous robot manipulation

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science China(Information sciences) 2024年第11期67卷 252-268页

作者： Yu LUO Tianying JI fuchun sun Qie SIMA huaping liu Mingxuan JING Jianwei ZHANG department of computer science and technology Tsinghua University science & technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences department of Informatics University of Hamburg

Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.

关键词： dexterous robot manipulation model predictive control smooth computation

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Multi-objective robust control based on fuzzy singularly perturbed models for hypersonic vehicles

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science China(Information sciences) 2011年第3期54卷 563-576页

作者： HU YeNan 1 , YUAN Yuan 2 , MIN HaiBo 2,3 & sun fuchun 1 1 department of computer {4. and {4., tsinghua university, beijing 100084, China 2 department of Automation, Xi’an Research Institute of High-Tech, Xi’an, Shaanxi 710025, China 3 beijing Institute of Astronautical System Engineering, beijing 100076, China 1. department of computer science and technology Tsinghua University Beijing 100084 China2. Department of Automation Xi’an Research Institute of High-Tech Xi’an Shaanxi 710025 China3. Beijing Institute of Astronautical System Engineering Beijing 100076 China

In this paper, we propose a multi-objective robust controller based on fuzzy singularly perturbed models (FSPM) for the longitudinal motion of an air-breathing hypersonic vehicle. The control objective is to provide velocity and altitude tracking in the presence of the uncertainties and unknown nonlinearities in the model caused by variations in flight conditions. By approximating the multi-time-scale model with FSPM, ill-posed matrix calculations are circumvented. Under this framework, a fuzzy multi-objective robust controller is developed, and the linear matrix inequalities (LMI) condition is derived. Numerical simulation using the longitudinal model of a hypersonic aircraft is provided to demonstrate the effectiveness of the proposed strategy.

关键词： FSPM multi-objective robust control intelligent control hypersonic vehicle

COMPLEX NUMBER MODE APPROACH FOR REDUCING VIBRATION OF STRUCTURAL FLEXIBLE REDUNDANT ROBOT MANIPULATORS

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Chinese Journal of Mechanical Engineering 2001年第2期14卷 102-105页

作者： Wu Licheng sun Zengqi sun fuchun department of {4. science and {4., Qinghua university Lu Zhen Yu Shouqian Zheng Hong beijing university of Aeronautics and Astronautics 清华大学计算机科学与工程系北京北京航空航天大学北京

A complex number mode analysis approach is proposed for vibration reducing of structural flexible redundant manipulators by utilizing self motion. In the proposed approach, the self motion is evaluated to nullify the modal exciting force of flexural motion, and the approach can be freely used when the degree of freedom of flexural motion is much greater than the available degree of reundancy. The availability and effectiveness of the proposed approach are demonstrated through numerical simulation with a four link spatial robotic manipulator possessing an end flexible link.

关键词： Redundant manipulators Self motion Vibration reducing Complex number mode analysis

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Information Fusion-based Mobile Robot Path Control

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Information Fusion-based Mobile Robot Path Control

Fabric Recognition Using Zero-Shot Learning

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作者： Jingyan Wang 1 , huaping liu 2 , Meng Gao 1 , fuchun sun 2 , Wei Xiao 2 1. Shijiazhuang Tiedao {4., Shijiazhuang 05004.2. department of {4. science and technology tsinghua university, beijing, 10084

The paper is combining with the deliberation-reaction hybrid control architecture by using a high-speed DSP chip is used to deal with the motion control and the mobile robot path planning, the technology of multi-sensor information fusion achieve for vertical and horizontal control, to reach precise positioning technology. In the case of local partially unknown environment, the function of obstacle avoidance is completed, and the trajectory coordinates of the mobile robot is showed on the PC real-time visual interface. The experimental results show the good usability of the proposed control method.

关键词： Mobile Robot Information Fusion Path Tracking

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tsinghua science and technology 2019年第6期24卷 645-653页

作者： Feng Wang huaping liu fuchun sun Haihong Pan department of computer science and technology Tsinghua UniversityBeijing 100084China College of Mechanical Engineering Guangxi UniversityNanning 530003China

In this work, we use a deep learning method to tackle the Zero-Shot Learning(ZSL) problem in tactile material recognition by incorporating the advanced semantic information into a training model. Our main technical contribution is our proposal of an end-to-end deep learning framework for solving the tactile ZSL problem. In this framework, we use a Convolutional Neural Network(CNN) to extract the spatial features and Long Short-Term Memory(LSTM) to extract the temporal features in dynamic tactile sequences, and develop a loss function suitable for the ZSL setting. We present the results of experimental evaluations on publicly available datasets, which show the effectiveness of the proposed method.

关键词： Zero-Shot-Learning (ZSL) fabric recog nition tactile recog nition deep lear ning

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Shared Control Teleoperation for Targets Acquisition

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Shared Control Teleoperation for Targets Acquisition