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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 {3. sun School of Electrical Engineering and Automation Tianjin University of Technology department of computer science and {3. 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

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 {3. 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

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,{3., Beijing 100084, china 2State Key Laboratory of Intelligent technology and Systems, Beijing 100084, china 3.7} National Laboratory for Information science and technology, Beijing 100084, china 1. department of computer science and {3. 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

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 {3. Mingxuan JING Jianwei ZHANG department of computer science and {3. 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 {3. of computer {3. and technology, tsinghua university, Beijing 100084, china 2 {3. 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 {3. 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

Fabric Recognition Using Zero-Shot Learning

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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 {3. Haihong Pan department of computer science and {3. 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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A novel multi-modal tactile sensor design using thermochromic material

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science china(Information sciences) 2019年第11期62卷 189-191页

作者： fuchun sun Bin FANG Hongxiang XUE huaping {3. Haiming HUANG department of computer science and {3. Tsinghua University Key Lab of Industrial computer Control Engineering of Hebei Province Yanshan University College of Information Engineering Shenzhen University

Tactile sensors are important for ensuring robotic perception [1], and their performance directly affects a robot’s capability for performing dexterous operations. These sensors mimic the tactile sensing capability of human hands. This is because the skin of human hands is involved in considerably meticulous works and can provide multi-

关键词： A novel multi-modal tactile sensor design using thermochromic material

A Review of Smart Materials for the Boost of Soft Actuators,Soft Sensors,and Robotics Applications

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Chinese Journal of Mechanical Engineering 2022年第2期35卷 12-27页

作者： Yufei Hao Shixin Zhang Bin Fang fuchun sun huaping {3. Haiyuan Li School of mechanical engineering University of Science and Technology BeijingBeijing 100083China School of Engineering and technology China University of Geosciences(Beijing)Beijing 100083China department of computer science and {3. Tsinghua National Laboratory for Information Science and TechnologyTsinghua UniversityBeijing 100084China School of Automation Beijing University of Posts and TelecommunicationsBeijing 100876China

With the advance of smart material science,robotics is evolving from rigid robots to soft *** to rigid robots,soft robots can safely interact with the environment,easily navigate in unstructured fields,and be minimized to operate in narrow spaces,owning to the new actuation and sensing technologies developed by the smart *** the review,different actuation and sensing technologies based on different smart materials are analyzed and *** to the driving or feedback signals,actuators are categorized into electrically responsive actuators,thermally responsive actuators,magnetically responsive actuators,and photoresponsive actuators;sensors are categorized into resistive sensors,capacitive sensors,magnetic sensors,and optical waveguide *** introducing the principle and several robotic prototypes of some typical materials in each category of the actuators and *** advantages and disadvantages of the actuators and sensors are compared based on the categories,and their potential applications in robotics are also presented.

关键词： Smart material Soft robot Actuator Sensor

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Information Fusion-based Mobile Robot Path Control

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Information Fusion-based Mobile Robot Path Control