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IEEE International Conference on robotics and Biomimetics

作者： Mengjie Qin Rong Yang Zheyuan Lin Nuo Xu Te Li Minhong Wan Chunlong Zhang Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China

Low-light image enhancement (LIE) aims to enhance image contrast and recover fine details for images captured in low-light conditions. However, the limitations of using a single image and manually defined priors lead to inadequate information and limited adaptability, resulting in the failure to reveal image details effectively. To tackle this problem, we propose an unsupervised dual information flow LIE model that learns adaptive priors from low-light image pairs. The model is based on the assumption that priors can be learned from pairs of images. Moreover, a simple self+supervised model is designed to perform feature processing on the original input and further processing on the image to avoid the suboptimal limitations of the model. As a result, our model exhibits superior performance on the LIE task compared to other algorithms. As a result, our model exhibits improved network adaptability and superior performance in the LIE task compared to other algorithms.

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Free Performance Boost for Voting-based 3D Object Detection in Point Clouds

Free Performance Boost for Voting-based 3D Object Detection ...

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IEEE International Conference on robotics and Biomimetics

作者： Rong Yang Mengjie Qin Zheyuan Lin Chunlong Zhang Te Li Minhong Wan Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China

Recently, detecting objects from 3D point clouds based on the voting mechanism has received increasing attention. Various improvements such as RGB feature embedding, context information fusion and bounding box refinement have been developed to further boost the performance of voting-based models since VoteNet was proposed. In this work, we revisit the VoteNet model and point out the significant negative impact brought by votes from background, which has never been discussed properly in previous research as far as we know. In order to suppress votes from the background to obtain purer vote clusters, we redesign the loss function for the training of voting module of voting-based 3D detector. Experiments on the challenging SUN RGB-D dataset show that the new loss function can significantly boost the detection performance of existing state-of-the-art voting-based 3D detectors with no additional computational overhead.

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Robust Nonlinear Observer-based Adaptive Visual Servo Control for a Multirotor

Robust Nonlinear Observer-based Adaptive Visual Servo Contro...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Long, Yu Zhong, Hang Qian, Zhenyu Liu, Min Zhang, Hui Jiang, Yiming Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China National Engineering Research Center for Robot Visual Perception and Control Changsha China Hunan University College of Robotics Changsha410082 China China China Highway Engineering ConsultingCorporation DATA CO. LTD Beijing100089 China

ISBN: (纸本)9798350372601

In this article, an robust nonlinear observer-based visual servo adaptive control strategy is investigated for a multirotor for stable tracking of targets. This article uses the Newton Euler equation to model the dynamics of the multirotor and describes the visual servo system model. In order to avoid being affected by various disturbances, this article designs disturbance observer and velocity observer to improve the robustness of the system. Based on nonlinear observers, this article designs a layered controller that includes a visual outer loop controller and an adaptive sliding mode geometric inner loop controller. To verify the feasibility of this algorithm, this article conducts simulation experiments using MATLAB and Simulink, and adds constant disturbances for verification. Both simulation experiments and experimental results in real environments show that the controller we designs has excellent stability and robustness. © 2024 IEEE.

关键词： Euler equations

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Development of Pneumatic Soft Gripper for Effective Material Handling

Development of Pneumatic Soft Gripper for Effective Material...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Shivarama Naveen Sritam Panigrahi Aviral Vinit I Harihara Sudar Mervin Joe Thomas Robotics Laboratory Dept. of Mechanical Engineering National Institute of Technology Karnataka Surathkal (NITK) Mangaluru India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Recent times have shown a drastic transition in robotics from rigid to soft mechanisms. The reason is that soft robots offer safer human-robot interactions, reduce weight and also offer strong environmental adaptability. However, the inherent characteristics of soft materials to exhibit multiple degrees of freedom (DoF) make it challenging to control their movements. Therefore, this paper presents the development of a soft gripper with simple architecture to effectively and economically manipulate delicate objects. The gripper constructed using silicon rubber has an air cavity to actuate its opening and closing pneumatically. This paper presents the different stages of its construction and demonstrates its application on a 4 DoF robotic arm for handling delicate objects. A simulation study of the structural parameters of the proposed soft gripper is also carried out using ANSYS finite element software. The preliminary results show the superior adaptability of the soft gripper in handling objects of various shapes and sizes. The proposed system can find application in food, biomedical and electronic industries.

关键词： Shape Service robots Soft robotics Pneumatic systems Software Silicon Finite element analysis

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Robust-Tube Model Predictive Control for Piezoelectrically Actuated Fast Mechanical Switches for Hybrid Circuit Breakers 2

Robust-Tube Model Predictive Control for Piezoelectrically A...

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2nd International Conference on Artificial Intelligence and Automation Control, AIAC 2024

作者： Chai, Yu Yang, Chen Dong, Huijuan Zhao, Jie Shardt, Yuri A. W. Ju, Bingfeng ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China Technical University of Ilmenau Department of Automation Engineering Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331529147

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%. © 2024 IEEE.

关键词： Predictive control systems

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Two-Stream (2+1)D CNN Based on Frame Difference Attention for Driver Behavior Recognition 10

Two-Stream (2+1)D CNN Based on Frame Difference Attention fo...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Zou, Yang Du, Shaoyi Han, Hongcheng Liu, Yuying Tian, Zhiqiang Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Shaanxi Xi'an China Shunan Academy of Artificial Intelligence Zhejiang Ningbo China Xi'an Jiaotong University School of Software Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350304770

Driving behavior recognition is a key technology in human-machine cooperative driving, which plays an important role in the construction of an intelligent transportation environment. Due to the similarity of drivers ' actions and backgrounds in driving environments, it's challenging for existing behavior recognition methods to recognize behaviors in driving environments precisely. To solve these problems, we propose the two-stream (2+1)D CNN with Frame Difference Attention Module. First, we use two streams to process the information captured from the face camera and road camera and use the Squeeze and Excitation attention module to fuse the stream information. Second, we propose the Frame Difference Attention Module to enhance the changes between adjacent frames so that the subtle actions in driving environments can be captured easier. We test our network on the Brain4Cars dataset and have a good performance on it. © 2023 IEEE.

关键词： Cameras

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engineering vascularised organoid-on-a-chip:strategies,advances and future perspectives

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Biomaterials Translational 2024年第1期5卷 21-32页

作者： Zhangjie Li Dingyuan Yu Chenyang Zhou Feifan Wang Kangyi Lu Yijun Liu Jiaqi Xu Lian Xuan Xiaolin Wang Department of Micro/Nano Electronics School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghaiChina National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong UniversityShanghaiChina National Center for Translational Medicine(Shanghai)SHU Branch Shanghai UniversityShanghaiChina

In recent years,advances in microfabrication technology and tissue engineering have propelled the development of a novel drug screening and disease modelling platform known as *** platform integrates organoids and organ-on-a-chip technologies,emerging as a promising approach for in vitro modelling of human organ ***-on-a-chip devices leverage microfluidic systems to simulate the physiological microenvironment of specific organs,offering a more dynamic and flexible setting that can mimic a more comprehensive human biological ***,the lack of functional vasculature has remained a significant challenge in this *** is crucial for the long-term culture and in vitro modelling of organoids,holding important implications for drug development and personalised medical *** review provides an overview of research progress in developing vascularised organoid-on-a-chip models,addressing methods for in vitro vascularisation and advancements in vascularised *** aim is to serve as a reference for future endeavors in constructing fully functional vascularised organoid-on-a-chip platforms.

关键词： drug screening microfluidic chip organoids tissue engineering vascularisation

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive Control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Yu, Guanfeng Zhang, Han Wang, Zehao Wang, Jingchuan Shanghai Jiao Tong University Institute of Medical Robotics Department of Automation China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350372601

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities. © 2024 IEEE.

关键词： Iterative methods

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Quenched PVDF/PMMA Porous Matrix for Triboelectric Energy Harvesting and Sensing

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Energy & Environmental Materials 2025年第1期8卷 225-234页

作者： Assem Mubarak Bayandy Sarsembayev Yerzhigit Serik Abdirakhman Onabek Zhanat Kappassov Zhumabay Bakenov Kazuyoshi Tsuchiya Gulnur Kalimuldina Department of Mechanical and Aerospace Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan Institute of Smart Systems and Artificial Intelligence Nazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan Department of Robotics Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan Department of Chemical and Materials Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan Micro/Nano Technology Center Tokai University4-1-1 KitakanameHiratsukaKanagawa 259-1292Japan

The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their *** introducing innovative solutions for energy harvesting and autonomous sensing,this research presents a cost-effective strategy to enhance the performance of triboelectric nanogenerators(TENGs).The TENG was fabricated from polyvinylidene fluoride(PVDF)and N,N'-poly(methyl methacrylate)(PMMA)blend with a porous structure via a novel optimized quenching *** developed approach results in a highβ-phase content(85.7%)PVDF/3wt.%PMMA porous blend,known for its superior piezoelectric ***/3wt.%PMMA modified porous TENG demonstrates remarkable electrical output,with a dielectric constant of 40 and an open-circuit voltage of approximately 600 *** porous matrix notably increases durability,enduring over 36000 operational cycles without performance ***,practical applications were explored in this research,including powering LEDs and pacemakers with a maximum power output of 750mWm^(-2).Also,TENG served as a self-powered tactile sensor for robotic applications in various temperature *** work highlights the potential of the PVDF/PMMA porous blend to utilize the next-generation self-powered sensors and power small electronic devices.

关键词： N N0-poly(methyl methacrylate)(PMMA) robotic tactile sensors polyvinylidene fluoride(PVDF) quenching triboelectric nanogenerators(TENGs)

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Refiner: Fine-grained Cross-modal Concepts Refinement for Compositional Zero-Shot Learning

Refiner: Fine-grained Cross-modal Concepts Refinement for Co...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiao Zhang Haodong Jing Hui Chen Yongqiang Ma Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent Compositional Zero-Shot Learning (CZSL) methods increasingly adopt the pre-trained vision-language models to capture the contextual relations between image and text spaces. However, the single-class-token design from Transformer-based encoder inevitably captures contextual information from unrelated objects and background, thus hindering the modeling of fine-grained class-specific visual features. Suffering from cross-modal gap, prior methods also struggle to improve compositional recognition performance. To address these issues, we propose a fine-grained cross-modal concepts refinement framework, termed as Refiner, which comprises two pivotal components: (i) the fine-grained concepts refinement of image embeddings to capture state-object context within visual scenes, and (ii) the cross-modal information fusion to mitigate the modality gap. By leveraging learnable query vectors to capture region-specific semantic information pertinent to composition labels, our approach refines visual representations with fine-grained state-object context information. As for cross-modal information fusion, we construct a robust image-to-text mapping by aligning visual embeddings with states, objects, and compositions, respectively. Extensive experiments demonstrate that our Refiner achieves new state-of-the-art performance across all popular benchmarks in both closed- and open-world settings.

关键词： Visualization Zero shot learning Semantics Benchmark testing Signal processing Transformers Vectors Acoustics Speech processing Context modeling

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