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A framework for the fusion of visual and tactile modalities for improving robot perception

Science China(Information Sciences) 2017年第1期60卷 145-156页

作者： Wenchang ZHANG Fuchun SUN Hang WU Haolin YANG The State Key Laboratory of Intelligent Technology and Systems Tsinghua University

Robots should ideally perceive objects using human-like multi-modal sensing such as vision, tactile feedback, smell, and hearing. However, the features presentations are different for each modal sensor. Moreover,the extracted feature methods for each modal are not the same. Some modal features such as vision, which presents a spatial property, are static while features such as tactile feedback, which presents temporal pattern,are dynamic. It is difficult to fuse these data at the feature level for robot perception. In this study, we propose a framework for the fusion of visual and tactile modal features, which includes the extraction of features, feature vector normalization and generation based on bag-of-system(BoS), and coding by robust multi-modal joint sparse representation(RM-JSR) and classification, thereby enabling robot perception to solve the problem of diverse modal data fusion at the feature level. Finally, comparative experiments are carried out to demonstrate the performance of this framework.

关键词： multi-modal fusion robot perception vision tactile classification

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Cutting-edge RNA quantitative detection methods for rapid and on-site analysis

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TrAC - Trends in Analytical Chemistry 2025年 190卷

作者： Dong, Tao Yu, Chenghui Han, Feng Mao, Qi Jing, Weixuan Yang, Zhaochu Matos Pires, Nuno Miguel Correia, José Higino Lin, Qijing Hu, Fei Yao, Honghong Hu, Kui Xu, Tao Jiang, Zhuangde Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Nan'an District Chongqing400067 China X Multidisciplinary Research Institute School of Instrument Science and Technology Xi'an Jiaotong University Xi'an710049 China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an710049 China Department of Industrial Electronics University of Minho Campus Azurem Guimaraes4800 Portugal Department of Pharmacology School of Medicine Southeast University Jiangsu Nanjing China Department of Cardiothoracic Surgery Chongqing Fifth People's Hospital China The Third People's Hospital of Nan ‘an District Chongqing China

RNA, a vital biomarker, is crucial for molecular diagnostics, encompassing pathogen detection, gene expression, tumor diagnosis, prognosis, and drug efficacy. Since the COVID-19 outbreak, its role in clinical settings has grown. This review examines established RNA detection methods like qPCR, sequencing, Nanostring, and fluorescence in situ hybridization (FISH), highlighting recent progress and trends. It also focuses on new Point-of-Care Testing (POCT) methods developed over the last five years, categorizing them by the necessity for reverse transcription and nucleic acid amplification. The review emphasizes advances in multiplex detection tailored for POCT, aiming to update researchers and clinicians on the latest RNA detection technologies. Among various RNA detection methods, the amplification-free approach is preferable for POCT applications compared to those requiring reverse transcription and sequence-dependent amplification, as they simplify the detection process by reducing technical complexity while retaining high sensitivity. © 2025

关键词： Transcription

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Distributed Minimum-time Consensus Prediction for High-order Multi-agent systems

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IEEE Transactions on Automatic Control 2025年

作者： Hu, Fulong Zhang, Hai-Tao Xu, Bowen Hu, Zhe School of Artificial Intelligence and Automation The Institute of Artificial Intelligence The Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Guangdong Hust Industrial Technology Research Institute Huazhong University of Science and Technology Dongguan523808 China Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization Dongguan523808 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi'an710072 China

In this note, a detective matrix-based distributed algorithm is proposed to address a distributed minimum-time consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent predicts the consensus value merely using the minimum length of individual and/or neighboring successive position time series. Compared to most existing algorithms that could only predict constant consensus values for first-order linear MASs, the present method could predict consensus manifolds for high-order linear MASs as well, which could be time-varying. Finally, both the effectiveness and superiority of the proposed distributed minimum-time consensus algorithm are substantiated by numerical simulations. © 2025 IEEE.

关键词： Multi agent systems

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Enhanced vibration energy harvesting of magnetically coupled piezoelectric cantilever beam with double-point stopper

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Mechanical systems and Signal Processing 2025年 233卷

作者： Wang, Lu Zhang, Ying Liu, Shuai Zhao, Lin-Chuan Hu, Kai-Ming Li, Zhikang Zhao, Libo Zhang, Wen-Ming Meng, Guang State Key Laboratory for Manufacturing Systems Engineering State Industry-Education Integration Center for Medical Innovations International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technologies Shaanxi Innovation Center for Special Sensing and Testing Technology in Extreme Environments Shaanxi Provincial University Engineering Research Center for Micro/Nano Acoustic Devices and Intelligent Systems Xi'an Jiaotong University Xi'an710049 China School of Instrument Science and Technology Xi'an Jiaotong University Xi'an710049 China State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China

Piezoelectric fragility is concerned with low broadband and large amplitude vibration for energy harvesting in human motion, vehicles, wave energy, etc. The design of the stopper is helpful for the protection and broadband of the piezoelectric vibration energy harvester (PVEH), but the output is limited due to uneven stress distribution and collision energy loss. This paper proposes a U-shaped mass PVEH with double-point stopper and magnetic coupling. The stress homogenization method of the cantilever beam is achieved by converting the collision energy into bending moment by double-point stopper, which effectively improves the electromechanical coupling coefficient of the structure. Thus, the double-point stopper enables 1.5 times saturation displacement within allowable PZT stress. The experimental performance is carried out that PVEH with double-point stopper and magnetic coupling has double higher power output of 10.2 mW than single-point stopper at the excitation level of 0.6 g. The stopper collision and the magnetic coupling broaden the high and low frequency response of the PVEH system jointly. It has voltage output beyond 40 V with broadband of 8–15.6 Hz under 1 g acceleration in test. This work has a good potential in the occasions of the big mass, stopper protection and magnetic coupling design for low frequency, broadband and large amplitude of PVEH. © 2025

关键词： Stress concentration

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

引用

arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

引用

arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Learning-Based NOMA-Enabled Queue-Aware Task Offloading and UAV 3D Trajectory Planning for SAGIN

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IEEE Transactions on Vehicular technology 2025年

作者： Qin, Peng Li, Hongjie Fu, Yang Hu, Jinhui Wu, Xue Zhang, Xianchao North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering Beijing102206 China Beijing University of Technology Beijing100124 China China Electronics Technology Group Co. Ltd New Smart City Research Institute Shenzhen518038 China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing314001 China

Space-air-ground integrated network (SAGIN) is a viable solution to serve users in such areas lacking ground base stations, in which case unmanned aerial vehicles (UAVs) provide massive access and satellites provide backhaul transmission. To improve spectrum utilization and throughput, non-orthogonal multiple access (NOMA) is used to reuse channels. However, different trajectories of UAVs and computing task assignment may lead to various communication delay and energy consumption. Additionally, regarding the high dynamic nature of SAGIN, it often falls into the dilemma of information uncertainty and curse of dimensionality. To overcome the above challenges, a hierarchical network model is designed, where UAVs and satellites collaborate to process users' offloaded computing tasks. Then, a joint UAV trajectory design, task offloading, task assignment and computing resource allocation problem for minimizing system cost is then proposed. Due to the coupling between queue delay constraints and decision-making, Lyapunov optimization is applied to split the issue into three subproblems. Firstly, a UAV trajectory design and task offloading algorithm based on multi-agent twin delayed deep deterministic policy gradient (MATD3) is designed to deal with the curse of dimension issue. Then, the problem of joint task assignment and UAV computing resource allocation is solved via CVX due to that it is convex. Finally, a low-complexity Greedy-based Satellite Computing Resource Allocation (GSCRA) algorithm is proposed to obtain the minimum system cost. Simulations show the superior performance of our algorithm compared with benchmarks in reducing the system cost. © 1967-2012 IEEE.

关键词： Resource allocation

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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy systems 2025年

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points. Our code is available at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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Boosting Stereo Image Noise Removal by Learning Uncertainty and Enriched Features

Boosting Stereo Image Noise Removal by Learning Uncertainty ...

引用

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

作者： Bingcai Wei Hui Liu Chuang Qian Xinyu Ren Xintong Xu School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Stereo image denoising is crucial to improve perceptual quality and autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in noisy data, leading to suboptimal denoising outcomes. To address this, we introduce a novel framework named the Cross-view Uncertainty-aware Gradient-assisted Fusion Network (CUGFNet), which pioneers the integration of uncertainty estimation, gradient-assisted feature extraction, and frequency interaction-based feature fusion for superior stereo image de-noising. CUGFNet’s primary innovation lies in its Cross-view Uncertainty-aware Probabilistic Feature Extractor (CUPFE), which provides a probabilistic interpretation that enables the model to make informed decisions during the denoising process. The Gradient-assisted Attentive Feature Aggregation Module (GAFAM) enhances the network’s ability to detect details and structure in stereo images by using spatial gradients to highlight important features. Extensive experimental validation across a variety of stereo image datasets substantiates the effectiveness of CUGFNet. This model delivers not only higher PSNR and SSIM scores but also demonstrates superior robustness against diverse noise levels.

关键词： Technological innovation Uncertainty Noise reduction Noise Feature extraction Probabilistic logic Noise measurement Speech processing Noise level Image denoising

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Model Predictive Control for Tracking Bounded References With Arbitrary Dynamics

arXiv

引用

arXiv 2025年

作者： Han, Shibo Hou, Bonan Zhang, Yuhao Shi, Xiaotong Zhao, Xingwei Department of Mechanical Engineering National University of Singapore 117576 Singapore Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Hubei University Wuhan430062 China

In this article, a model predictive control (MPC) method is proposed for constrained linear systems to track bounded references with arbitrary dynamics. Besides control inputs to be determined, artificial reference is introduced as additional decision variable, which serves as an intermediate target to cope with sudden changes of reference and enlarges domain of attraction. Cost function penalizes both artificial state error and reference error, while terminal constraint is imposed on artificial state error and artificial reference. We specify the requirements for terminal constraint and cost function to guarantee recursive feasibility of the proposed method and asymptotic stability of tracking error. Then, periodic and non-periodic references are considered and the method to determine required cost function and terminal constraint is proposed. Finally, the efficiency of the proposed MPC controller is demonstrated with simulation examples. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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