检索结果-内蒙古大学图书馆

PM_(2.5) probabilistic forecasting system based on graph generative network with graph U-nets architecture

学校读者我要写书评

暂无评论

Journal of Central South University 2025年第1期32卷 304-318页

作者： LI Yan-fei YANG Rui DUAN Zhu LIU Hui School of Mechatronic Engineering Hunan Agricultural UniversityChangsha 410128China Institute of Artificial Intelligence and Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075China

Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other *** the changes and trends of air pollution can provide a scientific basis for governance and prevention *** this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of *** generative network(GGN)is used to process time-series meteorological data with complex *** graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the *** addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval *** the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale *** PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.

关键词： PM_(2.5)interval forecasting graph generative network graph U-Nets sparse Bayesian regression kernel density estimation spatial-temporal characteristics

RINGO: Real-time Navigation with a Guiding Trajectory for Aerial Manipulators in Unknown Environments

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhang, Zhaopeng Wu, Shizhen Guo, Chenfeng Fang, Yongchun Han, Jianda Liang, Xiao Institute of Robotics and Automatic Information System College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Motion planning for aerial manipulators in constrained environments has typically been limited to known environments or simplified to that of multi-rotors, which leads to poor adaptability and overly conservative trajectories. This paper presents RINGO: Real-time Navigation with a Guiding Trajectory, a novel planning framework that enables aerial manipulators to navigate unknown environments in real time. The proposed method simultaneously considers the positions of both the multi-rotor and the end-effector. A pre-obtained multi-rotor trajectory serves as a guiding reference, allowing the end-effector to generate a smooth, collision-free, and workspace-compatible trajectory. Leveraging the convex hull property of B-spline curves, we theoretically guarantee that the trajectory remains within the reachable workspace. To the best of our knowledge, this is the first work that enables real-time navigation of aerial manipulators in unknown environments. The simulation and experimental results show the effectiveness of the proposed method. The proposed method generates less conservative trajectories than approaches that consider only the multi-rotor. © 2025, CC BY.

关键词： Manipulators

New Type of Insoles Based on Air Pressure Arrays for Fall Detection in the Elderly

学校读者我要写书评

暂无评论

New Type of Insoles Based on Air Pressure Arrays for Fall De...

International Conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2024

作者： Xu, Yiwei Lu, Junjie Wan, Xiao Chang, Tao Lei, Qian Huang, Zixin School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9789819633166

For traditional fall detection insoles, the number of sensors is large, the cost is high, the sensor must correspond to the detection site one by one, and the resolution is low when the distance is far from the geometric center of the sensor, and the high sensitivity cannot be maintained in a large range. The rigid conduction of the force formed by the sensor and the soles of the elderly leads to poor comfort and low trust of the machine. A new soft insole for fall detection, based on an air pressure array, was designed and produced. Insole uses BMP280 pressure sensor and a 3D printed mold for silicone multi-layer injection molding. With four built-in airbags in one foot, tactile pressure can be converted to air pressure. STM32 microprocessor is used to construct the pressure information acquisition and processing system. Finally, Kalman filter algorithm is used to process the collected information. The experiment shows that the pressure detection system based on pressure change has high sensitivity and small repeatability error, and can effectively detect the pressure change and distribution of the sole. And reduce the noise interference, effectively improve the comfort and improve the precision and stability of recognition and acquisition equipment at the same time reduce the cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pressure sensors

Pedestrians and Robots: A Novel Dataset for Learning Distinct Social Navigation Forces

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Agrawal, Subham Ostermann-Myrau, Nico Dengler, Nils Bennewitz, Maren Humanoid Robots Lab University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence The Center for Robotics Bonn Germany

The increasing use of robots in human-centric public spaces such as shopping malls, sidewalks, and hospitals, requires understanding of how pedestrians respond to their presence. However, existing research lacks comprehensive datasets that capture the full range of pedestrian behaviors, e.g., including avoidance, neutrality, and attraction in the presence of robots. Such datasets can be used to effectively learn models capable of accurately predicting diverse responses of pedestrians to robot presence, which are crucial for advancing robot navigation strategies and optimizing pedestrian-aware motion planning. In this paper, we address these challenges by collecting a novel dataset of pedestrian motion in two outdoor locations under three distinct conditions, i.e., no robot presence, a stationary robot, and a moving robot. Thus, unlike existing datasets, ours explicitly encapsulates variations in pedestrian behavior across the different robot conditions. Using our dataset, we propose a novel Neural Social Robot Force Model (NSRFM), an extension of the traditional Social Force Model that integrates neural networks and robot-induced forces to better predict pedestrian behavior in the presence of robots. We validate the NSRFM by comparing its generated trajectories on different real-world datasets. Furthermore, we implemented it in simulation to enable the learning and benchmarking of robot navigation strategies based on their impact on pedestrian movement. Our results demonstrate the model’s effectiveness in replicating real-world pedestrian reactions and its its utility in developing, evaluating, and benchmarking social robot navigation algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Social robots

SharedAssembly: A Data Collection Approach via Shared Tele-Assembly

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Wu, Yansong Chen, Xiao Chen, Yu Sadeghian, Hamid Wu, Fan Bing, Zhenshan Haddadin, Sami König, Alexander Knoll, Alois CMIRMI - Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Assembly is a fundamental skill for robots in both modern manufacturing and service robotics. Existing datasets aim to address the data bottleneck in training general-purpose robot models, falling short of capturing contact-rich assembly tasks. To bridge this gap, we introduce SharedAssembly, a novel bilateral teleoperation approach with shared autonomy for scalable assembly execution and data collection. User studies demonstrate that the proposed approach enhances both success rates and efficiency, achieving a 97.0% success rate across various sub-millimeter-level assembly tasks. Notably, novice and intermediate users achieve performance comparable to experts using baseline teleoperation methods, significantly enhancing large-scale data *** Codes 68T40 © 2025, CC BY-NC-ND.

关键词： Robotic assembly

D2S: Towards Efficient Sparse 3D Object Detection via Dense to Sparse Knowledge Distillation

学校读者我要写书评

暂无评论

D2S: Towards Efficient Sparse 3D Object Detection via Dense ...

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

作者： Yuqi Huang Longjun Liu Yingke Gao Haonan Zhang Haoteng Li Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Beijing Institute of Control Engineering China Academy of Space Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

LiDAR-based 3D object detection is widely used in high-level autonomous driving schemes. However, the cumbersome modules in most 3D detectors lead to substantial computational overhead. Despite knowledge distillation (KD) is an effective approach for compressing models, previous methods cannot be extended to the dense-to-sparse paradigm. To this end, we propose a simple yet effective Dense to Sparse Knowledge Distillation (D2S) framework for accelerating 3D detectors. Firstly, to compensate for the difference in predicted location between dense and sparse detectors, we introduce a lightweight feature diffusion (FeaD) module for spreading important features. Secondly, to achieve high performance, we propose a dual-stream distillation scheme to transfer knowledge. In this scheme, we align both of the feature and category prediction between distillation pairs at important positions. Extensive experiments on KITTI and Waymo Open Dataset demonstrate the effectiveness of our method. For example, on KITTI dataset, the sparse detector we obtained surpasses VoxelNeXt with around 2.0× fewer parameters and 1.6× fewer FLOPs.

关键词： Three-dimensional displays Computational modeling Detectors Object detection Signal processing Feature extraction Acoustics Speech processing Autonomous vehicles

Optimization-free Smooth Control Barrier Function for Polygonal Collision Avoidance

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Wu, Shizhen Fang, Yongchun Sun, Ning Lu, Biao Liang, Xiao Zhao, Yiming Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

Polygonal collision avoidance (PCA) is short for the problem of collision avoidance between two polygons (i.e., polytopes in planar) that own their dynamic equations. This problem suffers the inherent difficulty in dealing with non-smooth boundaries and recently optimization-defined metrics, such as signed distance field (SDF) and its variants, have been proposed as control barrier functions (CBFs) to tackle PCA problems. In contrast, we propose an optimization-free smooth CBF method in this paper, which is computationally efficient and proved to be nonconservative. It is achieved by three main steps: a lower bound of SDF is expressed as a nested Boolean logic composition first, then its smooth approximation is established by applying the latest log-sum-exp method, after which a specified CBF-based safety filter is proposed to address this class of problems. To illustrate its wide applications, the optimization-free smooth CBF method is extended to solve distributed collision avoidance of two underactuated nonholonomic vehicles and drive an underactuated container crane to avoid a moving obstacle respectively, for which numerical simulations are also performed. © 2025, CC BY.

关键词： Magnetic levitation vehicles

STVO: Spatial-Temporal Constrained Velocity Obstacle for Safe Navigation among Pedestrians

学校读者我要写书评

暂无评论

IEEE Transactions on Vehicular Technology 2025年

作者： Song, Yichen Wang, Runhua Bi, Qingchen Pan, Zhangchao Gao, Haiming Zhang, Xuebo Nankai University College of Artificial Intelligence Institute of Robotics and Automatic Information System The Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China

This paper presents a novel motion planning method that safely navigates the unmanned ground vehicle (UGV) among pedestrian-rich environments by constructing the spatial-temporal constrained velocity obstacle (STVO) region. Compared with existing velocity obstacle (VO) type-based methods, the novelty of this work is twofold: 1) An innovative temporal constraint is introduced into the velocity space, also playing the role as an adaptive collision time constraint to dynamically adjust the velocity obstacle region according to real-time perceived pedestrian density;2) The spatial constraint is proposed to foresee and evade potential entrapments for the UGV, by carefully considering the position relationship between the UGV and the VO region. Additionally, a velocity resampling mechanism is further designed to prevent the UGV from being trapped. Above techniques augments the navigation flexibility and safety of the UGV, thereby significantly improves the success rate of practical navigation in dynamic and challenging pedestrian environments. Extensive simulations and real-world experiments have been conducted, especially the experiment in pedestrian-rich school cafeteria demonstrating satisfactory practicality of our approach. (Supplementary video link: https://***/xt4KosuPMYs). © 1967-2012 IEEE.

关键词： Pedestrian safety

Global Convergence of Continual Learning on Non-IID Data

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhu, Fei Liu, Yujing Liu, Wenzhuo Zhang, Zhaoxiang Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation CAS Hong Kong Key Laboratory of Systems and Control Academy of Mathematics and Systems Science CAS China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation CAS China School of Artificial Intelligence University of Chinese Academy of Sciences China

Continual learning, which aims to learn multiple tasks sequentially, has gained extensive attention. However, most existing work focuses on empirical studies, and the theoretical aspect remains under-explored. Recently, a few investigations have considered the theory of continual learning only for linear regressions, establishes the results based on the strict independent and identically distributed (i.i.d.) assumption and the persistent excitation on the feature data that may be difficult to verify or guarantee in practice. To overcome this fundamental limitation, in this paper, we provide a general and comprehensive theoretical analysis for continual learning of regression models. By utilizing the stochastic Lyapunov function and martingale estimation techniques, we establish the almost sure convergence results of continual learning under a general data condition for the first time. Additionally, without any excitation condition imposed on the data, the convergence rates for the forgetting and regret metrics are provided. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning