检索结果-内蒙古大学图书馆

SSRN 2025年

作者： Shih, Pin-Chu Chiang, Yun-Chi Hu, Jun-Jie Fu, Li-Chen Department of Electrical Engineering National Taiwan University Taipei100 Taiwan NTU Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taipei100 Taiwan

High-quality 3D reconstruction is essential for ap- plications such as autonomous driving, AR/VR, and smart cities. Traditional methods using handheld sensors often result in incomplete and misaligned models. While autonomous explo- ration can improve these results, it often sacrifices quality for speed. In this paper, we propose a system for high-quality 3D reconstruction of large-scale indoor environments through autonomous exploration using a mobile robot equipped with a solid-state LiDAR on a 2-degree-of-freedom gimbal. This system not only controls the mobile robot to visit unknown places and areas with insufficient reconstruction quality but also facilitates high-frequency, real-time exploration path planning. This paper concludes with various simulations and real-world experiments to validate the effectiveness of our system. © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

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D3-ARM: High-Dynamic, Dexterous and Fully Decoupled Cable-driven Robotic Arm

arXiv

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arXiv 2025年

作者： Luo, Hong Xu, Jianle Li, Shoujie Liang, Huayue Chen, Yanbo Xia, Chongkun Wang, Xueqian Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Advanced Manufacturing Sun Yat-sen University Shenzhen518107 China

Cable transmission enables motors of robotic arm to operate lightweight and low-inertia joints remotely in various environments, but it also creates issues with motion coupling and cable routing that can reduce arm’s control precision and performance. In this paper, we present a novel motion decoupling mechanism with low-friction to align the cables and efficiently transmit the motor’s power. By arranging these mechanisms at the joints, we fabricate a fully decoupled and lightweight cable-driven robotic arm called D3-Arm with all the electrical components be placed at the base. Its 776 mm length moving part boasts six degrees of freedom (DOF) and only 1.6 kg weights. To address the issue of cable slack, a cable-pretension mechanism is integrated to enhance the stability of long-distance cable transmission. Through a series of comprehensive tests, D3-Arm demonstrated 1.29 mm average positioning error and 2.0 kg payload capacity, proving the practicality of the proposed decoupling mechanisms in cable-driven robotic arm. Copyright © 2025, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Unsupervised Training Framework for 3D Point Cloud Object Detection Model 25

Unsupervised Training Framework for 3D Point Cloud Object De...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

作者： Min-Jyun Wu Hsiang-Jui Lin Shih-Hao Hung Chi-Sheng Shih Dept. of Computer Science and Information Engineering National Taiwan University Taipei Taiwan High Performance and Scientific Computing (HPSC) Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Center of Artificial Intelligence and Advanced Robotics National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400706295

Point cloud data frames are critical, if not indispensable, for precise robot navigation and localization, but training the object detection models for them remains challenging. Many models require labeled 3D objects to train the model. However, the sparse and occluded 3D point cloud data make it difficult, if not impossible, to automate the labeling process. This work proposes a training framework to generate 3D labels on point clouds to tackle the aforementioned challenges. The proposed method takes advantage of the consecutive presence of the same object on different frames to automate the labeling process. The experimental results show that the unsupervised framework trains a robust model for 3D object detection. On the roadside data, the model archives 90.27% AP for scooters and 91.33% AP for cars. On nuScenes dataset, the framework demonstrates the detection precision doubles on IoU 0.25 and IoU 0.5 when the recalls remain similar, compared to the model trained by the MODEST framework.

关键词： computer systems organization

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Brain Region Knowledge Based Dual-Stream Transformer for EEG Emotion Recognition

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Yichuan Xu, Geng-Xin Liang, Haotian Wang, Yishan Wan, Feng Li, Ye Southern University of Science and Technology Shenzhen518055 China Research Center for Biomedical Information Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Institute of Advanced Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering The Faculty of Science and Technology The Centre for Cognitive and Brain Sciences The Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau China

In consumer electronics, affective computing allows devices to recognize and respond to the emotional states of individuals, thereby enhancing personalization and satisfaction. Electroencephalogram (EEG)-based emotion recognition methods have garnered attention for their capability to provide real-time, objective insights into emotional and cognitive states. Existing research mainly focuses on extracting temporal and spectral features from EEG signals for emotion recognition. However, electrode shifts may occur across different experiments during data acquisition, resulting in models with limited robustness and constrained classification performance. To address this challenge, this paper introduces a Brain Region Knowledge based Dual-Stream network (BRKDSnet) for emotion recognition. It employs a dual-stream architecture to effectively fuse the temporal, spectral, and spatial features of EEG signals. By incorporating prior brain region partition knowledge, it efficiently aggregates channel features into distinct brain region features. Such a brain region based partition mitigates noise issues caused by electrode shifts during user wearing in consumer electronic applications, and leading to learn more robust feature representations. The experiments conducted on the widely used emotion recognition datasets SEED and SEED-IV shows that BRKDSnet achieves state-of-the-art results, with accuracies of 97.81% (improved by 0.64%-3.57% compared to existing works) and 90.76% (improved by 3.13%-11.39%) respectively. Finally, through analysis of the visualization results, it is found that EEG channels located in the temporal lobe contributed the most to emotion recognition classification, which also provides the potential for future applications in wearable devices. © 2025 IEEE.

关键词： Electroencephalography

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Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging

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IEEE Transactions on Medical Imaging 2025年

作者： Li, Yuanhao Chen, Badong Hu, Zhongxu Suzuki, Keita Bai, Wenjun Koike, Yasuharu Yamashita, Okito RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Advanced Telecommunications Research Institute International Department of Computational Brain Imaging Kyoto619-0237 Japan 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 Xi’an710049 China Huazhong University of Science and Technology School of Mechanical Science and Engineering Wuhan430074 China Institute of Science Tokyo Institute of Integrated Research Yokohama226-8501 Japan

Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function for Bayesian inference. However, the electromagnetic measurements of brain activity are usually affected by miscellaneous artifacts, leading to a potentially non-Gaussian distribution for the observation noise. Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task. In this study, we aim to solve this problem by proposing a new likelihood model which is robust with respect to non-Gaussian noises. Motivated by the robust maximum correntropy criterion, we propose a new improper distribution model concerning the noise assumption. This new noise distribution is leveraged to structure a robust likelihood function and integrated with hierarchical prior distributions to estimate source activities by variational inference. In particular, the score matching is adopted to determine the hyperparameters for the improper likelihood model. A comprehensive performance evaluation is performed to compare the proposed noise assumption to the conventional Gaussian model. Simulation results show that, the proposed method can realize more precise source reconstruction by designing known ground-truth. The realworld dataset also demonstrates the superiority of our new method with the visual perception task. This study provides a new backbone for Bayesian source imaging, which would facilitate its application using real-world noisy brain signal. © 1982-2012 IEEE.

关键词： Gaussian noise (electronic)

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Complex environment localization system using complementary ceiling and ground map information

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Autonomous Robots 2023年第6期47卷 669-683页

作者： Yu, Chee-An Chen, Hao-Yun Wang, Chun-Chieh Fu, Li-Chen Department of Electrical Engineering National Taiwan University Taipei Taiwan NTU Center for Artificial Intelligence and Advanced Robotics Taipei Taiwan

This paper proposes a robust localization system using complementary information extracted from ceiling and ground plans, particularly applicable to dynamic and complex environments. The ceiling perception provides the robot with stable and time-invariant environmental features independent of the dynamic changes on the ground, whereas the ground perception allows the robot to navigate in the ground plane while avoiding stationary obstacles. We propose an architecture to fuse ground 2D LiDAR scan and ceiling 3D LiDAR scan with our enhanced mapping algorithm associating perception from both sources efficiently. The localization ability and the navigation performance can be promisingly secured even in a harsh environment with our complementary sensed information from the ground and ceiling. The salient feature of our work is that our system can simultaneously map both the ceiling and ground plane efficiently without extra efforts of deploying articulated landmarks and apply such hybrid information effectively, which facilitates the robot to travel through any indoor environment with human crowds without getting lost. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Ceilings

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Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions

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IEEE Transactions on Affective Computing 2023年第2期14卷 1228-1235页

作者： Huang, Tsung-Ren Hsu, Shin-Min Fu, Li-Chen National Taiwan University Department of Psychology Center for Artificial Intelligence & Advanced Robotics Taipei106319 Taiwan National Taiwan University Center for Artificial Intelligence & Advanced Robotics Department of Electrical Engineering Department of Computer Science and Information Engineering Taipei106319 Taiwan

Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a novel way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions. © 2010-2012 IEEE.

关键词： Face recognition

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Evaluating Trunk Control Ability in Patients with Spinal Cord Injury via a Robotic Brace

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Guo, Xingzhao Zhou, Zhihao Shi, Jiehong Wang, Rongli Wang, Ninghua Wang, Qining Zhengzhou University School of Mechanical and Power Engineering Zhengzhou450001 China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering Beijing100871 China Peking University Institute for Artificial Intelligence Beijing100871 China Peking University First Hospital Beijing100034 China Peking University School of Advanced Manufacturing and Robotics Faculty of Engineering Beijing100871 China University of Health and Rehabilitation Sciences Qingdao China

Evaluating trunk control ability is significant in guiding patients towards proper functional training. Existing assessment techniques are subjective with low resolution, lack multi-dimensional assessment capability, or fail to provide active protection for patients during evaluation. This study proposes a robotic brace, RoboBDsys-II, to assess trunk control ability in multiple dimensions. It is capable of implementing external interventions and providing active protection during evaluation, and collecting kinematic, kinetic, and center of pressure data simultaneously. This reduces the evaluation time and allows for simultaneous evaluation. We recruited 10 patients with spinal cord injury, and assessed their trunk muscle strength, range of motion and resistance ability. The relative and absolute reliability were analyzed by ICC and %SEM, and the validity was examined by the correlation metrics. Results indicate heterogeneity of trunk control abilities for patients with spinal cord injury in different directions, guiding therapists to focus on the areas where the patient's abilities are weak. 10/12 metrics in ROM and resistance assessment exhibit good reliability with high ICC (> 0.75) and low %SEM ( © 1964-2012 IEEE.

关键词： Electrotherapeutics

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Spatial Graph-Based Localization and Navigation on Scaleless Floorplan

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IEEE robotics and Automation Letters 2024年第4期9卷 3932-3939页

作者： Ewe, Zu Lin Chang, Fu-Hao Huang, Yi-Shiang Fu, Li-Chen National Taiwan University Department of Electrical Engineering Taipei100 Taiwan National Taiwan University NTU Center for Artificial Intelligence and Advanced Robotics Taipei100 Taiwan

Effective navigation in unfamiliar environments remains a critical challenge for successful deployment. Current navigation methods, which rely on autonomous or teleoperated exploration and map building, pose technical difficulties for inexperienced end-users. In contrast, humans can effectively navigate using abstract floorplans, suggesting the potential for service robots to leverage similar techniques. The practical application of floorplan-based navigation;however, is currently limited by methods that require pre-exploration or floorplan with accurate measurements or scale. This letter aims to address the aforementioned challenges and investigate the feasibility of floorplan-based navigation in unfamiliar environments. Specifically, we propose a novel scale-invariant floorplan localization method, enabling navigation without relying on precise scale information. Furthermore, we introduce an incremental graph augmentation approach that enriches the floorplan representation with traversability information derived from robot observations. Finally, we develop an efficient navigation framework capable of utilizing both the inherent structure of the floorplan and real-time observations. Experimental results demonstrate that our scale-invariant floorplan localization method outperforms baseline methods in most cases when floorplan scale information is unavailable, and our graph-based navigation system exhibits superior success and efficiency as compared to grid-based counterparts. The outcomes of this research contribute to the advancement of service robot deployment in unfamiliar environments, particularly in scenarios where extensive exploration and map building may be impractical or technically challenging for end-users. © 2016 IEEE.

关键词： Navigation systems

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