检索结果-内蒙古大学图书馆

Closed-Loop Placement Planning for Regrasping and Reconstruction With Single-View RGB-D Images

IEEE Transactions on Automation Science and Engineering 2025年 22卷 14084-14095页

作者： Chen, Zhiyuan Liu, Jiangshan Chen, Ronghao Wang, Jiankun Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China Southern University of Science and Technology Jiaxing Research Institute Jiaxing030024 China

In robotic reorientation, achieving certain positions or orientations within a workspace can be difficult due to collisions and interference between objects and the environment. Regrasping is a technology that overcomes limitations on object reorientation using multiple pick-and-place actions. However, constructing a regrasping graph, which consists of predicted intermediate placements, is challenging with only partial observations of unknown objects. To address this challenge, we design a closed-loop placement planning system using single-view RGB-D images. This system seamlessly integrates perception and manipulation processes, capturing RGB-D images for object reconstruction while predicting intermediate placements for constructing regrasping graphs simultaneously. To optimize the regrasping graph construction, we propose a Truncated Signed Distance Function (TSDF)-based placement evaluation algorithm that selects the next-best-placement by estimating the information gain and retention of the placements. Additionally, we adopt a novel data fusion method based on predicted intermediate placements to enhance object reconstruction and TSDF updates, effectively mitigating in-hand errors. Our system surpasses baselines in accuracy and diversity of placement predictions. It demonstrates superior performance in object reconstruction, excelling in quantitative analysis and visualization. Real-world experiments validate the effectiveness of our system in reorientation and reconstruction tasks on real industrial parts, demonstrating its robustness and applicability in practical scenarios. © 2004-2012 IEEE.

关键词： Data fusion

来源：评论

学校读者我要写书评

暂无评论

A Hierarchical Progressive perception System for Autonomous Luggage Trolley Collection

引用

IEEE Transactions on Automation Science and Engineering 2025年 22卷 14484-14497页

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China Southern University of Science and Technology Jiaxing Research Institute Jiaxing314000 China Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China

Advancements in intelligent vehicle technologies and autonomous driving are now used for tasks like collecting luggage trolleys at airports. The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys. However, existing methods for detecting and locating luggage trolleys often fail when they are not fully visible. To address this, we introduce the hierarchical progressive perception system, which enhances the detection and localization of luggage trolleys under partial occlusion. The proposed system integrates the hierarchical process structure and progressive perception strategy. This innovative structure processes the luggage trolley’s position and orientation separately. It can accurately determine the luggage trolley’s position with just one well-detected keypoint and estimate its orientation when partially occluded. Once the luggage trolley’s initial pose is detected, the progressive perception strategy continuously refines this information until the robot begins grasping. The proposed system only needs RGB images for labeling and training, eliminating the need for complex data collection and annotation. The experiments on detection and localization demonstrate that the proposed system is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. © 2004-2012 IEEE.

关键词： Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Real-Time Optimal Charging Strategy for Battery Swapping Stations Under Time-of-Use Pricing

引用

IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2043-2056页

作者： Yan, Huanyu Sun, Chenxi Liao, Huanxin Tang, Xiaoying School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Shenzhen518172 China

Battery Swapping Stations (BSSs), the emerging infrastructure for electric vehicles (EVs), are swiftly proliferating facilities bridging energy and transportation networks. As the power grid's demand-side-management approach evolves, the optimal charging strategy for competitive BSSs needs further investigation. This paper proposes a real-time optimal charging strategy for each non-cooperative BSS operating under a unified power grid that implements Time-of-use (TOU) pricing. We construct a non-cooperative game model to encapsulate the competition among BSSs under the EV reservation mechanism. To resolve the game, we prove the existence of a unique Nash Equilibrium under any number of players and swapping prices, and design an algorithm to solve the equilibrium. Additionally, we suggest strategies for EVs without reservations. Specifically, we demonstrate the conditions under which the BSS profit diminishes when serving directly drive-in EVs. We also establish that the potential cost arising from no-show reserved EVs is limited by a constant. Simulations validate that our proposed battery charging strategy significantly enhances the profits of a 12-station BSS system. Moreover, the real-time optimal charging strategy also accomplishes peak-shaving over multiple time periods. © 2013 IEEE.

关键词： Charging stations

来源：评论

学校读者我要写书评

暂无评论

Contour Knowledge-Aware perception Learning for Semantic Segmentation

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4560-4575页

作者： You, Chao Jiao, Licheng Li, Lingling Liu, Xu Liu, Fang Ma, Wenping Yang, Shuyuan Xidian University Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China International Research Center of Intelligent Perception and Computation School of Artificial Intelligence Xi’an710071 China Xi’an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an710049 China

The diversity of contextual information is of great importance for accurate semantic segmentation. However, most methods focus on single spatial contextual information, which results in an overlap of the semantic content of categories and a loss of contour information of objects. In this article, we propose a novel contour knowledge-aware perception learning network (CKPL-Net) to capture diverse contextual information by space-category aggregation module (SCAM) and contour-aware calibration module (CACM). First, SCAM is introduced to enhance intraclass consistency and interclass differentiation of features. By integrating space-aware and category-aware attention, SCAM reduces the redundancy of features from a categorical perspective while maintaining spatial correlation of pixels, substantially avoiding the overlap of the semantic content in categories. Second, CACM is designed to maintain the integrity of objects by perceiving contour contextual information. It develops a novel contour-aware knowledge and adaptively transforms the grid structure of convolutions for boundary pixels, which effectively calibrates the representation of features near boundaries. Finally, the quantitative and qualitative analyses on the three public datasets: ISPRS Potsdam dataset, ISPRS Vaihingen dataset, and WHDLD dataset, demonstrate that the proposed CKPL-Net achieves superior performance compared with prevalent methods, which indicates diverse contextual information is beneficial for accurate segmentation. © 1991-2012 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Incorporating Bounded Rationality into Electric Vehicle Highway Charging Decisions: A Bayesian Game Analysis

引用

IEEE Internet of Things Journal 2025年第11期12卷 15249-15260页

作者： Yan, Huanyu Tang, Xiaoying The Chinese University of Hon g Kong School of Science and Engineering Guangdong Shenzhen518172 China The Chinese University of Hon g Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518172 China The Chinese University of Hon g Kong Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network School of Science and Engineering Guangdong Shenzhen518172 China

Electric vehicles (EVs) represent a critical intelligent terminal within the Internet of Things (IoT). Despite the year-on-year growth in EV penetration, the highway driving experience still requires improvement. Accurate prediction of EV highway charging behavior is crucial to addressing this issue. This paper introduces a novel bounded rationality framework to analyze highway charging decisions. Specifically, we utilize prospect theory to capture the tendency of drivers to reserve more electricity than theoretically necessary. We then propose a Bayesian game in which EV drivers, unaware of others' decisions, aim to minimize costs, including range anxiety, charging fees, and queuing time. To gain insights into the game, we prove the existence and uniqueness of the Bayesian Nash Equilibrium in two practical scenarios. Our numerical experiments, based on real-life data, demonstrate that drivers' risk aversion tendency significantly influence EV charging decisions, charging demand, queuing lengths at charging stations, and the departure rate on the highway network. Furthermore, our strategy reduces cumulative EV cost and CSs' charging costs compared to other benchmarks. © 2014 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

DE-Unet: Dual-encoder U-Net for Ultra-high Resolution Remote Sensing Image Segmentation

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12290-12302页

作者： Liu, Ye Song, Shitao Wang, Miaohui Gao, Hao Liu, Jun Nanjing University of Posts and Telecommunications School of Automation and Artificial Intelligence China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Lancaster University School of Computing and Communications United Kingdom

In recent years, there has been a growing demand for remote sensing image semantic segmentation in various applications. The key to semantic segmentation lies in the ability to globally comprehend the input image. While recent transformer-based methods can effectively capture global contextual information, they suffer from high computational complexity, particularly when it comes to ultra-high resolution (UHR) remote sensing images, it is even more challenging for these methods to achieve a satisfactory balance between accuracy and computation speed. To address these issues, we propose in this paper a CNN-based dual-encoder U-Net for effective and efficient UHR image segmentation. Our method incorporates dual encoders into the symmetrical framework of U-Net. The dual encoders endow the network with strong global and local perception capabilities simultaneously, while the U-Net's symmetrical structure guarantees the network's robust decoding ability. Additionally, multi-path skip connections ensure ample information exchange between the dual encoders, as well as between the encoders and decoders. Furthermore, we proposes a context-aware modulation fusion module that guides the encoder-encoder and encoder-decoder data fusion through global receptive fields. Experiments conducted on public UHR remote sensing datasets such as the Inria Aerial and DeepGlobe have demonstrated the effectiveness of proposed method. Specifically on the Inria Aerial dataset, our method achieves a 77.42% mIoU which outperforms the baseline [1] by 3.14% while maintaining comparable inference speed. © 2008-2012 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

DS-Seq: Deriving Smooth 3D Human Motion Sequences from Video Time Cues 41st

DS-Seq: Deriving Smooth 3D Human Motion Sequences from Vide...

引用

41st Computer Graphics International Conference, CGI 2024

作者： Peng, Tao Peng, Delang Li, Li Liu, Junping Zhang, Zili Hu, Xinrong School of Computer and Artificial Intelligence Wuhan Textile University Wuhan China China NationalTextile and ApparelCouncil Key Laboratory of Intelligent Perception and Computing Shenzhen China

ISBN: (纸本)9783031818059

When applying the methods for reconstructing the 3D poses and shapes of human bodies based on single-frame images to videos, the insufficient reconstruction accuracy of individual frames and the unbalanced utilization of information from the current frame, past frames, and future frames often lead to the inability to restore high-precision and smooth 3D human motion. Specifically, this results in irregular jitter in the generated human motion. To address this issue, we propose a mesh recovery system (DS-Seq). By utilizing a higher-precision 2D feature detection framework and leveraging a motion constraint framework based on temporal features to equally incorporate information from past and future frames, our DS-Seq can reconstruct smooth 3D human motion. Additionally, the precision of 3D poses and shapes per frame achieved by our DS-Seq surpasses that of the current state-of-the-art method TCMR by over 10%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D modeling

来源：评论

学校读者我要写书评

暂无评论

ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle

arXiv

引用

arXiv 2025年

作者： Wang, Yinchuan Ren, Bin Zhang, Xiang Wang, Pengyu Wang, Chaoqun Song, Rui Li, Yibin Meng, Max Q.-H. School of Control Science and Engineering Shandong University Jinan China Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

LiDAR-based SLAM is recognized as one effective method to offer localization guidance in rough environments. However, off-the-shelf LiDAR-based SLAM methods suffer from significant pose estimation drifts, particularly components relevant to the vertical direction, when passing to uneven terrains. This deficiency typically leads to a conspicuously distorted global map. In this article, a LiDAR-based SLAM method is presented to improve the accuracy of pose estimations for ground vehicles in rough terrains, which is termed Rotation-Optimized LiDAR-Only (ROLO) SLAM. The method exploits a forward location prediction to coarsely eliminate the location difference of consecutive scans, thereby enabling separate and accurate determination of the location and orientation at the front-end. Furthermore, we adopt a parallel-capable spatial voxelization for correspondence-matching. We develop a spherical alignment-guided rotation registration within each voxel to estimate the rotation of vehicle. By incorporating geometric alignment, we introduce the motion constraint into the optimization formulation to enhance the rapid and effective estimation of LiDAR's translation. Subsequently, we extract several keyframes to construct the submap and exploit an alignment from the current scan to the submap for precise pose estimation. Meanwhile, a global-scale factor graph is established to aid in the reduction of cumulative errors. In various scenes, diverse experiments have been conducted to evaluate our method. The results demonstrate that ROLO-SLAM excels in pose estimation of ground vehicles and outperforms existing state-of-the-art LiDAR SLAM frameworks. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetic levitation vehicles

来源：评论

学校读者我要写书评

暂无评论

IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution

引用

Neuroscience Bulletin 2025年第2期41卷 344-348页

作者： Chao-Yu Yang Yan Shen Xiaoyang Qi Lufeng Ding Yanyang Xiao Qingyuan Zhu Hao Wang Cheng Xu Pak-Ming Lau Pengcheng Zhou Fang Xu Guo-Qiang Bi Division of Life Sciences and Medicine CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei230026China Interdisciplinary Center for Brain Information Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Center for Integrative Imaging Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei230027China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application MoE Key Laboratory of Brain-Inspired Intelligent Perception and CognitionSchool of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei230088China Faculty of Life and Health Sciences Shenzhen University of Advanced TechnologyShenzhen518107China CAS Key Laboratory of Brain Connectome and Manipulation The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China

Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing ***,large brain samples pose major challenges to this approach.

关键词： brain mapping three dimensional fluorescence microscopy mesoscale brain mapping lightsheet based methods block face methods high throughput volumetric imaging tissue clearing techniques

来源：评论

学校读者我要写书评

暂无评论

Complete Gait Phases Recognition Based on Muscle Synergy Using PSO-CNN-LSTM Algorithm

引用

IEEE Sensors Journal 2025年第10期25卷 16775-16786页

作者： Zhang, Kewen Li, Xiaoling Chen, Xin Yu, Longjie Jin, Yinan Fan, Bingfei Du, Mingyu Bao, Guanjun Wu, Xinyu Cai, Shibo Zhejiang University of Technology College of Mechanical Engineering Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education Hangzhou China Zhejiang University of Technology College of Mechanical Engineering Hangzhou China ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship ZJUT Yinhu Research Institute of Innovation and Entrepreneurship Fuyang District Hangzhou311400 China Chinese Academy of Sciences Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen China

Accurately recognizing gait phases, by applying proper instrumentation and measurement, is significant in walking rehabilitation training for patients with impaired mobility. In this study, seven phases of complete stand-walk-stand cycle as well as continuous daily walking were recognized based on muscle synergy and PSO-CNN-LSTM model. Firstly, the complete stand-walk-stand walking cycle were divided into starting phase, terminal swing phase, loading response phase, mid-stance phase, terminal stance phase, initial swing phase, and stopping phase, based on the features of surface electromyography (sEMG) collected by portable sEMG acquisition system and measured motion data of lower limb. Secondly, a muscle weight matrix and an activation sequence matrix were calculated by using non-negative matrix factorization (NMF). Finally, a PSO-CNN-LSTM network was designed to recognize the complete stand-walk-stand cycle by applying above mentioned matrices as input. Fourteen subjects volunteered to perform linear walking experiments under procedure to verify the feasibility of the proposed approach by comparing results with other classifiers and features. Experimental results show that the proposed approach was capable of achieving an average recognition accuracy of up to 85.384%. This work will offer promising gait recognition method for rehabilitation robots to achieve natural and flexible human-robot interaction. © 2001-2012 IEEE.

关键词： Non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：