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arXiv 2023年

作者： Wang, Guangming Zheng, Yu Guo, Yanfeng Liu, Zhe Zhu, Yixiang Burgard, Wolfram Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China Department of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Electrical and Computer Engineering University of California Los Angeles United States Institute of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Technology Nuremberg Germany

Robot localization using a previously built map is essential for a variety of tasks including highly accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this paper, we present I2PNet, a novel end-to-end 2D-3D registration network. I2PNet directly registers the raw 3D point cloud with the 2D RGB image using differential modules with a unique target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. 2D-3D cost volume module implicitly constructs the soft point-to-pixel correspondence on the intrinsic-independent normalized plane of the pinhole camera model. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. We conduct extensive localization experiments on the KITTI Odometry and nuScenes datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin. In addition, I2PNet has a higher efficiency than the previous works and can perform the localization in real-time. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments

arXiv

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arXiv 2024年

作者： Wang, Jiacheng Du, Hongyang Niyato, Dusit Xiong, Zehui Kang, Jiawen Ai, Bo Han, Zhu Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore China Ministry of Education Guangzhou510006 China State Key Laboratory of Rail Traffic Control and Safety Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing. © 2024, CC BY.

关键词： Artificial intelligence

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Jseun: Joint Semantic-Instance Segmentation of Point Cloud Via Neighbor Features for Indoor Scenes

SSRN

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SSRN 2023年

作者： Chen, Hui Jiang, Jinlong Chen, Wanlou Liu, Wanquan Liu, Peter Xiaoping College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Nanovision Medical Technology Co. Ltd Shanghai200000 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Efficient and accurate segmentation of the point cloud for indoor scenes is an important task for 3D scene understanding. Due to the complexity of indoor environments. It is difficult to fully extract the details of point cloud, resulting in poor recognizability in existing models. In this paper, a novel framework called joint semantic-instance segmentation via enhancedutilization of neighbor features is proposed to achieve semantic and instance segmentation of indoor scenes. This framework mines point cloud information from a local scale, but it is different from other models in that it focuses on the full mining of point cloud neighbor information. In this regard, a new neighbor feature extraction module is developed to enrich the expression of neighborhood information and enlarge the construction of the neighborhood neighbor scope. Besides, semantic and instance segmentations take advantages of each other by constructing a mutual promotion model of semantic and instance, which can obtain more accurate semantic and instance predictions for indoor scenes. The proposed scheme is evaluated on the S3DIS dataset and compared with existing methods. Experimental results demonstrate that this method is more effective and efficient than previous approaches in joint 3D semantic and instance segmentation. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Decentralized Nonconvex Robust Optimization over Unsafe Multiagent systems: System Modeling, Utility, Resilience, and Privacy Analysis

arXiv

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arXiv 2024年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Cheng, Huqiang Guo, Xiaoyu Huang, Tingwen School of Automation Central South University Changsha410083 China Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Key Laboratory of Dependable Services Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing400044 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Privacy leakage and Byzantine failures are two adverse factors to the intelligent decision-making process of multiagent systems (MASs). Considering an unsafe MAS with these two issues, this paper targets the resolution of a class of nonconvex optimization problems under the Polyak-Lojasiewicz (P-L) condition. To address this problem, we first identify and construct the unsafe MAS model. Under this kind of unfavorable MASs, we mask the local gradients with Gaussian noises and adopt a resilient aggregation method self-centered clipping (SCC) to design a differentially private (DP) decentralized Byzantine-resilient algorithm, namely DP-SCC-PL, which simultaneously achieves differential privacy and Byzantine resilience. The convergence analysis of DP-SCC-PL is challenging since the convergence error can be contributed jointly by privacy-preserving and Byzantine-resilient mechanisms, as well as the nonconvex relaxation, which is addressed via seeking the contraction relationships among the disagreement measure of reliable agents before and after SCC aggregation, together with the optimal gap. Theoretical results reveal that DP-SCC-PL achieves consensus among all reliable agents and sublinear (inexact) convergence with well-designed step-sizes. It has also been proved that if there are no privacy issues and Byzantine agents, then the asymptotic exact convergence can be recovered. Numerical experiments verify the utility, resilience, and differential privacy of DP-SCC-PL by tackling a nonconvex optimization problem satisfying the P-L condition under various Byzantine attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO systems with Low-Density Pilots

arXiv

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arXiv 2023年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Department of Automation Tsinghua University Beijing100084 China The School of Intelligent Systems Science and Engineering The GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel estimation

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Dexterous Manipulation through Imitation Learning: A Survey

arXiv

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arXiv 2025年

作者： An, Shan Meng, Ziyu Tang, Chao Zhou, Yuning Liu, Tengyu Ding, Fangqiang Zhang, Shufang Mu, Yao Song, Ran Zhang, Wei Hou, Zeng-Guang Zhang, Hong School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Control Science and Engineering Shandong University Jinan250061 China State Key Laboratory of General Artificial Intelligence Beijing100086 China Department of Mechanical and Process Engineering ETH Zurich Zurich8092 Switzerland Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Informatics University of Edinburgh EH8 9AB United Kingdom Department of Computer Science University of Hong Kong 999077 Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Grippers

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Platoon Keeping and Collision Avoidance Based on Distributed Model Predictive control Under Arbitrary Communication Chain Malfunction

Platoon Keeping and Collision Avoidance Based on Distributed...

引用

Chinese Automation Congress (CAC)

作者： Nan Chen Yutao Chen Dingci Lin Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China 5G+Industrial Internet Institute Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

In this paper, a platoon keeping and collision avoidance method based on distributed model predictive control(DMPC) is proposed to solve the problem of arbitrary communication chain malfunctions under a bidirectional-leader topology. Firstly, for the platoon under normal communication, a DMPC controller is designed by using the trajectory information of the leader and predecessor vehicle, which also satisfies the situation when communication chain between the follower (except follower 1) and the leader malfunction. Secondly, for other communication chain malfunction situation, we proposed the corresponding communication topology transformation scheme and controller transformation scheme, which makes the proposed control scheme can adapt to arbitrary communication chain malfunction situation. Finally, as for the control strategy proposed in this paper, some simulation experiments are carried out.

关键词： Adaptation models Automation Predictive models Topology Trajectory Collision avoidance Predictive control

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Distributed Differential Game for Formation control of Multi-UAV with Obstacle Avoidance

Distributed Differential Game for Formation Control of Multi...

引用

Chinese Automation Congress (CAC)

作者： Zeyi Li Wenyan Xue Dongfang Li Dingci Lin Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China 5G+Industrial Internet Institute Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

For the multi-unmanned aerial vehicle formation control (MUAVFC) in obstacle environments, this work proposes a new distributed non-cooperative differential game framework. This scheme adequately reflects the dynamic interaction of each UAV in the formation control process and effectively reflects the interaction of individual strategies and behaviors. Meanwhile, the game model is added with obstacle avoidance penalty constraints to achieve obstacle avoidance in the UAV formation flight and ensure flight safety. Each UAV needs to obtain the information of its neighbors following the communication topology to minimize the cost function, so that all strategy converges to Nash equilibrium. Based on the existing differential game method that only considers the formation goal, this work adds a penalty function term representing the distance between obstacles in the game model to achieve flight obstacle avoidance. In addition, this work strictly proves the existence of Nash equilibrium. The simulation results verify that the UAVs in this scheme have successfully formed a formation.

关键词： Simulation Differential games Games Autonomous aerial vehicles Nash equilibrium Formation control Safety

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A Dynamic Safe Arbitrator for Human-Machine Shared control

A Dynamic Safe Arbitrator for Human-Machine Shared Control

引用

Chinese Automation Congress (CAC)

作者： Haocong Chen Yutao Chen Wu Wang Dingci Lin Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China 5G+Industrial Internet Institute Fuzhou University Fuzhou China Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

Shared control has become a popular subject of human-machine hybrid intelligence. This paper presents a dynamic safety arbitrator for shared control in an automated vehicle. The vehicle is controlled autonomously by a model predictive controller (MPC) tracking a given trajectory, while at the same time human input is provided via a joystick. Unlike traditional mixed-initiative control methods that blend weighted human and vehicle inputs, the proposed arbitrator first adopts control barrier function (CBF) to generate weights for human and vehicle inputs under safety critical situations, and then employs the null-space based control (NSBC) to merge the two inputs with priorities determined by the weights. As a result, the final input to the vehicle has a clear physical meaning that represents dynamic priorities of human and vehicle autonomous inputs, in which the higher priority input is fully adopted and the lower priority input is partially adopted without intervening the higher priority input. An immediate advantage of the proposed arbitrator is that there is no need to tune the weights between human and autonomous inputs. Simulation results show that the dynamic safe arbitrator is effective for shared control.

关键词： Automation Decision making Predictive models Real-time systems Human in the loop Safety Trajectory

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A QoE Evaluation and Adaptation Method for Multi-player Online Games 14th

A QoE Evaluation and Adaptation Method for Multi-player Onli...

引用

14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Wang, Zaijian Guo, Wei Zhao, Le Yang, Weidong Mao, Shiwen Li, Zhipeng The School of Physics and Electronic Information Anhui Normal University Anhui Wuhu241002 China The Key Laboratory of Grain Information Processing and Control at Henan University of Technology Ministry of Education Henan Zhengzhou450001 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Anhui Province Key Laboratory of Optoelectric Materials Science and Technology School of Physics and electronic information Anhui Normal University Wuhu241002 China

ISBN: (纸本)9783030898137

To evaluate the performance of multi-player online games (MPOGs) (e.g., the online Chinese poker game), this paper presents a novel Effective Quality of Experience (EQoE) concept that considers the interaction among multiple players. A novel QoE evaluation and adaptation method is proposed utilizing EQoE. Specifically, the proposed method assigns different weights to different players according to their roles in the multi-player online poker game. Some players with a lower weight are willing to reduce their own QoE to save network resources for players with a higher weight in order to keep the game going under poor network conditions. Our simulation study demonstrates the feasibility of the proposed QoE evaluation and adaptation method. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Quality control

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