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arXiv 2024年

作者： Jiang, Chenxing Zhang, Kunyi Yang, Sheng Shen, Shaojie Xu, Chao Gao, Fei The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Alibaba DAMO Academy Hangzhou China Huzhou Institute Zhejiang University Huzhou China

In this paper, we propose an interoceptive-only odometry system for ground robots with neural network processing and soft constraints based on the assumption of a globally continuous ground manifold. Exteroceptive sensors such as cameras, GPS and LiDAR may encounter difficulties in scenarios with poor illumination, indoor environments, dusty areas and straight tunnels. Therefore, improving the pose estimation accuracy only using interoceptive sensors is important to enhance the reliability of navigation system even in degrading scenarios mentioned above. However, interoceptive sensors like IMU and wheel encoders suffer from large drift due to noisy measurements. To overcome these challenges, the proposed system trains deep neural networks to correct the measurements from IMU and wheel encoders, while considering their uncertainty. Moreover, because ground robots can only travel on the ground, we model the ground surface as a globally continuous manifold using a dual cubic B-spline manifold to further improve the estimation accuracy by this soft constraint. A novel space-based sliding-window filtering framework is proposed to fully exploit the C2 continuity of ground manifold soft constraints and fuse all the information from raw measurements and neural networks in a yaw-independent attitude convention. Extensive experiments demonstrate that our proposed approach can outperform state-of-the-art learning-based interoceptive-only odometry methods. © 2024, CC BY.

关键词： Navigation systems

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MAPPG: Multi-Agent Phasic Policy Gradient

MAPPG: Multi-Agent Phasic Policy Gradient

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IEEE Conference on Decision and control

作者： Qi Zhang Xuetao Zhang Yisha Liu Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin China

We propose a Multi-Agent Phasic Policy Gradient (MAPPG) algorithm, which can assist agents to further alleviate the non-stationarity of the environment. Different from the existing methods, the auxiliary phase is introduced to train the local policy directly by using the environment state, which can be naturally integrated into other algorithms. Specifically, the hidden layer feature sharing is proposed, which ensures feature sharing between the global value network and the local policy network for the first time. Meanwhile, mirror descent is utilized to iteratively update the policy in the auxiliary stage, which makes the policy update more robust. Through a series of evaluations on multi-agent Particle and multi-agent Mujoco benchmark environments, the experimental results show that our method achieves higher rewards than state-of-the-art benchmarks.

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

arXiv

引用

arXiv 2024年

作者： Sun, Li Huang, Zhenhao Wu, Hua Ye, Junda Peng, Hao Yu, Zhengtao Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois ChicagoIL United States

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Human-centric assembly in smart factories

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CIRP Annals 2025年

作者： Wang, Lihui Gao, Robert X. Krüger, Jörg Váncza, József Department of Production Engineering KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Aerospace Engineering Case Western Reserve University Cleveland United States Institute for Machine Tools and Factory Management Technical University Berlin Germany Laboratory of Engineering and Management Intelligence Institute for Computer Science and Control Hungary

Assembly in future smart factories needs to address three challenges, including human centricity, sustainability, and resilience. Conventional approaches for automation in assembly have reached a bottleneck in terms of operation automomy, leaving various tasks to continued manual labour by human operators. To ease the burden on humans both physically and intellectually, human-centric assembly enhanced by augmented robots, cognitive systems, mixed reality and collaborative intelligence, assisted by thought-driven brain robotic controls, provides a promising solution. Within the context, this keynote provides an in-depth analysis of the state of human-centric assembly and identifies potentially fruitful research directions in future smart factories. © 2025 The Author(s)

关键词： Factory automation

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Topology-Guided Perception-Aware Receding Horizon Trajectory Generation for UAVs

Topology-Guided Perception-Aware Receding Horizon Trajectory...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Gang Sun Xuetao Zhang Yisha Liu Hanzhang Wang Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence also School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence the Institute of Robotics and Automatic Information System (lRAIS) Nankai University Tianjin China

The perception-aware motion planning method based on the localization uncertainty has the potential to improve the localization accuracy for robot navigation. How-ever, most of the existing perception-aware methods pre-build a global feature map and can not generate the perception- aware trajectory in real time. This paper proposes a topology- guided perception-aware receding horizon trajectory generation method, which contains a topology-guided position trajectory generation and a perception-aware yaw angle trajectory generation. Specifically, a memorable active map is built by selectively storing the visual landmarks. After that, a library of candidate topological trajectories are generated, which are then evaluated in terms of the perception quality based on the active map, smoothness, collision possibility and feasibility. In addition, the yaw angle trajectory is obtained through a front-end multiple refined path search and a back-end path- guided trajectory optimization. Comparative simulation and real-world experiments are carried out to confirm that the proposed method can keep more visual features in view and reduce the localization error.

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Neural Network-Based Prescription of Chinese Herbal Medicines 11

Neural Network-Based Prescription of Chinese Herbal Medicine...

引用

11th International Conference on Information technology in Medicine and Education, ITME 2021

作者： Zhao, Wen Lu, Weikai Zhou, Chang'en Li, Zuoyong Fan, Haoyi Lin, Xuejuan Yang, Zhaoyang Li, Candong Fujian University of Traditional Chinese Medicine School of Traditional Chinese Medicine Fuzhou350122 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China

ISBN: (纸本)9781665406796

Objective: To develop a neural network model that recommends traditional Chinese medicine (TCM) herbal prescriptions. Methods: We constructed a new dataset of diagnosis and treatment knowledge from the Treatise on Febrile Diseases. Based on TCM's logical principles of 'syndrome differentiation' and 'state recognition', a back-propagation neural network model is proposed that simulates clinical diagnosis and treatment. Results: The proposed model is a four-layer BP neural network. Experiments on the constructed dataset show that the proposed method achieved the best precision, recall, and F1-scores. Conclusion: The proposed method provides much more accurate herbal prescription recommendations than logistic regression. © 2021 IEEE.

关键词： Diagnosis

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Traffic Sign Detection With Event Cameras and DCNN

arXiv

引用

arXiv 2022年

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Department of Digital Systems Silesian University of Technology Gliwice Poland

In recent years, event cameras (DVS – Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions – parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022, CC BY.

关键词： Cameras

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MSONoC: A Metasurface-assisted Hybrid Waveguide/FSO Network-on-Chip Architecture

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Sun, Wei Li, Jingsi Guo, Pengxing He, Xiangyu Wang, Zimo Hu, Cong Liu, Weichen Hou, Weigang Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communications and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Engineering Nanyang Technological University Singapore

Optical networks-on-chips (ONoCs) have emerged as the most promising solution for on-chip interconnection networks, offering distinct advantages such as high bandwidth and low latency. However, traditional wired-based ONoCs often suffer from a high average number of hops, leading to challenges such as increased insertion loss, crosstalk noise, and energy consumption. Therefore, this paper presents a novel ONoC structure known as the metasurface ONoC (MSONoC), which integrates wired waveguide and free-space optical (FSO) transmission with the assistance of metasurfaces. Specifically, this work encompasses the design of a passive metasurface structure tailored for ONoCs, a virtual network cluster partitioning method, an optical metasurface access points select strategy, and a shortest-path routing algorithm. Performance verification is conducted using a modified Noxim simulator. The simulation results demonstrate that the proposed MSONoC offers advantages over traditional NoCs, radio frequency (RF)-based hybrid wired/wireless NoCs, and wired-based ONoCs in terms of throughput, delay, energy consumption, and signal-to-noise ratio under both synthetic traffic and real traffic models. These improvements proves the effectiveness of the MSONoC architecture in optimizing network performance, making it a promising solution for high-performance, energy-efficient communication in on-chip networks. © 2017 IEEE.

关键词： Network-on-chip

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

引用

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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