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An Intelligent Deterministic Scheduling Method for Ultra-Low Latency Communication in Edge Enabled Industrial Internet of Things

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arXiv 2022年

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada The Computer school Hubei University of Arts and Science Xiangyang441000 China The National Yunlin University of Science and Technology Taiwan The Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, Time Sensitive Network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then a non-collision theory based deterministic scheduling (NDS) method is proposed to achieve ultra-low latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultra-low latency services and guarantee efficient bandwidth utilization. © 2022, CC BY.

关键词： Scheduling

D-VPnet: A Network for Real-time Dominant Vanishing Point Detection in Natural Scenes

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arXiv 2020年

作者： Bo Liu, Yin Ming, Zeng Meng, Qing-Hao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin300072 China

As an important part of linear perspective, vanishing points (VPs) provide useful clues for mapping objects from 2D photos to 3D space. Existing methods are mainly focused on extracting structural features such as lines or contours and then clustering these features to detect VPs. However, these techniques suffer from ambiguous information due to the large number of line segments and contours detected in outdoor environments. In this paper, we present a new convolutional neural network (CNN) to detect dominant VPs in natural scenes, i.e., the Dominant Vanishing Point detection Network (D-VPnet). The key component of our method is the feature line-segment proposal unit (FLPU), which can be directly utilized to predict the location of the dominant VP. Moreover, the model also uses the two main parallel lines as an assistant to determine the position of the dominant VP. The proposed method was tested using a public dataset and a Parallel Line based Vanishing Point (PLVP) dataset. The experimental results suggest that the detection accuracy of our approach outperforms those of state-of-the-art methods under various conditions in real-time, achieving rates of 115fps. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

Efficient Path Planning in Narrow Passages for Robots with Ellipsoidal Components

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arXiv 2021年

作者： Ruan, Sipu Poblete, Karen L. Wu, Hongtao Ma, Qianli Chirikjian, Gregory S. Department of Mechanical Engineering National University of Singapore Singapore Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Epic Systems VeronaWI53593 United States Motional Inc. PittsburghPA15207 United States

Path planning has long been one of the major research areas in robotics, with PRM and RRT being two of the most effective classes of planners. Though generally very efficient, these sampling-based planners can become computationally expensive in the important case of "narrow passages". This paper develops a path planning paradigm specifically formulated for narrow passage problems. The core is based on planning for rigid-body robots encapsulated by unions of ellipsoids. Each environmental feature is represented geometrically using a strictly convex body with a C1 boundary (e.g., superquadric). The main benefit of doing this is that configuration-space obstacles can be parameterized explicitly in closed form, thereby allowing prior knowledge to be used to avoid sampling infeasible configurations. Then, by characterizing a tight volume bound for multiple ellipsoids, robot transitions involving rotations are guaranteed to be collision-free without needing to perform traditional collision detection. Furthermore, by combining with a stochastic sampling strategy, the proposed planning framework can be extended to solving higher dimensional problems in which the robot has a moving base and articulated appendages. Benchmark results show that the proposed framework often outperforms the sampling-based planners in terms of computational time and success rate in finding a path through narrow corridors for both single-body robots and those with higher dimensional configuration spaces. Physical experiments using the proposed framework are further demonstrated on a humanoid robot that walks in several cluttered environments with narrow passages. © 2021, CC BY-NC-ND.

关键词： Motion planning

Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression

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arXiv 2020年

作者： Liu, Yin-Bo Zeng, Ming Meng, Qing-Hao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin300072 China

Unstructured road vanishing point (VP) detection is a challenging problem, especially in the field of autonomous driving. In this paper, we proposed a novel solution combining the convolutional neural network (CNN) and heatmap regression to detect unstructured road VP. The proposed algorithm firstly adopts a lightweight backbone, i.e., depthwise convolution modified HRNet, to extract hierarchical features of the unstructured road image. Then, three advanced strategies, i.e., multi-scale supervised learning, heatmap super-resolution, and coordinate regression techniques are utilized to achieve fast and high-precision unstructured road VP detection. The empirical results on Kong’s dataset show that our proposed approach enjoys the highest detection accuracy compared with state-of-the-art methods under various conditions in real-time, achieving the highest speed of 33 fps. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

High-Precision 3D Printing by Deploying Expandable Microspheres

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SSRN

SSRN 2024年

作者： Xu, Yang Wang, Fucheng Zhang, Pei Lu, Chengbang Liu, Weixuan Guo, Yueying Ni, Zhipeng Wang, Jinqing Lu, Baoyang Lou, Qin Liang, Xiangyu Liu, Ji Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Department of Metrology and Measurement Engineering China Jiliang University Hangzhou310018 China Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen518120 China Institute of Bast Fiber Crops Center of Southern Economic Crops Chinese Academy of Agricultural Sciences Changsha410205 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Yiwu Saiens Polymer Materials Co. Ltd Jinhua322099 China Jiangxi Key Lab of Flexible Electronics Flexible Electronics Innovation Institute Jiangxi Science & Technology Normal University Nanchang330013 China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen518055 China

Stereolithography-based 3D printing (3DP) technology is widely employed in various industries, including manufacturing, healthcare, energy, biomedical, art and other fields. However, the precision issues, such as dimensional shrinkage and structural warping, significantly hinder its wide applications. In this study, we present a straightforward, efficient yet general strategy to engineer the photosensitive resins with thermally expandable microspheres, thus enabling significant improvement in structural fidelity of the 3DP constructs. Our findings demonstrate that addition of expandable microspheres substantially mitigates the volume shrinkage below 3.98%, in comparison to over 10% for commercially-available photosensitive resins. An exceptionally high precision of 0.035% in dimensional deviation could be achieved, in comparison to over 0.1% for commercial one. Furthermore, due to the low filling ratio, the improvement in 3DP precision does not sacrifice the mechanical properties, thus it does not affect the applications where those photosensitive resins are originally targeting. Our method represents an effective strategy to improve the 3D printing resolution of photosensitive resins, thus opening new directions for high precision 3D printing technology. © 2024, The Authors. All rights reserved.

关键词： Shrinkage

Accurate grid keypoint learning for efficient video prediction

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arXiv 2021年

作者： Gao, Xiaojie Jin, Yueming Dou, Qi Fu, Chi-Wing Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The CUHK T Stone Robotics Institute Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Video prediction methods generally consume substantial computing resources in training and deployment, among which keypoint-based approaches show promising improvement in efficiency by simplifying dense image prediction to light keypoint prediction. However, keypoint locations are often modeled only as continuous coordinates, so noise from semantically insignificant deviations in videos easily disrupt learning stability, leading to inaccurate keypoint modeling. In this paper, we design a new grid keypoint learning framework, aiming at a robust and explainable intermediate keypoint representation for long-term efficient video prediction. We have two major technical contributions. First, we detect keypoints by jumping among candidate locations in our raised grid space and formulate a condensation loss to encourage meaningful keypoints with strong representative capability. Second, we introduce a 2D binary map to represent the detected grid keypoints and then suggest propagating keypoint locations with stochasticity by selecting entries in the discrete grid space, thus preserving the spatial structure of keypoints in the long-term horizon for better future frame generation. Extensive experiments verify that our method outperforms the state-of-the-art stochastic video prediction methods while saves more than 98% of computing resources. We also demonstrate our method on a robotic-assisted surgery dataset with promising results. Our code is available at https://***/ xjgaocs/Grid-Keypoint-Learning. Copyright © 2021, The Authors. All rights reserved.

关键词： Location

Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM

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Enhanced Visual Feedback with Decoupled Viewpoint Control in...

IEEE-RAS International Conference on Humanoid Robots

作者： Yang Chen Leyuan Sun Mehdi Benallegue Rafael Cisneros-Limón Rohan P. Singh Kenji Kaneko Arnaud Tanguy Guillaume Caron Kenji Suzuki Abderrahmane Kheddar Fumio Kanehiro School of Integrative and Global Majors (SIGMA) University of Tsukuba Japan CNRS-AIST JRL (Joint Robotics Laboratory) IRL National Institute of Advanced Industrial Science and Technology (AIST) Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Japan MIS lab Universite de Picardie Jules Verne Amiens France Faculty of Engineering and Center for Cybernetics Research University of Tsukuba Japan

ISBN: (数字)9798350309799

ISBN: (纸本)9798350309805

In immersive humanoid robot teleoperation, there are three main shortcomings that can alter the transparency of the visual feedback: (i) the lag between the motion of the operator's and robot's head due to network communication delays or slow robot joint motion. This latency could cause a noticeable delay in the visual feedback, which jeopardizes the embodiment quality, can cause dizziness, and affects the interactivity resulting in operator frequent motion pauses for the visual feedback to settle; (ii) the mismatch between the camera's and the headset's field-of-views (FOV), the former having generally a lower FOV; and (iii) a mismatch between human's and robot's range of motions of the neck, the latter being also generally lower. In order to leverage these draw-backs, we developed a decoupled viewpoint control solution for a humanoid platform which allows visual feedback with low-latency and artificially increases the camera's FOV range to match that of the operator's headset. Our novel solution uses SLAM technology to enhance the visual feedback from a reconstructed mesh, complementing the areas that are not covered by the visual feedback from the robot. The visual feedback is presented as a point cloud in real-time to the operator. As a result, the operator is fed with real-time vision from the robot's head orientation by observing the pose of the point cloud. Balancing this kind of awareness and immersion is important in virtual reality based teleoperation, considering the safety and robustness of the control system. An experiment shows that the effectiveness of our solution.

关键词： Point cloud compression Visualization Simultaneous localization and mapping Humanoid robots Virtual reality Real-time systems Robustness

An Accelerated Iterative Learning Control Approach for X-Y precision plane motion stage

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An Accelerated Iterative Learning Control Approach for X-Y p...

作者： Tao Zhang Hongyu Yan Danping Jia Hualiang Zhang Bingjie Zhao Xu Lu Yang Liu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Dalian University of Technology School of Artificial Intelligence Shenyang University of Technology School of Information Science and Engineering Shenyang University of Technology

X-Y precision plane motion stage is widely used in precision motion control fields such as lithography machine workpiece stage,CNC machine tool,Laser measuring system,etc.,which requires high accuracy of the position *** X-Y precision plane motion stage uses two permanent magnet linear synchronous motors(PMLSM) as drivers to drive the motion table to perform XY two-degree-of-freedom *** open-closedloop PD-type iterative learning controller(ILC) approach based on accelerated convergence was proposed to improve trajectory tracking performance of *** interval was changed by real-time correction of the exponential gain matrix in the interval to accelerate the convergence *** to the λ norm,the monotone convergence of the proportion differentiation(PD) was developed for the *** controller consists of two terms:a PID controller which enhances robustness to disturbances and system parameter variability and an ILC which generally improves the performance in the repetitive tasks by using the information from previous *** experimental results show that tracking precision and convergence speed of PMLSM can be effectively improved by the scheme.

关键词： X-Y precision plane motion stage Iterative learning control tracking precision convergence rate

Development and Evaluation of a Robotic System for Safe Cardiac Sheath Delivery

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IEEE Transactions on Biomedical engineering 2025年 PP卷 PP页

作者： Wang, Weizhao Saija, Carlo Xu, Zhouyang Zeidan, Aya Wilcox, Joshua Patterson, Tiffany Redwood, Simon Wang, Shuangyi Rhode, Kawal Housden, Richard King's College London School of Biomedical Engineering and Imaging Sciences LondonWC2R 2LS United Kingdom Guy's and St Thomas' NHS Foundation Trust Cardiovascular Department LondonSE1 7EH United Kingdom King's College London School of Cardiovascular Medicine and Sciences LondonSE1 7EH United Kingdom Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing101408 China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Hong Kong

Objective: This study aimed to develop and evaluate a 3-degree-of-freedom (DoF) robotic system for the safe delivery of cardiac sheaths through challenging anatomical structures, including the fossa ovalis and pathways with tight curves. Methods: The robot and its kinematic model were built on a previously proposed single-DoF actuation module and bending model. A sheath delivery strategy (SDS) was developed, combining two control methods: tip position control to approach an optimal entry point and point-constrained control to maintain consistent navigation through this point, minimizing tissue contact. Technical performance was evaluated through trajectory-following and point-crossing tests, followed by feasibility experiments in a simulated scenario. Trials were conducted by three cardiologists using a validated phantom model under fluoroscopic guidance, comparing SDS with joint control (JC) and manual control (MC). Results: Average root mean square errors were 2.10 mm for tip position control and 1.86 mm for point-constrained control. SDS outperformed MC with significantly shorter trajectory lengths and lower root mean square jerk. Compared to JC, SDS reduced sheath-induced movements (an indirect measure of force) and increased retraction success rates at the fossa ovalis. Conclusion: The proposed robotic system reduced tissue wall contact compared to JC and provided smoother, more controlled operations than MC, ensuring safer and more effective delivery through confined pathways. Significance: This work contributes to advancing robotic-assisted cardiac sheath delivery, providing a reliable and safer method for navigating challenging anatomical structures. © 2025 IEEE Computer Society. All rights reserved.

关键词： Medical robotics