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Author Correction: Aerial additive manufacturing with multiple autonomous robots

Nature 2024年第7998期626卷 E5页

作者： Ketao Zhang Pisak Chermprayong Feng Xiao Dimos Tzoumanikas Barrie Dams Sebastian Kay Basaran Bahadir Kocer Alec Burns Lachlan Orr Talib Alhinai Christopher Choi Durgesh Dattatray Darekar Wenbin Li Steven Hirschmann Valentina Soana Shamsiah Awang Ngah Clément Grillot Sina Sareh Ashutosh Choubey Laura Margheri Vijay M Pawar Richard J Ball Chris Williams Paul Shepherd Stefan Leutenegger Robert Stuart-Smith Mirko Kovac Department of Aeronautics Imperial College London London UK. School of Engineering and Materials Science Queen Mary University of London London UK. Department of Computing Imperial College London London UK. Department of Architecture and Civil Engineering University of Bath Bath UK. Department of Computer Science University College London London UK. Materials and Technology Centre of Robotics Swiss Federal Laboratories for Materials Science and Technology (Empa) Dübendorf Switzerland. Department of Informatics Technical University of Munich Garching Germany. Stuart Weitzman School of Design University of Pennsylvania Philadelphia PA USA. Department of Aeronautics Imperial College London London UK. m.kovac@imperial.ac.uk. Materials and Technology Centre of Robotics Swiss Federal Laboratories for Materials Science and Technology (Empa) Dübendorf Switzerland. m.kovac@imperial.ac.uk.

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Robot-Assisted Vascular Shunt Insertion with the dVRK Surgical Robot

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Journal of Medical robotics Research 2023年第3-4期8卷

作者： Dharmarajan, Karthik Panitch, Will Shi, Baiyu Huang, Huang Chen, Lawrence Yunliang Moghani, Masoud Yu, Qinxi Hari, Kush Low, Thomas Fer, Danyal Garg, Animesh Goldberg, Ken Department of Electrical Engineering and Computer Sciences University of California Berkeley 94709 CA United States Department of Industrial Engineering and Operations Research University of California Berkeley 94709 CA United States Department of Mechanical and Industrial Engineering University of Toronto Toronto M5S 3G8 ON Canada Department of Computer Science University of Toronto Toronto M5S 3G8 ON Canada Robotics Laboratory Sri International Menlo Park 94025 CA United States Department of General Surgery University of California San Francisco East Bay Oakland 94602 CA United States School of Interactive Computing Georgia Institute of Technology Atlanta 30332 GA United States

Vascular shunt insertion is a common surgical procedure performed to restore blood flow to damaged tissues temporarily. It usually requires a surgeon and a surgical assistant. We consider three scenarios: (1) a surgeon is available locally;(2) a remote surgeon is available via teleoperation;(3) no surgeon is available. In each scenario, a minimally invasive surgical-assistant da Vinci robot operates in a different mode either by teleoperation or automation. Robotic assistance for this procedure is challenging due to precision and control uncertainty. The role of the robot in this task depends on the availability of a human surgeon. We propose a trimodal framework for vascular shunt insertion assisted by a da Vinci Research Kit (dVRK) robotic surgical assistant (RSA). To help further study for the community, we also present a physics-based simulated environment for shunt insertion built on top of the NVIDIA Isaac ORBIT simulator. We collect a large dataset of trajectories for the shunt insertion environment using ORBIT and implement these trajectories to show the simulator's realism, showcasing the possibility for future work to use the simulator for policy learning. Physical experiments demonstrate a success rate of 65-100% for mode (1), 100% for mode (2), and 75-95% for mode (3) across vessel phantoms with different sizes, color, and material properties. For dataset and videos, see https://***/***/ravsi. © 2023 World Scientific Publishing Company.

关键词： robot simulation Surgical robotics teleoperation visual servoing

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Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection

arXiv

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arXiv 2021年

作者： Chen, Yurong Zhang, Hui Wang, Yaonan Wu, Q. M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Hunan Changsha410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University ONP7B 5E1 Canada Vector Institute TorontoONM5G 1M1 Canada

Anomaly detection refers to identifying the observation that deviates from the normal pattern, which has been an active research area in various domains. Recently, the increasing data scale, complexity, and dimension turns the traditional representation and statistical-based outlier detection method into challenging. In this paper, we leverage the generative model in hyperspectral images anomaly detection. The gist is to model the distribution of the normal data, while the out-of-distribution sample can be viewed as the outlier. At first, the variational inference-based anomaly detection methods are investigated. We theoretically and empirically find that they are unstable due to the strong notion of distance (f-divergence) served as the regularization. Secondly, this paper introduces sliced Wasserstein distance, which is a weaker distribution measure compared with f-divergence. However, the number of randomly slicing poses a difficulty to estimate the true distance. In the end, we propose a projected sliced Wasserstein (PSW) autoencoder-based anomaly screening method. In particular, we leverage a computation-friendly eigen-decomposition method to find the principal component as slicing the high-dimensional data. Furthermore, our proposed distance can be calculated with the closed-form, even the prior distribution is not Gaussian. Comprehensive experiments conducted on various real-world hyperspectral anomaly detection benchmarks demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Spectroscopy

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On some locally symmetric embedded spaces with non-negative scalar curvature and their characterization

arXiv

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arXiv 2021年

作者： Sherif, Abbas M. Dunsby, Peter K.S. Goswami, Rituparno Center for Geometry and Phyics Institute for Basic Sciences Pohang University of Science and Technology 77 Cheongham-ro Nam-gu Gyeongbuk Pohang37673 Korea Republic of Cosmology and Gravity Group Department of Mathematics and Applied Mathematics University of Cape Town Rondebosch7701 South Africa South African Astronomical Observatory Observatory Cape Town7925 South Africa School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Private Bag X54001 Durban4000 South Africa

In this work we perform a general study of properties of a class of locally symmetric embedded hypersurfaces in spacetimes admitting a 1 + 1 + 2 spacetime decomposition. The hypersurfaces are given by specifying the form of the Ricci tensor with respect to the induced metric. These are slices of constant time in the spacetime. Firstly, the form of the Ricci tensor for general hypersurfaces is obtained and the conditions under which the general case reduces to those of constant time slices are specified. We provide a characterization of these hypersurfaces, with key physical quantities in the spacetime playing a role in specifying the local geometry of these hypersurfaces. Furthermore, we investigate the case where these hypersurfaces admit a Ricci soliton structure. The particular cases where the vector fields associated to the solitons are Killing or conformal Killing vector fields are analyzed. Finally, in the context of spacetimes with local rotational symmetry it is shown that, only spacetimes in this class with vanishing rotation and spatial twist can admit the hypersurface types considered, and that the hypersurfaces are necessarily flat. And if such hypersurface do admit a Ricci soliton structure, the soliton is steady, with the components of the soliton field being constants. © 2021, CC BY.

关键词： Solitons

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Continuity scaling: A rigorous framework for detecting and quantifying causality accurately

arXiv

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arXiv 2022年

作者： Ying, Xiong Leng, Si-Yang Ma, Huan-Fei Nie, Qing Lai, Ying-Cheng Lin, Wei School of Mathematical Sciences SCMS SCAM Fudan University Shanghai200433 China Research Institute for Intelligent Complex Systems CCSB LCNBI Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China School of Mathematical Sciences Soochow University Suzhou215006 China Department of Mathematics Department of Developmental and Cell Biology NSF-Simons Center for Multiscale Cell Fate Research University of California IrvineCA92697-3875 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287-5706 United States Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Khoshvaght, Parisa Haider, Amir Rahmani, Amir Masoud Gharehchopogh, Farhad Soleimanian Anka, Ferzat Lansky, Jan Hosseinzadeh, Mehdi Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of AI and Robotics Sejong University Seoul05006 Korea Republic of Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Fatih Sultan Mehmet Vakif University Istanbul Turkey Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Korea Republic of

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms. © 2025 Elsevier B.V.

关键词： Resource allocation

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Image Matching Algorithm based on ORB and K-Means Clustering

Image Matching Algorithm based on ORB and K-Means Clustering

引用

Information science, computer technology and Transportation (ISCTT), International Conference on

作者： Liye Zhang Fudong Cai Jinjun Wang Changfeng Lv Wei Liu Guoxin Guo Huanyun Liu Yixin Xing School of Computer Science and Technology Shandong University of Technology Zibo China Electric Power Automation Division Shandong Senter Electronic Zibo China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Jinan Xintongda Electronic Technology Co. Ltd Jinan China

ISBN: (数字)9781728185750

ISBN: (纸本)9781728185767

With the rapid development of science and technology, image processing technology plays an important role in the field of computer vision. In order to improve the matching speed and real-time requirements, this paper proposes an image matching algorithm based on ORB and K-means clustering, which can effectively improve the accuracy of image feature point location and the accuracy and efficiency of image feature matching, and reduce the time consumption. The algorithm uses sub-pixel interpolation to optimize the traditional ORB algorithm, which improves the accuracy and characteristics of clustering calculation.

关键词： Interpolation Information science Image matching Clustering algorithms Transportation Real-time systems Stereo vision

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A Miniaturised Neuromorphic Tactile Sensor integrated with an Anthropomorphic Robot Hand

A Miniaturised Neuromorphic Tactile Sensor integrated with a...

引用

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Benjamin Ward-Cherrier Jörg Conradt Manuel G. Catalano Matteo Bianchi Nathan F. Lepora University of Bristol and Bristol Robotics Laboratory University of Bristol UK School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Istituto Italiano di Tecnologia Genova Italia Universita di Pisa Pisa Italia

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Restoring tactile sensation is essential to enable in-hand manipulation and the smooth, natural control of upper-limb prosthetic devices. Here we present a platform to contribute to that long-term vision, combining an anthropomorphic robot hand (QB SoftHand) with a neuromorphic optical tactile sensor (neuroTac). Neuromorphic sensors aim to produce efficient, spike-based representations of information for bio-inspired processing. The development of this 5-fingered, sensorized hardware platform is validated with a customized mount allowing manual control of the hand. The platform is demonstrated to succesfully identify 4 objects from the YCB object set, and accurately discriminate between 4 directions of shear during stable grasps. This platform could lead to wide-ranging developments in the areas of haptics, prosthetics and telerobotics.

关键词： Neuromorphics Tactile sensors Hardware Optical sensors Object recognition Telerobotics Prosthetics

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Adaptive navigation scheme for optimal deep-sea localization using multimodal perception cues

arXiv

引用

arXiv 2019年

作者： Chavez, Arturo Gomez Xu, Qingwen Mueller, Christian A. Schwertfeger, Soren Birk, Andreas Robotics Group Computer Science and Electrical Engineering Jacobs University Bremen School of Information Science Technology of ShanghaiTech University

Underwater robot interventions require a high level of safety and reliability. A major challenge to address is a robust and accurate acquisition of localization estimates, as it is a prerequisite to enable more complex tasks, e.g. floating manipulation and mapping. State-of-the-art navigation in commercial operations, such as oil production (OGP), rely on costly instrumentation. These can be partially replaced or assisted by visual navigation methods, especially in deepsea scenarios where equipment deployment has high costs and risks. Our work presents a multimodal approach that adapts state-of-the-art methods from on-land robotics, i.e., dense point cloud generation in combination with plane representation and registration, to boost underwater localization performance. A two-stage navigation scheme is proposed that initially generates a coarse probabilistic map of the workspace, which is used to filter noise from computed point clouds and planes in the second stage. Furthermore, an adaptive decision-making approach is introduced that determines which perception cues to incorporate into the localization filter to optimize accuracy and computation performance. Our approach is investigated first in simulation and then validated with data from field trials in OGP monitoring and maintenance scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： Navigation

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Event-based motion segmentation with spatio-temporal graph cuts

arXiv

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arXiv 2020年

作者： Zhou, Yi Gallego, Guillermo Lu, Xiuyuan Liu, Siqi Shen, Shaojie School of Robotics Hunan University Changsha China The Technische Universität Berlin The Einstein Center Digital Future Berlin Germany Robotic Institute The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Identifying independently moving objects is an essential task for dynamic scene understanding. However, traditional cameras used in dynamic scenes may suffer from motion blur or exposure artifacts due to their sampling principle. By contrast, event-based cameras are novel bio-inspired sensors that offer advantages to overcome such limitations. They report pixel-wise intensity changes asynchronously, which enables them to acquire visual information at exactly the same rate as the scene dynamics. We develop a method to identify independently moving objects acquired with an event-based camera, i.e., to solve the event-based motion segmentation problem. We cast the problem as an energy minimization one involving the fitting of multiple motion models. We jointly solve two subproblems, namely event-cluster assignment (labeling) and motion model fitting, in an iterative manner by exploiting the structure of the input event data in the form of a spatio-temporal graph. Experiments on available datasets demonstrate the versatility of the method in scenes with different motion patterns and number of moving objects. The evaluation shows state-of-the-art results without having to predetermine the number of expected moving objects. We release the software and dataset under an open source licence to foster research in the emerging topic of event-based motion segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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