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Allocation and Migration of Virtual Machines Using Machine Learning

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Computers, Materials & Continua 2022年第2期70卷 3349-3364页

作者： Suruchi Talwani Khaled Alhazmi Jimmy Singla Hasan JAlyamani Ali Kashif Bashir School of Computer Science&Engg. Lovely Professional UniversityPunjabIndia National Center for Robotics and Internet of Things Technology Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computing and Mathematics ManchesterMetropolitan UniversityManchesterM156BHUK

Cloud computing promises the advent of a new era of service boosted by means of virtualization *** process of virtualization means creation of virtual infrastructure,devices,servers and computing resources needed to deploy an application *** extensively practiced technology involves selecting an efficient Virtual Machine(VM)to complete the task by transferring applications from Physical Machines(PM)to VM or from VM to *** whole process is very challenging not only in terms of computation but also in terms of energy and *** research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data *** Learning(ML)based Artificial Bee Colony(ABC)is used to rank the VM with respect to the load while considering the energy efficiency as a crucial *** most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy,applications are migrated fromoneVMto *** simulation analysis is performed inMatlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.

关键词： Cloud computing VM allocation VM migration machine learning

Robust Bayesian Scene Reconstruction by Leveraging Retrieval-Augmented Priors

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arXiv 2024年

作者： Wright, Herbert Zhi, Weiming Johnson-Roberson, Matthew Hermans, Tucker University of Utah Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT United States Robotics Institute Carnegie Mellon University PittsburghPA United States NVIDIA Corporation SeattleWA United States

Constructing 3D representations of object geometry is critical for many downstream robotics tasks, particularly tabletop manipulation problems. These representations must be built from potentially noisy partial observations. In this work, we focus on the problem of reconstructing a multi-object scene from a single RGBD image, generally from a fixed camera in the scene. Traditional scene representation methods generally cannot infer the geometry of unobserved regions of the objects from the image. Attempts have been made to leverage deep learning to train on a dataset of observed objects and representations, and then generalize to new observations. However, this can be brittle to noisy real-world observations and objects not contained in the dataset, and cannot reason about their confidence. We propose BRRP, a reconstruction method that leverages preexisting mesh datasets to build an informative prior during robust probabilistic reconstruction. In order to make our method more efficient, we introduce the concept of retrieval-augmented prior, where we retrieve relevant components of our prior distribution during inference. The prior is used to estimate the geometry of occluded portions of the in-scene objects. Our method produces a distribution over object shape that can be used for reconstruction or measuring uncertainty. We evaluate our method in both simulated scenes and in the real world. We demonstrate the robustness of our method against deep learning-only approaches while being more accurate than a method without an informative prior. © 2024, CC BY.

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V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes

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arXiv 2024年

The ability to construct concise scene representations from sensor input is central to the field of robotics. This paper addresses the problem of robustly creating a 3D representation of a tabletop scene from a segmented RGB-D image. These representations are then critical for a range of downstream manipulation tasks. Many previous attempts to tackle this problem do not capture accurate uncertainty, which is required to subsequently produce safe motion plans. In this paper, we cast the representation of 3D tabletop scenes as a multi-class classification problem. To tackle this, we introduce V-PRISM, a framework and method for robustly creating probabilistic 3D segmentation maps of tabletop scenes. Our maps contain both occupancy estimates, segmentation information, and principled uncertainty measures. We evaluate the robustness of our method in (1) procedurally generated scenes using open-source object datasets, and (2) real-world tabletop data collected from a depth camera. Our experiments show that our approach outperforms alternative continuous reconstruction approaches that do not explicitly reason about objects in a multi-class formulation. © 2024, CC BY.

关键词： Uncertainty analysis

V-TX: A VR-Based Deployment and Evaluation System for Wearable Telexistence Robotic Systems

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V-TX: A VR-Based Deployment and Evaluation System for Wearab...

International Conference on Cyberworlds

作者： Abdullah Iskandar Hala Khazer Shebli Aburajouh Eman Hussain Al-Shaer Fatima Thabit Al-Yafei Bushra Ahmad Al-Arqaban Moneera Al-Yousef Pinchu Yang Osama Halabi Mohammed Al-Sada Tatsuo Nakajima Computer Science and Engineering Waseda University Japan School of Computing Telkom University Indonesia Computer Science and Engineering Qatar University Qatar Future Robotics Organization Waseda University Japan KINDI Computing Research Center Qatar University Qatar

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Wearable telexistence robotic systems have risen in prominence after the COVID-19 pandemic, being vital for various applications. However, their design, fabrication, and evaluation require substantial resources and are unsupported by robot simulation systems. Therefore, we present V-TX, which is a VR-based system for deploying and evaluating wearable telexistence systems. V-TX extends key telexistence systems’ components with versatility for customizing different attributes based on the intended application. To demonstrate the potential of V-TX, we replicated a real telexistence robot in V-TX, called Piton, and showed a novel potential use for exploring and evaluating different robot designs, interaction methods, and deployments. We conducted a comparative evaluation between the real Piton robot and its V-TX implementation. The results show that V-TX gave results that match those of the real counterpart across the main factors of telexistence, indicating its viability as an alternative to traditional evaluation approaches using real robots. Lastly, we discuss the limitations and present future research directions.

关键词： Fabrication COVID-19 Pandemics Design methodology Virtual reality Telexistence Robots Testing

A Radial Folding Mechanism to Enable Surgical Continuum Manipulators to Fit Through Smaller Ports

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A Radial Folding Mechanism to Enable Surgical Continuum Mani...

International Symposium on Medical robotics (ISMR)

作者： Mariana E. Smith Daniel S. Esser Margaret Rox Alan Kuntz Robert J. Webster Department of Mechanical Engineering Vanderbilt University Nashville TN Robotics Center Kahlert School of Computing University of Utah Salt Lake City UT USA

Tendon driven continuum robots promise tentacle-like dexterity in minimally invasive surgical applications. These robots are subject to conflicting design goals. It is desirable for the robot to fit through the smallest port possible, yet it is also desirable for the robot's diameter to be large, so that for a given tension, tendons can apply larger actuation moments to the robot. To satisfy both goals simultaneously, we propose a new radial folding mechanism that facilitates a 2.5x diameter change along the robot. We show that our folding tendon manipulator can be modeled by existing mechanics-based models. Comparing at consistent tendon tensions, the robot has a larger range of motion and larger stiffness than a non-folding continuum robot that fits through the same sized entry port.

关键词： Minimally invasive surgery Shape Shape measurement Prototypes Manipulators Robot sensing systems Pins

The dynamic nature of trust: Trust in Human-Robot Interaction revisited

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arXiv 2023年

作者： Rhim, Jimin Kwak, Sonya S. Lim, Angelica Millar, Jason University of Ottawa School of Engineering Design and Teaching Innovation Canada KIST Center for Intelligent and Interactive Robotics Korea Republic of Simon Fraser University School of Computing Science Canada

The role of robots is expanding from tool to collaborator. Socially assistive robots (SARs) are an example of collaborative robots that assist humans in the real world. As robots enter our social sphere, unforeseen risks occur during human-robot interaction (HRI), as everyday human space is full of uncertainties. Risk introduces an element of trust, so understanding human trust in the robot is imperative to initiate and maintain interactions with robots over time. While many scholars have investigated the issue of human-robot trust, a significant portion of that discussion is rooted in the human-automation interaction literature. As robots are no longer mere instruments, but social agents that co-exist with humans, we need a new lens to investigate the longitudinal dynamic nature of trust in HRI. In this position paper, we contend that focusing on the dynamic nature of trust as a new inquiry will help us better design trustworthy robots. © 2023, CC BY.

关键词： Machine design

Accounting for Hysteresis in the Forward Kinematics of Nonlinearly-Routed Tendon-Driven Continuum Robots via a Learned Deep Decoder Network

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arXiv 2024年

作者： Cho, Brian Y. Esser, Daniel S. Thompson, Jordan Thach, Bao Webster, Robert J. Kuntz, Alan Robotics Center The Kahlert School of Computing The University of Utah Salt Lake CityUT84112 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37203 United States

— Tendon-driven continuum robots have been gaining popularity in medical applications due to their ability to curve around complex anatomical structures, potentially reducing the invasiveness of surgery. However, accurate modeling is required to plan and control the movements of these flexible robots. Physics-based models have limitations due to unmodeled effects, leading to mismatches between model prediction and actual robot shape. Recently proposed learning-based methods have been shown to overcome some of these limitations but do not account for hysteresis, a significant source of error for these robots. To overcome these challenges, we propose a novel deep decoder neural network that predicts the complete shape of tendon-driven robots using point clouds as the shape representation, conditioned on prior configurations to account for hysteresis. We evaluate our method on a physical tendon-driven robot and show that our network model accurately predicts the robot’s shape, significantly outperforming a state-of-the-art physics-based model and a learning-based model that does not account for hysteresis. Copyright © 2024, The Authors. All rights reserved.

关键词： Kinematics

Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN Computer Science 2023年第1期4卷 53页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN Computer vision Deep learning Smart glasses Smoke monitoring

DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects

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arXiv 2023年

作者： Thach, Bao Cho, Brian Y. Ho, Shing-Hei Hermans, Tucker Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States NVIDIA Corporation SeattleWA United States

Applications in fields ranging from home care to warehouse fulfillment to surgical assistance require robots to reliably manipulate the shape of 3D deformable objects. Analytic models of elastic, 3D deformable objects require numerous parameters to describe the potentially infinite degrees of freedom present in determining the object’s shape. Previous attempts at performing 3D shape control rely on hand-crafted features to represent the object shape and require training of object-specific control models. We overcome these issues through the use of our novel DeformerNet neural network architecture, which operates on a partial-view point cloud of the manipulated object and a point cloud of the goal shape to learn a low-dimensional representation of the object shape. This shape embedding enables the robot to learn a visual servo controller that computes the desired robot end-effector action to iteratively deform the object toward the target shape. We demonstrate both in simulation and on a physical robot that DeformerNet reliably generalizes to object shapes and material stiffness not seen during training, including ex vivo chicken muscle tissue. Crucially, using DeformerNet, the robot successfully accomplishes three surgical sub-tasks: retraction (moving tissue aside to access a site underneath it), tissue wrapping (a sub-task in procedures like aortic stent placements), and connecting two tubular pieces of tissue (a subtask in anastomosis). Copyright © 2023, The Authors. All rights reserved.

关键词： Tissue