A Holistic Cloud-Enabled Robotics System for Real-Time Video Tracking Application

Liu, Bingwei; Chen, Yu; Blasch, Erik; Pham, Khanh; Shen, Dan; Chen, Genshe

doi:10.1007/978-3-642-40861-8_64

Cited by 55 publications

(31 citation statements)

References 16 publications

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“…The well-known attractive features of Cloud Computing include on-demand scalability of highly available and reliable pooled computing resources, secure access to metered services from anywhere, and displacement of data and services from inside to outside the organization. In our previous research, we have been exploring and evaluating the application of Cloud computing technology in robotic systems [19], mobile telemedicine Cloud [20,21,22,23], text analytics [24] and SSA [15]. While the applications seem unrelated, we found that the system framework and Cloud side considerations are similar, with differences in application specific aspects such as data-generating sensors, data processing algorithms and potential users, etc.…”

Section: Introductionmentioning

confidence: 88%

An adaptive process-based cloud infrastructure for space situational awareness applications

et al. 2014

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Space situational awareness (SSA) and defense space control capabilities are top priorities for groups that own or operate man-made spacecraft. Also, with the growing amount of space debris, there is an increase in demand for contextual understanding that necessitates the capability of collecting and processing a vast amount sensor data. Cloud computing, which features scalable and flexible storage and computing services, has been recognized as an ideal candidate that can meet the large data contextual challenges as needed by SSA. Cloud computing consists of physical service providers and middleware virtual machines together with infrastructure, platform, and software as service (IaaS, PaaS, SaaS) models. However, the typical Virtual Machine (VM) abstraction is on a per operating systems basis, which is at too low-level and limits the flexibility of a mission application architecture. In responding to this technical challenge, a novel adaptive process based cloud infrastructure for SSA applications is proposed in this paper. In addition, the details for the design rationale and a prototype is further examined. The SSA Cloud (SSAC) conceptual capability will potentially support space situation monitoring and tracking, object identification, and threat assessment. Lastly, the benefits of a more granular and flexible cloud computing resources allocation are illustrated for data processing and implementation considerations within a representative SSA system environment. We show that the container-based virtualization performs better than hypervisor-based virtualization technology in an SSA scenario.

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Section: Introductionmentioning

confidence: 88%

An adaptive process-based cloud infrastructure for space situational awareness applications

et al. 2014

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“…Among potential benefits of cloud robotics, to provide seamless and low-cost service robots is one of the most meaningful topics at the current stage. In order to simplify the problem, most cloud-based robotic systems set a very strict assumption, i.e., the resource in cloud is unconstrained [32]. As a matter of fact, most of the resources in the cloud robotics system are indeed constrained [33].…”

Section: B Current Cloud Robotic Approachesmentioning

confidence: 99%

Real-Time Multisensor Data Retrieval for Cloud Robotic Systems

Wang

Liu

Meng

2015

IEEE Trans. Automat. Sci. Eng.

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Cloud technology elevates the potential of robotics with which robots possessing various capabilities and resources may share data and combine new skills through cooperation. With multiple robots, a cloud robotic system enables intensive and complicated tasks to be carried out in an optimal and cooperative manner. Multisensor data retrieval (MSDR) is one of the key fundamental tasks to share the resources. Having attracted wide attention, MSDR is facing severe technical challenges. For example, MSDR is particularly difficult when cloud cluster hosts accommodate unpredictable data requests triggered by multiple robots operating in parallel. In these cases, near real-time responses are essential while addressing the problem of the synchronization of multisensor data simultaneously. In this paper, we present a framework targeting near real-time MSDR, which grants asynchronous access to the cloud from the robots. We propose a market-based management strategy for efficient data retrieval. It is validated by assessing several quality-of-service (QoS) criteria, with emphasis on facilitating data retrieval in near real-time. Experimental results indicate that the MSDR framework is able to achieve excellent performance under the proposed management strategy in typical cloud robotic scenarios.Note to Practitioners-This paper was motivated by the problem of sharing resources in cloud robotic systems efficiently for accomplishing real-time tasks. Existing approaches to cloud robotics bear very strict assumptions that the resources are unconstrained and ubiquitous. However, there are technical challenges for multirobot systems to access the cloud and retrieve resources in near real-time. This paper presents a general framework for setting up cloud robotic system with a novel resource management strategy. We mathematically formulate the problem of multisensor data retrieval through the cloud as a Stackelberg game, and propose an optimal solution with proof. We then define the QoS criteria for evaluation considering the constraints of robotic tasks. In the experimental scenarios, our management mechanism significantly improves the performance for multisensor data retrieval in the evaluation of QoS, CPU load, and bandwidth usage.

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“…They concluded that the proposed platform provides strong advantages in performance without compromising security. The DAvinCi project reported in 20 provides a platform for solving Fast SLAM as a paral- 24 present Cloud Enabled Robotics System (CERS) that allows robots to mesh together to form a local networked system. These robots can offload heavy computation tasks to the server side implementation of a cloud enabled software architecture.…”

Section: Remote Brain: Offloading Computationmentioning

confidence: 99%

Five Traits of Performance Enhancement Using Cloud Robotics: A Survey

Qureshi

Koubâa

2014

Procedia Computer Science

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Recently, robots and automation systems have been at the front of research with the majority of systems still operating independently using onboard computation, memory manipulation and communication. With improvements in communication technology and the increasing availability of network, new approaches where robot and automation processing is performed remotely with access to large scale datasets, support a range of functions. Cloud Robotics supplements performance enhancement of robotics and autonomous systems by providing a global infrastructure in innovative ways. This paper summarizes recent research into five traits of Cloud Robotics for performance enhancement in robotics and autonomous systems: 1) Remote Brain, 2) Big Data and Shared Knowledge-base, 3) Collective Learning, 4) Intelligence and Behavior, and 5) Cloud architectures. Towards the end, in this survey, we present future directions for research in cloud robotics.

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A Holistic Cloud-Enabled Robotics System for Real-Time Video Tracking Application

Cited by 55 publications

References 16 publications

An adaptive process-based cloud infrastructure for space situational awareness applications

An adaptive process-based cloud infrastructure for space situational awareness applications

Real-Time Multisensor Data Retrieval for Cloud Robotic Systems

Five Traits of Performance Enhancement Using Cloud Robotics: A Survey

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