Cloud RoboticsCloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low cost, smarter robots with an intelligent "brain" in the cloud. The "brain" consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.
Qing Liang,Ming Liu
IEEE Transactions on Automation Science and Engineering, no. 1 (2020): 191-206
We proposed a site survey approach that can automatically build maps of opportunistic signals for indoor localization by a dedicated surveyor using a smartphone
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Peide Cai, Xiaodong Mei,Lei Tai, Yuxiang Sun,Ming Liu
international conference on robotics and automation, pp.1-1, (2020)
To realize high-speed drift control through manifold corners for autonomous vehicles, we propose a closed-loop controller based on the model-free deep reinforcement learning algorithm soft actor-critic to control the steering angle and throttle of simulated vehicles
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IEEE Robotics and Automation Letters, no. 4 (2019): 4555-4562
The more flexible Lifelong Federated Reinforcement Learning will offer a wider range of services in cloud robotic systems
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International Journal of Distributed Sensor Networks, (2016): 3159805:1-3159805:10
The massive data can be collected from smart artifacts and transferred to the cloud through the industrial wireless network
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Comput. Networks, (2016): 158-168
Based on the model presented in this paper, we are going to build up a smart factory prototype, and develop algorithms for further optimizing system performance
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CVPR, (2016): 845-853
We have presented HyperNet, a fully trainable deep architecture for joint region proposal generation and object detection
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IEEE Sensors Journal, no. 20 (2016): 7373-7380
The softwaredefined industrial Internet of Things will facilitate the evolution of Industry 4.0
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Jiafu Wan, Shenglong Tang,Hehua Yan,Di Li, Shiyong Wang,Athanasios V. Vasilakos
IEEE Access, (2016): 2797-2807
The remaining content is organized in the following fashion: First, we describe the overall structure of the robotic cloud, and analyze several major elements of its ecology and conduct an analysis of current key issues to be solved
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Nature, no. 7553 (2015): 467-475
Current computer-aided design software was not created with free-form 3D fabrication processes in mind, and does not accommodate the complex non-homogeneous 3D designs that may be desired for soft robotics
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Automation Science and Engineering, IEEE Transactions  , no. 2 (2015): 398-409
A S illustrated in Fig. 1, the Cloud has potential to enhance a broad range of robots and automation systems
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Wireless Networks, no. 1 (2015)
We have presented a brief survey of Industry 4.0, Industrial wireless networks and wireless nodes covering a range of areas from the general to the specific
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Foundations and Trends in Robotics, no. 1 (2015): 1-104
We focus on mobile robotics applications in which point clouds are to be registered
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ESEC/SIGSOFT FSE, (2015)
We model the number of pull requests merged per month as a response against explanatory variables that measure test coverage (measured as a count of the number of source lines of code in test files;
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Automation Science and Engineering, IEEE Transactions  , no. 2 (2015): 481-493
We showed how the computing environments and the communication protocols allow robots to offload their computation to the cloud
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Sensors, no. 12 (2015)
This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data
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Microprocessors and Microsystems: Embedded Hardware Design, no. 8 (2015)
We addressed the architecture of cloud-assisted industrial cyber physical systems, and highlighted some key enabling technologies in this process
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Automation Science and Engineering, IEEE Transactions, no. 1 (2015): 85-102
This section gives a general overview of the proposed Framework, named FASEM whose main goal is to provide an automatic and dynamic services monitoring when an event occurs in an ambient environment
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IEEE T. Automation Science and Engineering, no. 2 (2015): 507-518
We proposed an architecture consists of a data center, cloud cluster hosts and robot clients
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Emerging Technologies and Factory Automation, (2015)
There, obstacles ahead detected by a 2D laser range finder are presented to the operator by a 3D representation for or in a video feedback overlaid by obstacles information for, and, on a display embedded to the chair
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IEEE T. Cybernetics, no. 12 (2014): 2626-2634
The composite neural dynamic surface control design has been investigated for a class of strict-feedback systems
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