The rising prospects of cloud robotic applications

Jordan, Sandor; Haidegger, Tamás; Kovács, Levente; Felde, Imre; Rudas, Imre J.

doi:10.1109/icccyb.2013.6617612

Cited by 31 publications

(13 citation statements)

References 12 publications

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“…Cloud robotics is defined as a cloud-centric technology where the “robots are connected to cloud computing infrastructure” to get access “to distributed computing resources” with “the ability to share training and labeling data for robot learning” (Jordan et al, 2013 ; Saha and Dasgupta, 2018 ).…”

Section: Internet Of Robotic Things Taxonomymentioning

confidence: 99%

Internet of Robotic Things Intelligent Connectivity and Platforms

et al. 2020

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The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and "things" have evolved significantly. "Things" now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of "intelligent things" (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.

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Section: Internet Of Robotic Things Taxonomymentioning

confidence: 99%

Internet of Robotic Things Intelligent Connectivity and Platforms

et al. 2020

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“…For future work it would be interesting to employ the proposed system on the cloud, which would enable multiple robots to take advantage of it [17] [18].…”

Section: Discussionmentioning

confidence: 99%

Forward control of robotic arm using the information from stereo-vision tracking system

Puheim

Bundzel

Madarasz

2013

2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI)

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In this paper we present the feed-forward neural network controller of robotic arm, which makes use of tracking method applied to stereo-vision cameras mounted on the head of the humanoid robot Nao, in order to touch the tracked object. The Tracking-Learning-Detection (TLD) method, which we use to detect and track the object, is known for its state-of-art performance and high robustness. This method was adjusted to be usable with a stereo-vision camera system, in order to provide 3D spatial coordinates of the object. These coordinates are used as the input for the feed-forward controller, which controls the arm of a humanoid robot. The goal of the controller is to move the hand of the robot to the object by setting arm joints into position corresponding to the object location. The controller is implemented as an artificial neural network and trained using the error back-propagation algorithm. The experiment, which demonstrates the proof of the concept, is also denoted in this paper.

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“…Mixed environment where users take services from both worlds is also possible. First family is partially related to M2C/M2M concept mentioned in earlier works [8,9], more precise we can depict these architectures as C->R. Second family is partially related to M2M/H2M models, and we can depict those architectures as R->U, where U (user) can be other robot (R) or H (human).…”

Section: Taxonomy For Cloud Roboticsmentioning

confidence: 94%

“…Authors give some classification of robotic architectures based on robot-to-c1oud computing models: peer-based model (robots and VM are equally treated as computing units), proxy-based model (one robot treated by other networked robots as a proxy system for communication with a computing cloud) and clone based model (every robot has corresponding clone on the cloud). Next good approach to classification and systematization of cloud robotics was made in [9]. In the paper authors evaluate cloud robotics defmitions and introduce classification of cloud robotics architectures -they separate them in three groups: Cloud Networks (classic network robotic architectures), which they divide in three subgroups -Teleoperated robots (telepresence systems), Multi-robot systems and Sensors arr ays; Cloud Computing (here they mainly refer to mentioned above [8]); Cloud Storage (robotic systems use remote storage for knowledge sharing and acquirement).…”

Section: Introductionmentioning

confidence: 99%

Cloud computing architectures for mobile robotics

Dyumin

Puzikov

Rovnyagin

et al. 2015

2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

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in last decade, classic IT-infrastructures in modern enterprises have been changed sufficiently by cloud computing. However, cloud approach can be used effectively in other applications. In this paper, we explore applicability of cloud paradigm in mobile robotics. First approach is using some classic cloud architectures, such as Infrastructure-as-a-Service, Platform-as-a-Service and Software-as-a-Service, with heterogeneous distributed mobile robotic system -for example, it can be used to offload a time consuming computations from mobile platforms to remote nodes or services. Second approach is using some special "robotic" types of clouds, such as Robot-as-aService and Function-as-a-Service to leverage abilities of a single robotics platform in mobile robotic system as services to robotic system's users or other mobile robotic platforms. We explore both approaches and give them some architectural examples.

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The rising prospects of cloud robotic applications

Cited by 31 publications

References 12 publications

Internet of Robotic Things Intelligent Connectivity and Platforms

Internet of Robotic Things Intelligent Connectivity and Platforms

Forward control of robotic arm using the information from stereo-vision tracking system

Cloud computing architectures for mobile robotics

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