Relevant bibliographies by topics / Cloud Robotics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cloud Robotics'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "Cloud Robotics"

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Shakya, Dr Subarna. "Survey on Cloud Based Robotics Architecture, Challenges and Applications." Journal of Ubiquitous Computing and Communication Technologies 2, no. 1 (March 11, 2020): 10–18. http://dx.doi.org/10.36548/jucct.2020.1.002.

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The emergence of the cloud computing, and the other advanced technologies has made possible the extension of the computing and the data distribution competencies of the robotics that are networked by developing an cloud based robotic architecture by utilizing both the centralized and decentralized cloud that is manages the machine to cloud and the machine to machine communication respectively. The incorporation of the robotic system with the cloud makes probable the designing of the cost effective robotic architecture that enjoys the enhanced efficiency and a heightened real- time performance. This cloud based robotics designed by amalgamation of robotics and the cloud technologies empowers the web enabled robots to access the services of cloud on the fly. The paper is a survey about the cloud based robotic architecture, explaining the forces that necessitate the robotics merged with the cloud, its application and the major concerns and the challenges endured in the robotics that is integrated with the cloud. The paper scopes to provide a detailed study on the changes influenced by the cloud computing over the industrial robots.
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Valko, Nataliia V., Nataliya O. Kushnir, and Viacheslav V. Osadchyi. "Cloud technologies for STEM education." CTE Workshop Proceedings 7 (March 20, 2020): 435–47. http://dx.doi.org/10.55056/cte.384.

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Cloud technologies being used in STEM education for providing robotics studying are highlighted in this article. Developing cloud robotic systems have not been used to their fullest degree in education but are applied by limited specialists’ number. Advantages given by cloud robotics (an access to big data, open systems, open environments development) lead to work with mentioned systems interfaces improving and having them more accessible. The potential represented by these technologies make them worth being shown to the majority of teachers. Benefits of cloud technologies for robotics and automatization systems are defined. An integrated approach to knowledge assimilation is STEM education basis. The demanded stages for robotics system development are shown and cloud sources which could be possibly used are analyzed in this article.
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Kamei, Koji, Shuichi Nishio, Norihiro Hagita, and Miki Sato. "Cloud networked robotics." IEEE Network 26, no. 3 (May 2012): 28–34. http://dx.doi.org/10.1109/mnet.2012.6201213.

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Mester, Gyula. "Cloud Robotics Model." Interdisciplinary Description of Complex Systems 13, no. 1 (2015): 1–8. http://dx.doi.org/10.7906/indecs.13.1.1.

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Koken, Busra. "Cloud Robotics Platforms." Interdisciplinary Description of Complex Systems 13, no. 1 (2015): 26–33. http://dx.doi.org/10.7906/indecs.13.1.4.

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Bogue, Robert. "Cloud robotics: a review of technologies, developments and applications." Industrial Robot: An International Journal 44, no. 1 (January 16, 2017): 1–5. http://dx.doi.org/10.1108/ir-10-2016-0265.

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Purpose This paper aims to provide an insight into the current state of cloud robotics developments, technology and applications. Design/methodology/approach Following a short introduction, this paper first considers the potential benefits of cloud robotics. It discusses cloud service providers and then considers a range of recent applications and developments involving humanoid, mobile and industrial robots. This is followed by details of some recent market entrants and their developments. Finally, brief concluding comments are drawn. Findings Cloud robotics is a rapidly developing technology made possible by the current ubiquitous internet connectivity and the growing number of powerful cloud computing services available. Benefits include access to big data sets, open-source algorithms, code and programmes, massively powerful parallel or grid computing and the sharing of information between robots. The technology has been applied successfully to humanoid, industrial, mobile and other classes of robots, often through direct collaborations between robot manufacturers and major IT companies. Several new companies have been established in very recent years to exploit the capabilities of cloud robotic technologies. Originality/value Cloud robotics is a highly topical and rapidly developing field, and this paper provides a detailed insight into recent developments and applications.
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Liu, Yanli, Heng Zhang, and Chao Huang. "A Novel RGB-D SLAM Algorithm Based on Cloud Robotics." Sensors 19, no. 23 (December 1, 2019): 5288. http://dx.doi.org/10.3390/s19235288.

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In this paper, we present a novel red-green-blue-depth simultaneous localization and mapping (RGB-D SLAM) algorithm based on cloud robotics, which combines RGB-D SLAM with the cloud robot and offloads the back-end process of the RGB-D SLAM algorithm to the cloud. This paper analyzes the front and back parts of the original RGB-D SLAM algorithm and improves the algorithm from three aspects: feature extraction, point cloud registration, and pose optimization. Experiments show the superiority of the improved algorithm. In addition, taking advantage of the cloud robotics, the RGB-D SLAM algorithm is combined with the cloud robot and the back-end part of the computationally intensive algorithm is offloaded to the cloud. Experimental validation is provided, which compares the cloud robotic-based RGB-D SLAM algorithm with the local RGB-D SLAM algorithm. The results of the experiments demonstrate the superiority of our framework. The combination of cloud robotics and RGB-D SLAM can not only improve the efficiency of SLAM but also reduce the robot’s price and size.
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Ahn, Hyunsik. "A Function as a Service Based Fog Robotic System for Cognitive Robots." Applied Sciences 9, no. 21 (October 27, 2019): 4555. http://dx.doi.org/10.3390/app9214555.

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Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a function as a service based fog robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency, and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.
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Saha, Olimpiya, and Prithviraj Dasgupta. "A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications." Robotics 7, no. 3 (August 30, 2018): 47. http://dx.doi.org/10.3390/robotics7030047.

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Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.
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Kamleshwar, Sahil. "Robotics and Automation." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 30, 2021): 2852–56. http://dx.doi.org/10.22214/ijraset.2021.35723.

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Cloud infrastructure and its extensive set of Internet-enabled resources have the potential to provide significant benefits to robots and flexible systems. We look for robots and data-switching programs or code from the network to support their performance, that is, when not all sense, calculation, and memory are integrated into the standalone system. This survey is designed for four possible Cloud benefits: 1) Big Data: access to photo libraries, maps, trajectories, and descriptive data; 2) Cloud Computing: access to the same grid computer with the demand for mathematical analysis, reading, and movement planning; 3) Integrated Robots Learning: robots that share tracking, control policies, and results; and 4) Census: use of crowdourcing to tap people's skills for image and video analysis, classification, reading, and error retrieval. The cloud can also improve robots and flexible systems by providing access to: a) data sets, publications, models, measurements, and simulation tools; b) open competitions for designs and programs; and c) open source software.
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Dissertations / Theses on the topic "Cloud Robotics"

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Forsman, Mona. "Point cloud densification." Thesis, Umeå universitet, Institutionen för fysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39980.

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Several automatic methods exist for creating 3D point clouds extracted from 2D photos. In manycases, the result is a sparse point cloud, unevenly distributed over the scene.After determining the coordinates of the same point in two images of an object, the 3D positionof that point can be calculated using knowledge of camera data and relative orientation. A model created from a unevenly distributed point clouds may loss detail and precision in thesparse areas. The aim of this thesis is to study methods for densification of point clouds. This thesis contains a literature study over different methods for extracting matched point pairs,and an implementation of Least Square Template Matching (LSTM) with a set of improvementtechniques. The implementation is evaluated on a set of different scenes of various difficulty. LSTM is implemented by working on a dense grid of points in an image and Wallis filtering isused to enhance contrast. The matched point correspondences are evaluated with parameters fromthe optimization in order to keep good matches and discard bad ones. The purpose is to find detailsclose to a plane in the images, or on plane-like surfaces. A set of extensions to LSTM is implemented in the aim of improving the quality of the matchedpoints. The seed points are improved by Transformed Normalized Cross Correlation (TNCC) andMultiple Seed Points (MSP) for the same template, and then tested to see if they converge to thesame result. Wallis filtering is used to increase the contrast in the image. The quality of the extractedpoints are evaluated with respect to correlation with other optimization parameters and comparisonof standard deviation in x- and y- direction. If a point is rejected, the option to try again with a largertemplate size exists, called Adaptive Template Size (ATS).
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Bruse, Andreas. "Exploiting Cloud Resources For Semantic Scene Understanding On Mobile Robots." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169116.

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Modern day mobile robots are constrained in the resources available to them. Only so much hardware can be fit onto the robotic frame and at the same time they are required to perform tasks that require lots of computational resources, access to massive amounts of data and the ability to share knowledge with other robots around it. This thesis explores the cloud robotics approach in which complex compu- tations can be offloaded to a cloud service which can have a huge amount of computational resources and access to massive data sets. The Robot Operat- ing System, ROS, is extended to allow the robot to communicate with a high powered cluster and this system is used to test our approach on such a complex task as semantic scene understanding. The benefits of the cloud approach is utilized to connect to a cloud based object detection system and to build a cat- egorization system relying on large scale datasets and a parallel computation model. Finally a method is proposed for building a consistent scene description by exploiting semantic relationships between objects.
Moderna mobila robotar har begränsade resurser. Det får inte plats hur mycket hårdvara som helst på roboten och ändå förväntas de utföra arbeten som kräver extremt mycket datorkraft, tillgång till enorm mängd data och samtidigt kommunicera med andra robotar runt omkring sig. Det här examensarbetet utforskar robotik i molnet där komplexa beräk- ningar kan läggas ut i en molntjänst som kan ha tillgång till denna stora mängd datakraft och ha plats för de stora datamängder som behövs. The Ro- bot Operating System, eller ROS, byggs ut för att stödja kommunikation med en molntjänst och det här systemet används sedan för att testa vår lösning på ett så komplext problem som att förstå en omgivning eller miljö på ett seman- tiskt plan. Fördelarna med att använda en molnbaserad lösning används genom att koppla upp sig mot ett objektigenkänningssytem i molnet och för att byg- ga ett objektkategoriseringssystem som förlitar sig på storskaliga datamängder och parallella beräkningsmodeller. Slutligen föreslås en metod för att bygga en tillförlitlig miljöbeskrivning genom att utnyttja semantiska relationer mellan föremål.
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Liu, Yuwei. "OpenMP based Action Entropy Active Sensing in Cloud Computing." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1584809369789769.

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Bhal, Siddharth. "Fog computing for robotics system with adaptive task allocation." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78723.

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The evolution of cloud computing has finally started to affect robotics. Indeed, there have been several real-time cloud applications making their way into robotics as of late. Inherent benefits of cloud robotics include providing virtually infinite computational power and enabling collaboration of a multitude of connected devices. However, its drawbacks include higher latency and overall higher energy consumption. Moreover, local devices in proximity incur higher latency when communicating among themselves via the cloud. At the same time, the cloud is a single point of failure in the network. Fog Computing is an extension of the cloud computing paradigm providing data, compute, storage and application services to end-users on a so-called edge layer. Distinguishing characteristics are its support for mobility and dense geographical distribution. We propose to study the implications of applying fog computing concepts in robotics by developing a middle-ware solution for Robotic Fog Computing Cluster solution for enabling adaptive distributed computation in heterogeneous multi-robot systems interacting with the Internet of Things (IoT). The developed middle-ware has a modular plug-in architecture based on micro-services and facilitates communication of IOT devices with the multi-robot systems. In addition, the developed middle-ware solutions support different load balancing or task allocation algorithms. In particular, we establish that we can enhance the performance of distributed system by decreasing overall system latency by using already established multi-criteria decision-making algorithms like TOPSIS and TODIM with naive Q-learning and with Neural Network based Q-learning.
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Nagrath, Vineet. "Software architectures for cloud robotics : the 5 view Hyperactive Transaction Meta-Model (HTM5)." Thesis, Dijon, 2015. http://www.theses.fr/2015DIJOS005/document.

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Le développement de logiciels pour les robots connectés est une difficulté majeure dans le domaine du génie logiciel. Les systèmes proposés sont souvent issus de la fusion de une ou plusieurs plates-formes provenant des robots, des ordinateurs autonomes, des appareils mobiles, des machines virtuelles, des caméras et des réseaux. Nous proposons ici une approche orientée agent permettant de représenter les robots et tous les systèmes auxiliaires comme des agents d’un système. Ce concept de l’agence préserve l’autonomie sur chacun des agents, ce qui est essentiel dans la mise en oeuvre logique d’un nuage d’éléments connectés. Afin de procurer une flexibilité de mise en oeuvre des échanges entre les différentes entités, nous avons mis en place un mécanisme d’hyperactivité ce qui permet de libérer sélectivement une certaine autonomie d’un agent par rapport à ces associés.Actuellement, il n’existe pas de solution orientée méta-modèle pour décrire les ensembles de robots interconnectés. Dans cette thèse, nous présentons un méta-modèle appelé HTM5 pour spécifier a structure, les relations, les échanges, le comportement du système et l’hyperactivité dans un système de nuages de robots. La thèse décrit l’anatomie du méta-modèle (HTM5) en spécifiant les différentes couches indépendantes et en intégrant une plate-forme indépendante de toute plateforme spécifique. Par ailleurs, la thèse décrit également un langage de domaine spécifique pour la modélisation indépendante dans HTM5. Des études de cas concernant la conception et la mise en oeuvre d’un système multi-robots basés sur le modèle développé sont également présentés dans la thèse. Ces études présentent des applications où les décisions commerciales dynamiques sont modélisées à l’aide du modèle HTM5 confirmant ainsi la faisabilité du méta-modèle proposé
Software development for cloud connected robotic systems is a complex software engineeringendeavour. These systems are often an amalgamation of one or more robotic platforms, standalonecomputers, mobile devices, server banks, virtual machines, cameras, network elements and ambientintelligence. An agent oriented approach represents robots and other auxiliary systems as agents inthe system.Software development for distributed and diverse systems like cloud robotic systems require specialsoftware modelling processes and tools. Model driven software development for such complexsystems will increase flexibility, reusability, cost effectiveness and overall quality of the end product.The proposed 5-view meta-model has separate meta-models for specifying structure, relationships,trade, system behaviour and hyperactivity in a cloud robotic system. The thesis describes theanatomy of the 5-view Hyperactive Transaction Meta-Model (HTM5) in computation independent,platform independent and platform specific layers. The thesis also describes a domain specificlanguage for computation independent modelling in HTM5.The thesis has presented a complete meta-model for agent oriented cloud robotic systems and hasseveral simulated and real experiment-projects justifying HTM5 as a feasible meta-model
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Toris, Russell C. "Spatial and Temporal Learning in Robotic Pick-and-Place Domains via Demonstrations and Observations." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/135.

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Traditional methods for Learning from Demonstration require users to train the robot through the entire process, or to provide feedback throughout a given task. These previous methods have proved to be successful in a selection of robotic domains; however, many are limited by the ability of the user to effectively demonstrate the task. In many cases, noisy demonstrations or a failure to understand the underlying model prevent these methods from working with a wider range of non-expert users. My insight is that in many mobile pick-and-place domains, teaching is done at a too fine grained level. In many such tasks, users are solely concerned with the end goal. This implies that the complexity and time associated with training and teaching robots through the entirety of the task is unnecessary. The robotic agent needs to know (1) a probable search location to retrieve the task's objects and (2) how to arrange the items to complete the task. This thesis work develops new techniques for obtaining such data from high-level spatial and temporal observations and demonstrations which can later be applied in new, unseen environments. This thesis makes the following contributions: (1) This work is built on a crowd robotics platform and, as such, we contribute the development of efficient data streaming techniques to further these capabilities. By doing so, users can more easily interact with robots on a number of platforms. (2) The presentation of new algorithms that can learn pick-and-place tasks from a large corpus of goal templates. My work contributes algorithms that produce a metric which ranks the appropriate frame of reference for each item based solely on spatial demonstrations. (3) An algorithm which can enhance the above templates with ordering constraints using coarse and noisy temporal information. Such a method eliminates the need for a user to explicitly specify such constraints and searches for an optimal ordering and placement of items. (4) A novel algorithm which is able to learn probable search locations of objects based solely on sparsely made temporal observations. For this, we introduce persistence models of objects customized to a user's environment.
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Trowbridge, Michael Aaron. "Autonomous 3D Model Generation of Orbital Debris using Point Cloud Sensors." Thesis, University of Colorado at Boulder, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1558774.

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A software prototype for autonomous 3D scanning of uncooperatively rotating orbital debris using a point cloud sensor is designed and tested. The software successfully generated 3D models under conditions that simulate some on-orbit orbit challenges including relative motion between observer and target, inconsistent target visibility and a target with more than one plane of symmetry. The model scanning software performed well against an irregular object with one plane of symmetry but was weak against objects with 2 planes of symmetry.

The suitability of point cloud sensors and algorithms for space is examined. Terrestrial Graph SLAM is adapted for an uncooperatively rotating orbital debris scanning scenario. A joint EKF attitude estimate and shape similiarity loop closure heuristic for orbital debris is derived and experimentally tested. The binary Extended Fast Point Feature Histogram (EFPFH) is defined and analyzed as a binary quantization of the floating point EFPFH. Both the binary and floating point EPFH are experimentally tested and compared as part of the joint loop closure heuristic.

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He, Linbo. "Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data : Improving 3D Point Cloud Segmentation Using Multimodal Fusion of Projected 2D Imagery Data." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157705.

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Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to analyze 2D images, videos, and even point clouds that contain 3D data points. On the first two problems, CNNs have achieved remarkable progress, but on point cloud segmentation, the results are less satisfactory due to challenges such as limited memory resource and difficulties in 3D point annotation. One of the research studies carried out by the Computer Vision Lab at Linköping University was aiming to ease the semantic segmentation of 3D point cloud. The idea is that by first projecting 3D data points to 2D space and then focusing only on the analysis of 2D images, we can reduce the overall workload for the segmentation process as well as exploit the existing well-developed 2D semantic segmentation techniques. In order to improve the performance of CNNs for 2D semantic segmentation, the study has used input data derived from different modalities. However, how different modalities can be optimally fused is still an open question. Based on the above-mentioned study, this thesis aims to improve the multistream framework architecture. More concretely, we investigate how different singlestream architectures impact the multistream framework with a given fusion method, and how different fusion methods contribute to the overall performance of a given multistream framework. As a result, our proposed fusion architecture outperformed all the investigated traditional fusion methods. Along with the best singlestream candidate and few additional training techniques, our final proposed multistream framework obtained a relative gain of 7.3\% mIoU compared to the baseline on the semantic3D point cloud test set, increasing the ranking from 12th to 5th position on the benchmark leaderboard.
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Wang, Chen. "Connectivity, Security and Integrationfor Cloud Manufacturing." Thesis, KTH, Industriell produktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-226522.

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Det här mastersprojektet syftar till att ansluta industriroboten till moln plattformen och utvärdera anslutning och säkerhet. För att uppnå bättre anslutning, säkerhet och integration, föreslås en modifierad Moln Tillverkningssystem- (CRS) arkitektur, som kännetecknas av hög modularitet, standardisering och komposibilitet. Arkitekturens specifika applikationer iprivata, offentliga och hybridmoln diskuteras också. Sedan är en  systemarkitektur med detaljerad mjukvarukomposition designad för Molnrobotik. Enligt den föreslagna systemarkitekturen presenteras möjliga säkerhetshotskällor och motsvarande lösningar.Under projektet används Universell Robot 5 (UR5) som en praktisk robotinstans för att utveckla en kommunikationsrutin mellan KTH Moln och robotar. Ett applikationsprogramgränssnitt (API) skrivet i Python for Universell Robot och servern är etablerad. API: n består av två modulära delar, Gateway Agenten och Applikationsmjukvaran.Gateway Agenten realiserar kopplingen mellan Universell Robot 5 (UR5) och molnet, medan applikationsmjukvaran kan anpassas till specifika tillämpningar och krav. I detta projekt utvecklas tre huvudfunktioner i applikationsmjukvaran, inklusive datainsamling, datavisualisering och fjärrkontroll. Förutom att utvärdera anslutning och stabilitet simulerasdet privata robotik molnsystemet och det offentliga robotik molnsystemet med KTH Moln.Hybrid robotik moln systemet diskuteras också. Genom resultaten av fallstudier verifieras anslutningen och integrationen av Moln Tillverkningssystem.
This master thesis project aims to connect the industrial robot to the Cloud platform, and evaluate the connectivity and security. To realize better connectivity, security and integration, a modified Cloud Manufacturing System (CRS) architecture is proposed, which is characterized by high modularity, standardization and composability. The architecture’s specific applications in private, public and hybrid cloud are discussed as well. Then, one system architecture with detailed software composition is designed for Cloud Robotics.According to the proposed system architecture, possible security threat sources and corresponding solutions are presented.During the project, Universal Robot 5 (UR5) is utilized as a practical robot instance to develop a communication routine between KTH Cloud and robots. An Application Program Interface (API) written by Python for Universal Robots and the server is established. The API consists of two modularized part, Gateway Agent and Application Package. The Gateway Agent realizes the connection between the Universal Robot 5 (UR5) and the cloud, while theApplication Package can be customized according to specific application and requirements. In this project, three main functions are developed in the Application Package, including data acquisition, data visualization and remote control. Besides, to evaluate connectivity and stability, private robotics cloud system and public robotics cloud system are simulated with KTH Cloud. The hybrid robotics cloud system is discussed as well. Through the results of case studies, the connectivity and integration of Cloud Manufacturing System are verified.
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Chleborad, Aaron A. "Grasping unknown novel objects from single view using octant analysis." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4089.

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Books on the topic "Cloud Robotics"

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Mello, Ricardo C., Moises R. N. Ribeiro, and Anselmo Frizera-Neto. Implementing Cloud Robotics for Practical Applications. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16908-3.

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Uden, Lorna. Workshop on Learning Technology for Education in Cloud (LTEC'12). Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Uden, Lorna. 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Koubaa, Anis, and Elhadi Shakshuki, eds. Robots and Sensor Clouds. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22168-7.

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Stakem, Patrick. Mobile Cloud Robotics. Independently Published, 2018.

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Pomerleau, Francois, Francis Colas, and Roland Siegwart. Review of Point Cloud Registration Algorithms for Mobile Robotics. Now Publishers, 2015.

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Mello, Ricardo C., Moises R. N. Ribeiro, and Anselmo Frizera-Neto. Implementing Cloud Robotics for Practical Applications: From Human-Robot Interaction to Autonomous Navigation. Springer International Publishing AG, 2022.

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Koubaa, Anis, and Elhadi Shakshuki. Robots and Sensor Clouds. Springer, 2016.

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Robots and Sensor Clouds. Springer, 2015.

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Koubaa, Anis, and Elhadi Shakshuki. Robots and Sensor Clouds. Springer London, Limited, 2015.

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Book chapters on the topic "Cloud Robotics"

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Lachure, Jaykumar, and Rajesh Doriya. "Fog Robotics." In Cloud Security, 223–46. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9780367821555-16.

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Vojić, Samir. "Cloud Robotics." In Lecture Notes in Networks and Systems, 191–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90893-9_23.

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Toffetti, Giovanni, and Thomas Michael Bohnert. "Cloud Robotics with ROS." In Studies in Computational Intelligence, 119–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20190-6_5.

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Mello, Ricardo C., Moises R. N. Ribeiro, and Anselmo Frizera-Neto. "Introduction to Cloud Robotics." In Springer Tracts in Advanced Robotics, 1–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16908-3_1.

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Lei, Chen. "Device–Cloud Collaboration." In Cognitive Intelligence and Robotics, 283–97. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2233-5_12.

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Zubrycki, Igor, and Grzegorz Granosik. "Teaching Robotics with Cloud Tools." In Robotics in Education, 301–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62875-2_27.

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Mello, Ricardo C., Moises R. N. Ribeiro, and Anselmo Frizera-Neto. "Cloud-Robot Communication." In Springer Tracts in Advanced Robotics, 51–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16908-3_4.

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Tang, Shenglong, Jiafu Wan, Hu Cai, and Fulong Chen. "Cloud Robotics: Insight and Outlook." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 94–103. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44350-8_10.

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Siruvoru, Vahini, and Nampally Vijay Kumar. "Cloud Robotics in Agriculture Automation." In New Trends in Computational Vision and Bio-inspired Computing, 1073–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41862-5_109.

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AlMazrua, Halah, Alia Alshehri, Mai Asiri, Alanod Almasaud, and Lamya Albraheem. "Cloud Robotics Knowledge Sharing Survey." In Proceedings of Seventh International Congress on Information and Communication Technology, 535–49. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2397-5_49.

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Conference papers on the topic "Cloud Robotics"

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Quintas, João M., Paulo J. Menezes, and Jorge M. Dias. "Cloud Robotics: Toward Context Aware Robotic Networks." In Biomechanics / Robotics. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.752-062.

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Quintas, João M., Paulo J. Menezes, and Jorge M. Dias. "Cloud Robotics: Toward Context Aware Robotic Networks." In Biomechanics / Robotics. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.752-062.

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Liu, Jiayi, Wenjun Xu, Jiaqiang Zhang, Zude Zhou, and Duc Truong Pham. "Industrial Cloud Robotics Towards Sustainable Manufacturing." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8733.

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Cloud Robotics (CR) is the combination of Cloud Computing and Robotics, which encapsulate resources related with robots as services and is also the robotics’ next stage of development. Under this background, due to the characteristics of convenient access, resource sharing and lower costs, industrial cloud robotics (ICR) is proposed to integrate the industrial robots resources in the worldwide to provide ICR services in worldwide. ICR also plays an important role in improving the productivity of manufacturing. In the manufacturing field, Cloud Manufacturing (CM) and Sustainable Manufacturing (SM) is the developing orientation of future manufacturing industry. The energy consumption optimization of ICR is the crucial issue for manufacturing sustainability. However, currently, ICR systems are not programmed efficiently, which leads to the increase of production costs and pollutant emissions. Thus, it is an actual problem to optimize the energy consumption of ICR. In this paper, in order to achieve the goal of energy consumption optimization in worldwide range, the framework of ICR towards sustainable manufacturing is presented, as well as its enabling methodologies, and it is used to support energy consumption optimization services of ICR in the Cloud environment. This framework can be used to support energy-efficient services related with ICR to realize sustainable manufacturing in the worldwide range.
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Ramharuk, Vikash, and Isaac Osunmakinde. "Cloud Robotics." In the Southern African Institute for Computer Scientist and Information Technologists Annual Conference 2014. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2664591.2664602.

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Toffetti, Giovanni, Tobias Lötscher, Saken Kenzhegulov, Josef Spillner, and Thomas Michael Bohnert. "Cloud Robotics." In UCC '17: 10th International Conference on Utility and Cloud Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3147234.3148100.

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Li, Lan, Wenjun Xu, Zhihao Liu, Bitao Yao, Zude Zhou, and Duc Truong Pham. "Digital Twin-Based Control Approach for Industrial Cloud Robotics." In ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-2920.

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Abstract Industrial robots can be mechanical intelligent agents by integrating programs, intelligent algorithms and facilitating intelligent manufacturing models from cyber world into physical entities. After introducing the concept of cloud, their storage, computing, knowledge sharing and evolution capabilities are further strengthened. Digital twin is an effective means to achieve the fusion of physics and information. Therefore, it is feasible to introduce the digital twin to the industrial cloud robotics (ICR), in order to facilitate the control optimization of robots’ running state. The traditional manufacturing task-oriented service composition is limited to execution in the cloud, and it is separated from the underlying robot equipment control, which greatly reduces the real-time performance and accuracy of the underlying service response, such as Robotic Control as a Cloud Service (RCaaCS). Therefore, this paper proposes a digital twin-based control approach for ICR. At the manufacturing cell level, robots’ control instruction service modeling is conducted, and then the control service in the digital world is mapped to the robot action control in the physical world through the concept of digital twin. The accumulated operational data in the physical world can be fed back to the digital world as a reference for simulation and control strategy adjustment, finally achieving the integration of cloud services and robot control. A case study based on workpiece disassembly is presented to verify the availability and effectiveness of the proposed control approach.
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Ren, Fuji. "Robotics cloud and robotics school." In 2011 7th International Conference on Natural Language Processing and Knowledge Engineering (NLPKE). IEEE, 2011. http://dx.doi.org/10.1109/nlpke.2011.6137767.

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Jin, Lixue, Wenjun Xu, Zhihao Liu, Junwei Yan, Zude Zhou, and Duc Truong Pham. "Knowledge Sharing and Evolution of Industrial Cloud Robotics." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6538.

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Industrial Cloud Robotics (ICR), with the characteristics of resource sharing, lower cost and convenient access, etc., can realize the knowledge interaction and coordination among cloud Robotics (CR) through the knowledge sharing mechanism. However, the current researches mainly focus on the knowledge sharing of service-oriented robots and the knowledge updating of a single robot. The interaction and collaboration among robots in a cloud environment still have challenges, such as the improper updating of knowledge, the inconvenience of online data processing and the inflexibility of sharing mechanism. In addition, the industrial robot (IR) also lacks a well-developed knowledge management framework in order to facilitate the knowledge evolution of industrial robots. In this paper, a knowledge evolution mechanism of ICR based on the approach of knowledge acquisition - interactive sharing - iterative updating is established, and a novel architecture of ICR knowledge sharing is also developed. Moreover, the semantic knowledge in the robot system can encapsulate knowledge of manufacturing tasks, robot model and scheme decision into the cloud manufacturing process. As new manufacturing tasks arrived, the robot platform downloads task-oriented knowledge models from the cloud service platform, and then selects the optimal service composition and updates the cloud knowledge by simulation iterations. Finally, the feasibility and effectiveness of the proposed architecture and approaches are demonstrated through the case studies.
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Stumm, Sven, Peter Neu, and Sigrid Brell-Cokcan. "Towards Cloud Informed Robotics." In 34th International Symposium on Automation and Robotics in Construction. Tribun EU, s.r.o., Brno, 2017. http://dx.doi.org/10.22260/isarc2017/0008.

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Ma, Yanping, Wenjun Xu, Sisi Tian, Jiayi Liu, Bitao Yao, Yang Hu, and Hao Feng. "Knowledge Graph-Based Manufacturing Capability Service Optimal Selection for Industrial Cloud Robotics." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8351.

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Abstract As an important part of Cloud Manufacturing (CMfg), Industrial Cloud Robotics (ICRs) encapsulates manufacturing capability of physical industrial robots as services for the users. However, a growing number of functionally equivalent services appear in CMfg platform due to the wide use of industrial robots in manufacturing field. It is important to carry out Manufacturing Capability Service (MCS) optimal selection for ICRs from various optional services under CMfg environment. But current service optimal selection method emphasizes on the non-function information of services, and it ignores the interactive relationships between different services and the basic function information of services, which make it difficult to satisfy the various personalized demands of users. Service optimal selection requires the integration and sharing of manufacturing knowledge. Knowledge graph provides an effective way to express and manage knowledge. And it can provide decision support for users to select appropriate ICRs service. Therefore, this paper proposes a method of knowledge graph-based manufacturing capability service optimal selection for ICRs. The function information, association information and non-function information of MCS are described based on knowledge graph. Based on this, the service optimal selection procedure is proposed to realize smart MCS optimal selection for ICRs, which includes feature selection, association selection and user custom weights of non-function indices selection. Finally, a case study based on robotic assembly is presented to demonstrate the effectiveness of proposed method.
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Reports on the topic "Cloud Robotics"

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Valko, Nataliia V., Nataliya O. Kushnir, and Viacheslav V. Osadchyi. Cloud technologies for STEM education. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3882.

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Cloud technologies being used in STEM education for providing robotics studying are highlighted in this article. Developing cloud robotic systems have not been used to their fullest degree in education but are applied by limited specialists’ number. Advantages given by cloud robotics (an access to big data, open systems, open environments development) lead to work with mentioned systems interfaces improving and having them more accessible. The potential represented by these technologies make them worth being shown to the majority of teachers. Benefits of cloud technologies for robotics and automatization systems are defined. An integrated approach to knowledge assimilation is STEM education basis. The demanded stages for robotics system development are shown and cloud sources which could be possibly used are analyzed in this article.
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Valko, Nataliia V., Viacheslav V. Osadchyi, and Vladyslav S. Kruhlyk. Cloud resources use for students' project activities. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4444.

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The modern educational system proclaims learning aimed at acquiring practical skills and based on the activity approach. Educational research projects are the necessary component of curricula in physics, computer science, biology and chemistry. There is a problem of specialized equipment and facilities using for the implementation of such projects in distance learning. Therefore, the issue of cloud resources using for distance learning organization in robotics is relevant. The article presents a brief overview of the current state of projects development in Ukrainian schools and approaches used in foreign educational institutions in teaching robotics distantly. The article describes the stages of robotics projects development such as organizational, communicative, project work, summarizing. The peculiarities of the stages in distance learning and the possibilities of cloud technologies in robotics are also considered. The authors’ experience in projects developing in this environment for students and future teachers is described.
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Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, July 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.
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Chen, Maggie, and Christian Volpe Martincus. Digital Technologies and Globalization: A Survey of Research and Policy Applications. Inter-American Development Bank, March 2022. http://dx.doi.org/10.18235/0004117.

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In recent years, the world has witnessed the rise of multiple specific digital technologies, including online trade platforms, robotics, artificial intelligence (AI), 3D printing, cloud computing, blockchain, and financial technology (fintech). These digital technologies are fundamentally transforming the ways that firms and individualsas both workers and consumerscommunicate, search, trade, and invest. They are also substantially changing how governments design and implement trade and investment policies and programs and, in so doing, how they interact with firms, individuals, and each other. This paper reviews the growing empirical literature on the trade, investment, and broader development effects of the adoption of specific digital technologies. It also describes the policy applications of these technologies and discusses the incipient empirical literature on the impacts thereof. Based on this review, it identifies several open questions and avenues of future research that may be useful for deepening our understanding of digital technologies and their policy implications.
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Strutynska, Oksana V., Grygoriy M. Torbin, Mariia A. Umryk, and Roman M. Vernydub. Digitalization of the educational process for the training of the pre-service teachers. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4437.

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According to the Development Concept of the Digital Economy and Society in Ukraine, the priority of this area is to develop a substantial national policy on digitalization of education, as this is the key part of the education reform in Ukraine. For this reason, universities should firstly take into account the particularities of teaching the current generation of students and the needs of the digital society as a whole. This paper considers the process of transition from informatization to digitalization in society, implementation of digital support for the educational process in the university, development of the digital educational environment for the training university teachers, and proposes the digital tools for such an environment. The authors propose several ways to improve the development level of digitalization of the educational environment in the university. This is to take into account the needs of the digital society and the modern generation of students, provide a high level of the digital literacy formation of university graduates and support the development of a new digital security system of the modern university. Aiming to design the digital educational environment for increasing the of educators’ digital literacy level, the authors propose to develop and implement the following computer, multimedia and computer-based learning tools and equipment, which includes blended and distance learning classes, cloud technologies, tools of virtual and augmented reality, tools for gamification of the educational process, educational robotics, tools for learning 3D technologies, MOOCs.
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