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Journal articles on the topic 'Cloud Robotics'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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1

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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4

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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5

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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6

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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10

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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Fosch-Villaronga, Eduard, and Christopher Millard. "Cloud robotics law and regulation." Robotics and Autonomous Systems 119 (September 2019): 77–91. http://dx.doi.org/10.1016/j.robot.2019.06.003.

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Mohanarajah, Gajamohan, Dominique Hunziker, Raffaello D'Andrea, and Markus Waibel. "Rapyuta: A Cloud Robotics Platform." IEEE Transactions on Automation Science and Engineering 12, no. 2 (April 2015): 481–93. http://dx.doi.org/10.1109/tase.2014.2329556.

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Botta, Alessio, Jonathan Cacace, Riccardo De Vivo, Bruno Siciliano, and Giorgio Ventre. "Networking for Cloud Robotics: The DewROS Platform and Its Application." Journal of Sensor and Actuator Networks 10, no. 2 (June 14, 2021): 34. http://dx.doi.org/10.3390/jsan10020034.

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With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications.

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Ranganathan, C. "Indian Scientist Research Productivity in Cloud Robotics: A Scientometric Analysis." Indian Journal of Information Sources and Services 8, no. 2 (August 5, 2018): 85–90. http://dx.doi.org/10.51983/ijiss.2018.8.2.515.

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This study analyses the Indian contributions of research papers related to Cloud Robotics was undertaken from Web of Science Databases has been used to retrieve the data for 15 years (1999-2013) by the searching the keyword “Cloud Robotics”. Most of the researchers preferred to publish their research results in 586 journal articles. The authorship trend shows that, out of total 629 research literatures published, 97% of them or published under the joint author of publications in Cloud Robotics research output. This study aims to examine the emergence of research areas, research groups and countries and the pattern of publication, authorship, institutions, growth rate of publication and journals coverage of the scientists in the field of Cloud Robotics.

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15

Pransky, Joanne. "The Pransky interview: Dr James Kuffner, CEO at Toyota Research Institute Advanced Development, Coinventor of the rapidly, exploring random tree algorithm." Industrial Robot: the international journal of robotics research and application 47, no. 1 (December 7, 2019): 7–11. http://dx.doi.org/10.1108/ir-11-2019-0226.

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Purpose The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned entrepreneur regarding his pioneering efforts of bringing technological inventions to market. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr James Kuffner, CEO at Toyota Research Institute Advanced Development (TRI-AD). Kuffner is a proven entrepreneur and inventor in robot and motion planning and cloud robotics. In this interview, Kuffner shares his personal and professional journey from conceptualization to commercial realization. Findings Dr Kuffner received BS, MS and PhD degrees from the Stanford University’s Department of Computer Science Robotics Laboratory. He was a Japan Society for the Promotion of Science (JSPS) Postdoctoral Research Fellow at the University of Tokyo where he worked on software and planning algorithms for humanoid robots. He joined the faculty at Carnegie Mellon University’s Robotics Institute in 2002 where he served until March 2018. Kuffner was a Research Scientist and Engineering Director at Google from 2009 to 2016. In January 2016, he joined TRI where he was appointed the Chief Technology Officer and Area Lead, Cloud Intelligence and is presently an Executive Advisor. He has been CEO of TRI-AD since April of 2018. Originality/value Dr Kuffner is perhaps best known as the co-inventor of the rapidly exploring random tree (RRT) algorithm, which has become a key standard benchmark for robot motion planning. He is also known for introducing the term “Cloud Robotics” in 2010 to describe how network-connected robots could take advantage of distributed computation and data stored in the cloud. Kuffner was part of the initial engineering team that built Google’s self-driving car. He was appointed Head of Google’s Robotics Division in 2014, which he co-founded with Andy Rubin to help realize the original Cloud Robotics concept. Kuffner also co-founded Motion Factory, where he was the Senior Software Engineer and a member of the engineering team to develop C++ based authoring tools for high-level graphic animation and interactive multimedia content. Motion Factory was acquired by SoftImage in 2000. In May 2007, Kuffner founded, and became the Director of Robot Autonomy where he coordinated research and software consulting for industrial and consumer robotics applications. In 2008, he assisted in the iOS development of Jibbigo, the first on-phone, real-time speech recognition, translation and speech synthesis application for the iPhone. Jibbigo was acquired by Facebook in 2013. Kuffner is one of the most highly cited authors in the field of robotics and motion planning, with over 15,000 citations. He has published over 125 technical papers and was issued more than 50 patents related to robotics and computer vision technology.

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Nataliia V. Valko, Nataliia V., Viacheslav V. Osadchyi, and Vladyslav S. Kruhlyk. "Cloud resources use for students’ project activities." CTE Workshop Proceedings 8 (March 19, 2021): 304–17. http://dx.doi.org/10.55056/cte.240.

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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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Hu, Guoqiang, Wee Tay, and Yonggang Wen. "Cloud robotics: architecture, challenges and applications." IEEE Network 26, no. 3 (May 2012): 21–28. http://dx.doi.org/10.1109/mnet.2012.6201212.

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18

Linner, Thomas, Jörg Güttler, Christos Georgoulas, and Thomas Bock. "USA² — Ubiquitous and Robot Assisted Cloud Manufacturing in an Ageing Society." Journal of Robotics and Mechatronics 27, no. 1 (February 20, 2015): 109. http://dx.doi.org/10.20965/jrm.2015.p0109.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/16.jpg"" width=""300"" />Micro home factory in future</div> In the project USA² (Ubiquitäres und Selbstständiges Arbeiten in einer alternden Gesellschaft), a robotic, mini-factory-like workspace was developed which integrates novel technologies from the field of telepresence, cooperative robotics, seamless interaction, 3D printing and cloud manufacturing. </span>

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Chen, Wuhui, Yuichi Yaguchi, Keitaro Naruse, Yutaka Watanobe, Keita Nakamura, and Jun Ogawa. "A Study of Robotic Cooperation in Cloud Robotics: Architecture and Challenges." IEEE Access 6 (2018): 36662–82. http://dx.doi.org/10.1109/access.2018.2852295.

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Kamburugamuve, Supun, Leif Christiansen, and Geoffrey Fox. "A Framework for Real Time Processing of Sensor Data in the Cloud." Journal of Sensors 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/468047.

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We describe IoTCloud, a platform to connect smart devices to cloud services for real time data processing and control. A device connected to IoTCloud can communicate with real time data analysis frameworks deployed in the cloud via messaging. The platform design is scalable in connecting devices as well as transferring and processing data. With IoTCloud, a user can develop real time data processing algorithms in an abstract framework without concern for the underlying details of how the data is distributed and transferred. For this platform, we primarily consider real time robotics applications such as autonomous robot navigation, where there are strict requirements on processing latency and demand for scalable processing. To demonstrate the effectiveness of the system, a robotic application is developed on top of the framework. The system and the robotics application characteristics are measured to show that data processing in central servers is feasible for real time sensor applications.

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Du, Zhihui, Ligang He, Yinong Chen, Yu Xiao, Peng Gao, and Tongzhou Wang. "Robot Cloud: Bridging the power of robotics and cloud computing." Future Generation Computer Systems 74 (September 2017): 337–48. http://dx.doi.org/10.1016/j.future.2016.01.002.

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Sorrentino, Alessandra, Filippo Cavallo, and Laura Fiorini. "A Plug and Play Transparent Communication Layer for Cloud Robotics Architectures." Robotics 9, no. 1 (March 22, 2020): 17. http://dx.doi.org/10.3390/robotics9010017.

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The cloud robotics paradigm aims at enhancing the abilities of robots by using cloud services, but it still poses several challenges in the research community. Most of the current literature focuses on how to enrich specific robotic capabilities, overlooking how to effectively establish communication between the two fields. Our work proposes a “plug-and-play” solution to bridge the communication gap between cloud and robotic applications. The proposed solution is designed based on the mature WebSocket technology and it can be extended to any ROS-based robotic platform. The main contributions of this work are the definition of a reliable autoconnection/autoconfiguration mechanism as well as to outline a scalable communication layer that allows the effective control of multiple robots from multiple users. The “plug-and-play” solution was evaluated in both simulated and real scenarios. In the first case, the presence of users and robots was simulated with Robot Operating System (ROS) nodes running on five machines. In the real scenario, three non-expert users teleoperated, simultaneously, three remote robots by using the proposed communication layer with different networking protocols. Results confirmed the reliability at different levels: at startup (success_rate = 100%); during high-rate communications (message_lost = 0%); in performing open-loop spiral trajectories with enhancement, with respect to similar works; and in the quality of simultaneous teleoperations.

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Ghotkar, Miss Trupti. "A Survey on Cloud Robotics and Automation." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 1952–55. http://dx.doi.org/10.22214/ijraset.2020.6319.

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Bozcuoglu, Asil Kaan, Gayane Kazhoyan, Yuki Furuta, Simon Stelter, Michael Beetz, Kei Okada, and Masayuki Inaba. "The Exchange of Knowledge Using Cloud Robotics." IEEE Robotics and Automation Letters 3, no. 2 (April 2018): 1072–79. http://dx.doi.org/10.1109/lra.2018.2794626.

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Lin, Shifeng, and Ning Wang. "Cloud robotic grasping of Gaussian mixture model based on point cloud projection under occlusion." Assembly Automation 41, no. 3 (April 5, 2021): 312–23. http://dx.doi.org/10.1108/aa-11-2020-0170.

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Purpose In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud. Design/methodology/approach First, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one. Findings In this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully. Originality/value In the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well.

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Romanov, A. M. "A review on control systems hardware and software for robots of various scale and purpose. Part 1. Industrial robotics." Russian Technological Journal 7, no. 5 (October 15, 2019): 30–46. http://dx.doi.org/10.32362/2500-316x-2019-7-5-30-46.

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A review of robotic systems is presented. The paper analyzes applied hardware and software solutions and summarizes the most common block diagrams of control systems. The analysis of approaches to control systems scaling, the use of intelligent control, achieving fault tolerance, reducing the weight and size of control system elements belonging to various classes of robotic systems is carried out. The goal of the review is finding common approaches used in various areas of robotics to build on their basis a uniform methodology for designing scalable intelligent control systems for robots with a given level of fault tolerance on a unified component base. This part is dedicated to industrial robotics. The following conclusions are made: scaling in industrial robotics is achieved through the use of the modular control systems and unification of main components; multiple industrial robot interaction is organized using centralized global planning or the use of previously simulated control programs, eliminating possible collisions in working area; intellectual technologies in industrial robotics are used primarily at the strategic level of the control system which is usually non-real time, and in some cases even implemented as a remote cloud service; from the point of view of ensuring fault tolerance, the industrial robots developers are primarily focused on the early prediction of faults and the planned decommissioning of the robots, and are not on highly-avaliability in case of failures; industrial robotics does not impose serious requirements on the dimensions and weight of the control devices.

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Frías, E., J. Balado, L. Díaz-Vilariño, and H. Lorenzo. "POINT CLOUD ROOM SEGMENTATION BASED ON INDOOR SPACES AND 3D MATHEMATICAL MORPHOLOGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W1-2020 (September 3, 2020): 49–55. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w1-2020-49-2020.

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Abstract. Room segmentation is a matter of ongoing interesting for indoor navigation and reconstruction in robotics and AEC. While in robotics field, the problem room segmentation has been typically addressed on 2D floorplan, interest in enrichment 3D models providing more detailed representation of indoors has been growing in the AEC. Point clouds make available more realistic and update but room segmentation from point clouds is still a challenging topic. This work presents a method to carried out point cloud segmentation into rooms based on 3D mathematical morphological operations. First, the input point cloud is voxelized and indoor empty voxels are extracted by CropHull algorithm. Then, a morphological erosion is performed on the 3D image of indoor empty voxels in order to break connectivity between voxels belonging to adjacent rooms. Remaining voxels after erosion are clustered by a 3D connected components algorithm so that each room is individualized. Room morphology is retrieved by individual 3D morphological dilation on clustered voxels. Finally, unlabelled occupied voxels are classified according proximity to labelled empty voxels after dilation operation. The method was tested in two real cases and segmentation performance was evaluated with encouraging results.

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Wang, Fei, Lin Zhang, and Yuanjun Laili. "Multi-granularity service composition in industrial cloud robotics." Robotics and Computer-Integrated Manufacturing 78 (December 2022): 102414. http://dx.doi.org/10.1016/j.rcim.2022.102414.

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CHEN, Wuhui, Yuichi YAGUCHI, Keitaro NARUSE, Yutaka WATANOBE, and Keita NAKAMURA. "Latency-Aware Computation Offloading Algorithm for Cloud Robotics." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 1P2—I02. http://dx.doi.org/10.1299/jsmermd.2017.1p2-i02.

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Xia, Chongkun, Yunzhou Zhang, Lei Wang, Sonya Coleman, and Yanbo Liu. "Microservice-based cloud robotics system for intelligent space." Robotics and Autonomous Systems 110 (December 2018): 139–50. http://dx.doi.org/10.1016/j.robot.2018.10.001.

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Oksa, Petri Tapani, and Tarmo Lipping. "Reliability of ROS Networked Mobile Robots." International Journal of Open Source Software and Processes 10, no. 1 (January 2019): 34–48. http://dx.doi.org/10.4018/ijossp.2019010103.

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When working remotely with mobile robotics, a reliable wireless communication network becomes essential, especially in large operating regions. As most teleoperated robots rely on standard Wi-Fi communication, network behavior has a crucial effect on autonomous robot control. The main goal of this research is to measure and diagnose the system reliability, roaming issues, and bottlenecks of such data transmission. To study these significant factors, two measurement scenarios were conducted. Measurements consist of two Wi-Fi access points (AP) and a TurtleBot II robot used in two different system set-up configurations. In the first configuration, two APs are connected in bridge mode (LAN connection) and in the second configuration the APs are connected in WDS bridge (Wireless Data Distribution) mode. This article presents the results of Robot Operating System (ROS) IEEE 802.11 network measurements in roaming mode, in wireless bridge mode, and in an extended coverage area employed in WDS mode. Results of data transmission measurements, configurations, and evaluation of the entire system are also presented. All the measurements utilize the Open Cloud Robotic Platform (OpenCRP)1 an open-source cloud robotics ecosystem based on service-oriented PaaS architecture using the Ubuntu Linux operating system.

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Lin, Yu, Yigong Wang, Yi-Fan Li, Zhuoyi Wang, Yang Gao, and Latifur Khan. "Single View Point Cloud Generation via Unified 3D Prototype." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2064–72. http://dx.doi.org/10.1609/aaai.v35i3.16303.

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As 3D point clouds become the representation of choice for multiple vision and graphics applications, such as autonomous driving, robotics, etc., the generation of them by deep neural networks has attracted increasing attention in the research community. Despite the recent success of deep learning models in classification and segmentation, synthesizing point clouds remains challenging, especially from a single image. State-of-the-art (SOTA) approaches can generate a point cloud from a hidden vector, however, they treat 2D and 3D features equally and disregard the rich shape information within the 3D data. In this paper, we address this problem by integrating image features with 3D prototype features. Specifically, we propose to learn a set of 3D prototype features from a real point cloud dataset and dynamically adjust them through the training. These prototypes are then integrated with incoming image features to guide the point cloud generation process. Experimental results show that our proposed method outperforms SOTA methods on single image based 3D reconstruction tasks.

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FLORES MONTAÑO, LUIS ALBERTO, JUAN CARLOS HERRERA LOZADA, JACOBO SANDOVAL GUTIERREZ, RODRIGO VAZQUEZ LOPEZ, and DANIEL LIBRADO MARTINEZ VAZQUEZ. "CIBERSECURITY ON INTERNET OF ROBOTICS THINGS: EXPERIMENTAL PLATFORM." DYNA 96, no. 5 (September 1, 2021): 540–45. http://dx.doi.org/10.6036/10022.

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The Internet of Robotic Things (IoRT) is a technology that looks for monitoring, operating, and maintaining the tasks of multiple robots through the cloud. However, using these robots in cyberspace has a risk and an inherent problem in cybersecurity. To analyze the implications of this technology, the objective was to design, operate and submit an IoRT system with the default configuration. The proposed methodology consisted of designing an IoRT architecture; implement three robotic platforms linked to the cloud, applying a sniffing and spoofing cyberattacks, assess the impacts, and propose solutions. The experiment used three prototypes: two servo motors, a 6-degree-of-freedom arm, and a workstation with a robot. Additionally, the tools of the experiment were a conventional computer, a Raspberry Pi microcomputer, the Robotic Operative System middleware, the Kali Linux distribution, and the ThingSpeak cloud service. The contributions of the work were three, first it was proven that four types of links are sufficient to homologate, and ensure the integrity, reliability, and availability in the operation of different types of robots. Also, it was possible the connection of these robots even though they are not designed to work on the internet through a slave-robot node link. Finally, a real list of the consequences was obtained, given the vulnerabilities and the attacks tested, as well as some recommendations.Keywords: Cybersecurity, IoRT, Industry 4.0., Common Vulnerabilities and Exposures, Cloud, ROS.

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Watanobe, Y., Y. Yaguchi, K. Nakamura, T. Miyaji, R. Yamada, and K. Naruse. "Architecture and framework for data acquisition in cloud robotics." International Journal of Information Technology, Communications and Convergence 4, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijitcc.2021.119082.

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Ermacora, Gabriele, Stefano Rosa, and Antonio Toma. "Fly4SmartCity: A cloud robotics service for smart city applications." Journal of Ambient Intelligence and Smart Environments 8, no. 3 (April 27, 2016): 347–58. http://dx.doi.org/10.3233/ais-160374.

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Vijaykumar and Saravanakumar. "Future Robotics Database Management System Along with Cloud TPS." International Journal on Cloud Computing: Services and Architecture 1, no. 3 (November 30, 2011): 103–13. http://dx.doi.org/10.5121/ijccsa.2011.1308.

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Sugiura, Komei, Yoshinori Shiga, Hisashi Kawai, Teruhisa Misu, and Chiori Hori. "A cloud robotics approach towards dialogue-oriented robot speech." Advanced Robotics 29, no. 7 (March 27, 2015): 449–56. http://dx.doi.org/10.1080/01691864.2015.1009164.

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Wang, Xi Vincent, Lihui Wang, Abdullah Mohammed, and Mohammad Givehchi. "Ubiquitous manufacturing system based on Cloud: A robotics application." Robotics and Computer-Integrated Manufacturing 45 (June 2017): 116–25. http://dx.doi.org/10.1016/j.rcim.2016.01.007.

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Yan, Hehua, Qingsong Hua, Yingying Wang, Wenguo Wei, and Muhammad Imran. "Cloud robotics in Smart Manufacturing Environments: Challenges and countermeasures." Computers & Electrical Engineering 63 (October 2017): 56–65. http://dx.doi.org/10.1016/j.compeleceng.2017.05.024.

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Hou, Gang, Kuanjiu Zhou, Tie Qiu, Xun Cao, Mingchu Li, and Jie Wang. "A novel green software evaluation model for cloud robotics." Computers & Electrical Engineering 63 (October 2017): 139–56. http://dx.doi.org/10.1016/j.compeleceng.2017.08.021.

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Kehoe, Ben, Sachin Patil, Pieter Abbeel, and Ken Goldberg. "A Survey of Research on Cloud Robotics and Automation." IEEE Transactions on Automation Science and Engineering 12, no. 2 (April 2015): 398–409. http://dx.doi.org/10.1109/tase.2014.2376492.

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Civera, Javier, Matei Ciocarlie, Alper Aydemir, Kostas Bekris, and Sanjay Sarma. "Guest Editorial Special Issue on Cloud Robotics and Automation." IEEE Transactions on Automation Science and Engineering 12, no. 2 (April 2015): 396–97. http://dx.doi.org/10.1109/tase.2015.2409511.

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Bui, Van Bien, Tien Long Banh, Duc An Pham, and Duc Toan Nguyen. "An efficient method for reflective surface scanning using Kinect v2." Ministry of Science and Technology, Vietnam 63, no. 1 (January 30, 2021): 34–38. http://dx.doi.org/10.31276/vjst.63(1).34-38.

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ToF cameras, also identified as range imaging cameras, are a recent generation of sensors. As they are desirable for measuring distances to objects at a high frame rate, such sensors are increasingly used for 3D acquisitions, and more generally for applications in robotics or reverse engineering. In this study, the authors presented an efficient method for reflective surface scanning using Kinect v2. The approach is based on the principles of the ICP (Iterative Closest Point) algorithm. During the Kinect v2 scanning, the obtained point cloud is incomplete and includes flying pixels. The authors solve the flying pixel problem by capturing point clouds as multiple viewpoints. Each point cloud belongs to each coordinate. Then, all of the point clouds must be aligned and combined using the ICP algorithm. The experimental results show that the quality of the method is significantly improved.

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Ogawa, Hiroaki, Keito Shishiki, Udaka A. Manawadu, and Keitaro Naruse. "Large Area Inspection Using 3D Point Cloud Data in a Disaster Response Robot." SHS Web of Conferences 102 (2021): 04010. http://dx.doi.org/10.1051/shsconf/202110204010.

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Large area inspection using a robot is critical in a disastrous situation; especially when humans are inhabiting the catastrophic environment. Unlike natural environments, such environments lack details. Thus, creating 3D maps and identifying objects has became a challenge. This research suggests a 3D Point Cloud Data (PCD) merging algorithm for the less textured environment, aiming World Robot Summit Standard Disaster Robotics Challenge 2021 (WRS). Spider2020, a robotic system designed by the Robot Engineering Laboratory, University of Aizu, was used in this research. Detecting QR codes in a wall and merging PCD, and generating a wall map are the two main tasks in the competition. The Zxing library was used to detect and decode QR codes, and the results were quite accurate. Since the 3D mapping environment has fewer textures, decoded QR code locations are used as the PCD mapping markers. The position of the PCD file was taken from the location given by the robotic arm in Spider2020. The accuracy of merging PCD was improved by including the position of PCD files in the merging algorithm. The robotic system can be used for Large area Inspections in a disastrous situation.

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Roy, Swarnabha, Tony Vo, Steven Hernandez, Austin Lehrmann, Asad Ali, and Stavros Kalafatis. "IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework." Sensors 22, no. 15 (July 26, 2022): 5569. http://dx.doi.org/10.3390/s22155569.

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There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Ground Vehicle (UGV) as a rover. Each robot is equipped with sensors to gather information to send to a cloud server where all computations are performed. Each vehicle is registered by blockchain ledger-based network security. In addition to these, relevant information and alerts are displayed on a website for the users. The usage of UAV–UGV cooperation allows for autonomous surveillance due to the high vantage field of view. Furthermore, the usage of cloud computation lowers the cost of microcontrollers by reducing their complexity. Lastly, blockchain technology mitigates the security issues related to adversarial or malicious robotic nodes connecting to the cluster and not agreeing to privacy rules and norms.

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Hasko, R. Т., and N. B. Shakhovska. "LEARNING ROBOTICS PLATFORM FOR FOG / EDGE / AIOT." Bionics of Intelligence 2, no. 93 (December 2, 2019): 34–38. http://dx.doi.org/10.30837/bi.2019.2(93).06.

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The article describes a three-level training robotics platform for use in the learning process. The proposed platformis an organic part of the modern spiral of development of information processes and systems and allows to improve thequality of the educational process, to increase its orientation towards practical experience and to reflect the current stateof AI synergy through Edge / Fog / Cloud with IoT

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Siriweera, Akila, and Keitaro Naruse. "Survey on Cloud Robotics Architecture and Model-Driven Reference Architecture for Decentralized Multicloud Heterogeneous-Robotics Platform." IEEE Access 9 (2021): 40521–39. http://dx.doi.org/10.1109/access.2021.3064192.

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Karri, Chiranjeevi, Omar Cheikhrouhou, Ahmed Harbaoui, Atef Zaguia, and Habib Hamam. "Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study." Applied Sciences 11, no. 14 (July 15, 2021): 6522. http://dx.doi.org/10.3390/app11146522.

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Real-time robotic applications encounter the robot on board resources’ limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task’s accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms.

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Gao, Ang, Yansu Hu, Lixin Li, Weijun Duan, and Huisheng Zhang. "A BP Network Control Approach for QoS-Aware MAC in Cloud Robotics." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (April 2018): 315–22. http://dx.doi.org/10.1051/jnwpu/20183620315.

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The basic idea of Cloud Robotics is dynamically uploading the compute-intensive applications to the cloud, which greatly enhances the intelligence of robots for the high processing and parallel ability of cloud. However, for the nature of uncertainty of mobility, different kinds of applications on robot may have different Quality of service (QoS). The paper proposes a BP network for QoS-aware MAC(BPFD-MAC) in Cloud Robotics form a view control theory, which can support both absolute and relative QoS guarantees while the energy saving. The hard and soft QoS constraints are de-coupled by normalized into a two-level cascade feedback loop. The former is Active Time Loop (AT-Loop) to enforce the absolute QoS guarantee for real-time application and the later is Contention Window Loop (CW-Loop) to enforce the relative QoS guarantee for Best Effort traffics. Finally, the Back-propagating (BP) neuron network based PID is used for self-tuning parameters and controller design. The hardware experiments demonstrate the feasibility of BPFD-MAC. Comparing with FD-MAC, BPFD-MAC has new feature of absolute QoS support and further developed two advantages:In the condition of heavy loads, BPFD have about 18% great throughput and 14% great power efficient; and in light load, BPFD have lower total energy consumption.

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Wang, Wenxin, Changming Zhao, and Haiyang Zhang. "PR-Alignment: Multidimensional Adaptive Registration Algorithm Based on Practical Application Scenarios." Machines 11, no. 2 (February 8, 2023): 254. http://dx.doi.org/10.3390/machines11020254.

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In the present day, 3D point clouds are considered to be an important form of representing the 3D world. In computer vision, mobile robotics, and computer graphics, point cloud registration is a basic task, and it is widely used in 3D reconstruction, reverse engineering, among other applications. However, the mainstream method of point cloud registration is subject to the problems of a long registration time as well as a poor modeling effect, and these two factors cannot be balanced. To address this issue, we propose an adaptive registration mechanism based on a multi-dimensional analysis of practical application scenarios. Through the use of laser point clouds and RGB images, we are able to obtain geometric and photometric information, thus improving the data dimension. By adding target scene classification information to the RANSAC algorithm, combined with geometric matching and photometric matching, we are able to complete the adaptive estimation of the transformation matrix. We demonstrate via extensive experiments that our method achieves a state-of-the-art performance in terms of point cloud registration accuracy and time compared with other mainstream algorithms, striking a balance between expected performance and time cost.

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