Ontological concepts for information sharing in cloud robotics

Freitas, Edison Pignaton de; Olszewska, Joanna Isabelle; Carbonera, Joel Luís; Fiorini, Sandro Rama; Khamis, Alaa; Barreto, Marcos; Prestes, Edson; Habib, Maki K.; Redfield, Signe; Chibani, Abdelghani; Gonçalves, Paulo J. S.; Bermejo–Alonso, Julita; Sanz, Ricardo; Tosello, Elisa; Olivares-Alarcos, Alberto; Konzen, Andréa Aparecida; Quintas, JoÃ£o; Li, Howard

doi:10.1007/s12652-020-02150-4

Cited by 19 publications

(8 citation statements)

References 50 publications

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“…Robotics engineering is becoming a vital part of everyday life, with diverse sensors generating big data demanding complex computations [ 92 ]. Cloud robotics is a branch of IoT that evolved from the fusion of cloud computing and networked robots [ 93 ]. The massive storage capacity of a centralized cloud and broad library of skills can be leveraged by robots to learn with experience.…”

Section: Evolving Computing Paradigms and Related Conceptsmentioning

confidence: 99%

“…The sharing of resources and data between robots through the cloud is integral to CR, along with robots themselves being shared as resources. These systems require standards for enabling coherent, semantic, data sharing, and service provisioning among the robots [ 93 ]. Cloud computing delegates fast and robust processing and storage capabilities to robots, along with collaborative learning capabilities through the sharing of knowledge.…”

Section: Evolving Computing Paradigms and Related Conceptsmentioning

confidence: 99%

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Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

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Section: Evolving Computing Paradigms and Related Conceptsmentioning

confidence: 99%

Section: Evolving Computing Paradigms and Related Conceptsmentioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…We have included overall latency analysis in this article, and we intend to delve further to understand sub-component latency as well. Cloud-based solutions in telerobotics [16] can offer a promising alternative allowing complex computation to be offloaded to the cloud servers. The bandwidth and latency outcomes will need to be understood for such an approach.…”

Section: Limitations At the Current Stagementioning

confidence: 99%

The Vicarios Virtual Reality Interface for Remote Robotic Teleoperation

Naceri

Mazzanti

Bimbo

et al. 2021

J Intell Robot Syst

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Intuitive interaction is the cornerstone of accurate and effective performance in remote robotic teleoperation. It requires high-fidelity in control actions as well as perception (vision, haptic, and other sensory feedback) of the remote environment. This paper presents Vicarios, a Virtual Reality (VR) based interface with the aim of facilitating intuitive real-time remote teleoperation, while utilizing the inherent benefits of VR, including immersive visualization, freedom of user viewpoint selection, and fluidity of interaction through natural action interfaces. Vicarios aims to enhance the situational awareness, using the concept of viewpoint-independent mapping between the operator and the remote scene, thereby giving the operator better control in the perception-action loop. The article describes the overall system of Vicarios, with its software, hardware, and communication framework. A comparative user study quantifies the impact of the interface and its features, including immersion and instantaneous user viewpoint changes, termed “teleporting”, on users’ performance. The results show that users’ performance with the VR-based interface was either similar to or better than the baseline condition of traditional stereo video feedback, approving the realistic nature of the Vicarios interface. Furthermore, including the teleporting feature in VR significantly improved participants’ performance and their appreciation for it, which was evident in the post-questionnaire results. Vicarios capitalizes on the intuitiveness and flexibility of VR to improve accuracy in remote teleoperation.

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“…Cloud robotics is a field of robotics that aims to use cloud technologies, services, and various cloud resources for robotics [10]. In this paper, we propose a new cloud system for managing swarm robot systems to perform multi-duty in real-time.…”

Section: Introductionmentioning

confidence: 99%

A new cloud autonomous system as a service for multi-mobile robots

Nasr

2022

Neural Comput & Applic

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Today, mobile robot is used in most industrial and commercial fields. It can improve and carry out work complex tasks quickly and efficiently. However, using swarm robots to execute some tasks requires a complex system for assigning robots to these tasks. The main issue in the robot control systems is the limited facilities of robot embedded system components. Although, some researchers used cloud computing to develop robot services. They didn’t use the cloud for solving robot control issues. In this paper, we have used cloud computing for controlling robots to solve the problem of limited robot processing components. The main advantage of using cloud computing is its intensive computing power. This advantage motivates us to propose a new autonomous system for multi-mobile robots as a services-based cloud computing. The proposed system consists of three phases: clustering phase, allocation phase, and path planning phase. It groups all tasks/duties into clusters using the k-means algorithm. After that, it finds the optimal path for each robot to execute its duties in the cluster based on the Nearest neighbor and Harris Hawks Optimizer (HHO). The proposed system is compared with systems that use a genetic algorithm, simulated annealing algorithm, and HHO algorithm. From the finding, we find that the proposed system is more efficient than the other systems in terms of decision time, throughput, and the total distance of each robot.

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Ontological concepts for information sharing in cloud robotics

Cited by 19 publications

References 50 publications

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

The Vicarios Virtual Reality Interface for Remote Robotic Teleoperation

A new cloud autonomous system as a service for multi-mobile robots

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