A Software Product Line Approach for Configuring Cloud Robotics Applications

Gherardi, Luca; Hunziker, Dominique; Mohanarajah, Gajamohan

doi:10.1109/cloud.2014.104

Cited by 20 publications

(13 citation statements)

References 24 publications

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“… Approach D: (Configuring Cloud Robotics Applications) (Gherardi, Hunziker, & Mohanarajah, 2014) explored the use of Extended Feature Models (EFMs) to provide support for the design and configuration of robotic applications when migrating to a cloud environment. The Models can be automatically transformed into XML and JSON configuration files offering support for the application migration to the cloud.…”

Section: Related Workmentioning

confidence: 99%

A Context-Aware and Self-Adaptation Strategy for Cloud Service Selection and Configuration in Run-Time

Benali¹,

Asri²

2019

CIS

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Day after day, the number of mobile applications deployed on cloud computing continues in increasing because of smartphone capabilities improvement. Cloud computing has already succeeded in the web-based application, for that reason, the demand for context-aware services provided by cloud computing increases. To customize a cloud service that takes into account the consumer requirements, which depend on information change, it brings to light many recent challenges to cloud computing about environment-aware, location-aware, time-aware. The cloud provider, moreover, has to manage personalized applications and the constraints of mobile devices in matters of interaction abilities and communication restrictions. This paper proposes a strategy for selecting automatically an appropriate cloud environment that runs out whole requirements, defines a configuration for the associated cloud environment and able to easily adapt to the change of the environment on either the user or the cloud side or both. This process builds on the principles of dynamic software product lines, Agent-oriented software engineering, and the MAPE-k model to select and configure cloud environments according to the consumer needs and the context change.

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Section: Related Workmentioning

confidence: 99%

A Context-Aware and Self-Adaptation Strategy for Cloud Service Selection and Configuration in Run-Time

Benali¹,

Asri²

2019

CIS

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“…They proposed an architecture that enables cost-effectiveness by delegating virtualization and dynamic tasks to robots, including robots that could belong to other clouds. Gerardi et al [14] proposed a PaaS approach to a configurable suite of products based on cloud robotics applications. It allowed end users to be free from the low-level decisions needed to construct architectures of complex systems distributed among robots and clouds.…”

Section: Related Workmentioning

confidence: 99%

A Function as a Service Based Fog Robotic System for Cognitive Robots

Ahn

2019

Applied Sciences

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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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“…Several additional heuristics have been developed to address the problem of not knowing the extent of overlap and avoiding false point matches. The approach used here is similar to Iterative Closest Compatible Point (ICCP) algorithm (Godin et al, 1994), where points are matched only if their associated feature (colour, normal vector, etc.) are within a given threshold.…”

Section: Iterative Closest Point (Icp)mentioning

confidence: 99%

“…The direction of the arrows indicates the flow of data from the publishers to the topics and topics to subscribers, and arrows with labels connect service servers to service clients. Notably, this architecture was derived from the developers of the RCE (Gherardi et al, 2014).…”

Section: Figurementioning

confidence: 99%

“…Heroku (Lindenbaum et al, 2007) provides access to program APIs and sockets, however it does not allow the server to send data to the robot, and does not allow robots to share information through networked computing environments. Rosbridge (Crick et al, 2012) is open source, and enables communication between a robot and one ROS environment in the cloud (Gherardi et al, 2014).…”

Section: The Roboearth Cloud Engine (Rce)mentioning

confidence: 99%

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Feasibility study of using the RoboEarth cloud engine for rapid mapping and tracking with small unmanned aerial systems

Li-Chee-Ming¹,

Armenakis²

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents the ongoing development of a small unmanned aerial mapping system (sUAMS) that in the future will track its trajectory and perform 3D mapping in near-real time. As both mapping and tracking algorithms require powerful computational capabilities and large data storage facilities, we propose to use the RoboEarth Cloud Engine (RCE) to offload heavy computation and store data to secure computing environments in the cloud. While the RCE's capabilities have been demonstrated with terrestrial robots in indoor environments, this paper explores the feasibility of using the RCE in mapping and tracking applications in outdoor environments by small UAMS.The experiments presented in this work assess the data processing strategies and evaluate the attainable tracking and mapping accuracies using the data obtained by the sUAMS. Testing was performed with an Aeryon Scout quadcopter. It flew over York University, up to approximately 40 metres above the ground. The quadcopter was equipped with a single-frequency GPS receiver providing positioning to about 3 meter accuracies, an AHRS (Attitude and Heading Reference System) estimating the attitude to about 3 degrees, and an FPV (First Person Viewing) camera. Video images captured from the onboard camera were processed using VisualSFM and SURE, which are being reformed as an Application-as-a-Service via the RCE. The 3D virtual building model of York University was used as a known environment to georeference the point cloud generated from the sUAMS' sensor data. The estimated position and orientation parameters of the video camera show increases in accuracy when compared to the sUAMS' autopilot solution, derived from the onboard GPS and AHRS. The paper presents the proposed approach and the results, along with their accuracies.

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A Software Product Line Approach for Configuring Cloud Robotics Applications

Cited by 20 publications

References 24 publications

A Context-Aware and Self-Adaptation Strategy for Cloud Service Selection and Configuration in Run-Time

A Context-Aware and Self-Adaptation Strategy for Cloud Service Selection and Configuration in Run-Time

A Function as a Service Based Fog Robotic System for Cognitive Robots

Feasibility study of using the RoboEarth cloud engine for rapid mapping and tracking with small unmanned aerial systems

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