Towards an Architecture for Mobile Cloud Robotics
Aparicio Carranza2013
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
Cloud computing is a ubiquitous term used today for computational models that rely on interconnected computers over the Internet offering greater processing power and storage capabilities than stand-alone solutions. Cloud computing has been applied to diverse fields including cloud robotics and mobile cloud. Cloud robotics is a paradigm where robots offload their heavy computations and storage needs to the cloud while focusing on simpler computation tasks. Mobile cloud computing (MCC) is the reliance on mobile devices such as smartphones and tablets for data gathering and simple processing while using the cloud for resource intensive tasks. We propose a mobile cloud robotics architecture that combines these two concepts. Three different architectural models for mobile cloud robotics are provided: the Lego NXT Mobile Cloud Robotics Architecture, the Android Powered Lego NXT Mobile Cloud Robotics Architecture, and the Hybrid Lego NXT Mobile Cloud Robotics Architecture. These architect...
Related papers
Cloud Robotics
Companion Proceedings of the10th International Conference on Utility and Cloud Computing, 2017
Robots are moving out of factories, service robotics is bringing them to our homes, work environments, cities, and outdoors. While the Robot Operating System (ROS) is promising to open the world of robotics to developers, a proper platform and ecosystem supporting robotic applications development is still missing. This work presents an example of cloud robotics application in which cloud computing is not just complementing limited robot capabilities, but is leveraged to provide a development and operations environment supporting the complete life-cycle of a robotics-enabled application. We relate on our experience building cloud robotics applications spanning heterogeneous hardware (i.e., robots and cloud servers) through a use case scenario.
Cloud robotics: architecture, challenges and applications
IEEE Network, 2000
We extend the computation and information sharing capabilities of networked robotics by proposing a cloud robotic architecture. The cloud robotics architecture leverages the combination of a virtual ad-hoc cloud formed by machine-to-machine (M2M) communications among participating robots, and an infrastructure cloud enabled by machine-to-cloud (M2C) communications. Cloud robotics utilizes elastic computing models, in which resources are dynamically allocated from a shared resource pool in the cloud, to support task offloading and information sharing in robotic applications. We propose communication protocols, and several elastic computing models to handle different applications. We discuss the technical challenges in computation, communications and security, and illustrate the potential benefits of cloud robotics in several applications.
Towards a Distributed Computation Offloading Architecture for Cloud Robotics
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019
Cloud robotics is incessantly gaining ground, especiallywith the rapid expansion of wireless networks and Internetresources. In particular, computation offloading is emerging asa new trend, enabling robots with more powerful computationresources. It helps them to overcome the hardware and softwarelimitations by leveraging parallel computing capabilities andthe availability of large amounts of resources in the cloud.However, the performance gain of computation offloading incloud robotics is still an ongoing research problem becauseof the conflicting factors that affect the performance. In thispaper, we investigate this issue and we design a distributed cloudrobotic architecture for computation offloading based on Kafkamiddleware as messaging broker. We experimentally validatedour solution and tested its performance using image processingalgorithms. Experimental results show a significant reductionin robot CPU load, as expected, with an increase in robotcommunication delays.
Cloud Robotics Platforms: Review and Comparative Analysis
2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), 2018
Due to the various advantages that the cloud can offer to robots, there has been the recent emergence of the cloud robotics paradigm. Cloud robotics permits robots to unload computing and storage related tasks into the cloud, and as such, robots can be built with smaller on-board computers. The use of cloud-robotics also allows robots to share knowledge within the community over a dedicated cloud space. In order to build-up robots that benefit from the cloud-robotics paradigm, different cloud-robotics platforms have been released during recent years. This paper critically reviews and compares existing cloud robotic platforms in order to provide recommendations on future use and gaps that still need to be addressed. To achieve this, 8 cloud robotic platforms were investigated. Key findings reveal varying underlying architectures and models adopted by these platforms, in addition to different features offered to end-users.
Cloud robotics: A software architecture: For heterogeneous large-scale autonomous robots
2016 World Automation Congress (WAC), 2016
The paper proposes a software architecture for cloud robotics which intends three subsystems in the cloud environment: Middleware Subsystem, Background Tasks Subsystem, and Control Subsystem. The architecture invokes cloud technologies such as cloud computing, cloud storage, and other networking platforms arranged on the assistances of congregated infrastructure and shared services for robotics, for instance Robot Operating System (ROS). Since the architecture is looking for reliable, scalable, and distributed system for the heterogeneous large-scale autonomous robots, Infrastructure as a Service (IaaS) is chosen among the cloud services. Three major tasks can be handled by the proposed software architecture Computing, Storage, and Networking. Hadoop-MapReduce provides the appropriate framework in the cloud environment to process and handle these tasks.
DAvinCi: A cloud computing framework for service robots
2010 IEEE International Conference on Robotics and Automation, 2010
We propose DAvinCi, a software framework that provides the scalability and parallelism advantages of cloud computing for service robots in large environments. We have implemented such a system around the Hadoop cluster with ROS (Robotic Operating system) as the messaging framework for our robotic ecosystem. We explore the possibilities of parallelizing some of the robotics algorithms as Map/Reduce tasks in Hadoop. We implemented the FastSLAM algorithm in Map/Reduce and show how significant performance gains in execution times to build a map of a large area can be achieved with even a very small eight-node Hadoop cluster. The global map can later be shared with other robots introduced in the environment via a Software as a Service (SaaS) Model. This reduces the burden of exploration and map building for the new robot and minimizes it's need for additional sensors. Our primary goal is to develop a cloud computing environment which provides a compute cluster built with commodity hardware exposing a suite of robotic algorithms as a SaaS and share data cooperatively across the robotic ecosystem.
Exportation to the cloud of distributed robotic tasks implemented in ROS
Proceedings of the Symposium on Applied Computing, 2017
Cloud computing is a paradigm shift in computation that has been gaining traction over the recent years, which is supported by the increasing ubiquity of a reliable wireless connection to the Internet. Cloud robotics, which aims at bringing this principle to the field of Mobile Robotics, allows robots accessing seemingly unlimited external computation, thus being able to free onboard computation power and perform more complex tasks or tasks that were not able to run otherwise. This paper describes the migration of two multi-robot tasks previously implemented and tested in ROS by our research group - multi-robot SLAM and multi-robot patrolling - to a cloud robotics-based implementation using the Rapyuta framework [12], with the aim of studying the tradeoff between robots' computation load decrease and bandwidth usage increase. With this purpose, both simulations and experiments with real robots were conducted.
Cloud Robotics: Towards Context Aware Robotic Networks
2011
This paper proposes an approach for an automated system composed by mobile robots and a smart-room following service oriented architecture, aiming to undertake complex and heavily computational tasks to aid the user in the execution of determined tasks. The proposed approach is inspired by the principles of Service Oriented Architecture, relying in cloud computing to provide an increased degree of scalability to the system. The robotic system will complement the group of virtual networks that the user may already be a part of, contributing as a connection bridge between virtual and real "worlds". The objective of this work targets the implementation of a service robotic system that allows distant groups of robots to share and exchange learned skills and improve cooperation with human agents. The connection to the cloud plays the role of knowledge repository for the robotic system. This will allow for distant groups of robots to share and exchange each other's learned skills and adapt to new situations of cooperation with human agents. A use case scenario is presented and suggests the application of the system in Assisted Living situations. In this scenario the context aware ability orchestrate the system towards providing health care services.
Managing a fleet of autonomous mobile robots (AMR) using cloud robotics platform
2017 European Conference on Mobile Robots (ECMR)
In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.
Towards An Architecture-Centric Approach to Manage Variability of Cloud Robotics
ArXiv, 2017
Cloud robotics is a field of robotics that attempts to invoke Cloud technologies such as Cloud computing, Cloud storage, and other Internet technologies centered around the benefits of converged infrastructure and shared services for robotics. In a few short years, Cloud robotics as a newly emerged field has already received much research and industrial attention. The use of the Cloud for robotics and automation brings some potential benefits largely ameliorating the performance of robotic systems. However, there are also some challenges. First of all, from the viewpoint of architecture, how to model and describe the architectures of Cloud robotic systems? How to manage the variability of Cloud robotic systems? How to maximize the reuse of their architectures? In this paper, we present an architecture approach to easily design and understand Cloud robotic systems and manage their variability.
Related papers
Cloud Robotic Architectures: Directions for Future Research from a Comparative Analysis
2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), 2018
Advances in robotics and cloud computing have led to the emergence of cloud robotics where robots can benefit from remote processing, greater memory and computational power, and massive data storage. The integration of robotics and cloud computing has often been regarded as a complex aspect due to the various components involved in such systems. In order to address this issue, different studies have attempted to create cloud robotic architectures to simplify representation into different blocks or components. However, limited study has been undertaken to critically review and compare these architectures. As such, this paper investigates and performs a comparative analysis of existing cloud robotic architectures in order to identify key limitations and recommend on the future of cloud robotic architectures. As part of this study, 7 such architectures have been reviewed and compared and results showed limited evaluation of existing architectures in favour of security weaknesses.
Cloud Robotics: A Survey
The idea of cloud robotics attracts many researchers mind in the last few years. Cloud robotics is a term combination of cloud technologies with its mass and services which is combined to serve the huge use of robotics applications. The power of robotics is behind the power of cloud which aims in the process of learning and exchanging knowledge, the use of cloud to process heavy tasks allows use of smaller on-board computers in a robot which needs to perform tasks in an accurate real time. So in realistically the Cloud can make robots lighter, cheaper and smaller. This paper surveys cloud robotics in order to give a clear platform in this technology.
The rising prospects of cloud robotic applications
2013 IEEE 9th International Conference on Computational Cybernetics (ICCC), 2013
Cloud Robotics is an emerging field within robotics, currently covering various application domains and robot network paradigms. This paper provides a structured, systematic overview of the numerous definitions, concepts and technologies linked to Cloud Robotics and cloud technologies in a broader sense. It also presents a roadmap for the near future, describing development trends and emerging application areas. Cloud Robotics may have a significant role in the future as an explicitly human-centered technology, capable of addressing the dire needs of our society.
Mobile Cloud Robotics as a Service with OCCIware
2017
HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Copyright
An Approach towards Survey and Analysis of Cloud Robotics
Advances in Information Security, Privacy, and Ethics
This chapter highlights the total structure and capabilities of robotic systems. This chapter then discusses the invocation of cloud technology in robotics technology empowering the whole system with higher processing power and bigger storage unit which was not possible earlier in the conventional robotic system being restricted in on-board manipulation. The flexibility of handling big data, ability to perform cloud computing, crowed sourcing and collaborative robot learning using the cloud robotics technology has been discussed briefly. This chapter describes concepts of Cloud Enabled Standalone Robotic System (CeSRS), Cloud Enabled Networked Robotic System (CeNRS), Cloud Robotic Networking System (CRNS), Standalone Robotic System (SRS), Common Networked Robotic (CNRS), Infrastructure As A Service (IAAS), Multi Robot System, R/R and R/C Network, ROS, Tele Operated Robotic System, Quality of Service (QoS), Virtual Machine (VM) and Cloud Datacenter. The existing applications of the c...
Cloud Robotics using ROS
Ijca Proceedings on National Conference Cum Workshop on Bioinformatics and Computational Biology, 2014
The application of the cloud computing concept to robots is called Cloud Robotics. It is a concept that utilizes the services of the cloud so that robots can have learning abilities. Since applications for Cloud Robotics have to be developed in a platform, majority of the cloud application developers choose ROS for it. Robot Operating System (ROS) is an open source middleware that has a collection of inter-programming language headers to allow the sharing of data between independent programs. ROS provides a graph-like structure for cloud robotics. A new library for ROS that is a pure Java implementation, called rosjava, allows Android applications to be developed for robots. Since Android has a booming market and billion users, it would be a huge leap in the field of Cloud Robotics.
A Study of Robotic Cooperation in Cloud Robotics: Architecture and Challenges
IEEE Access
Networked robotics involves a collection of robots working together to perform complex tasks, such as search and rescue task in disaster management. Because such tasks are beyond the capacity of a single powerful robot, networked robotics has been widely researched. However, the modes of cooperation in traditional networked robotics have been restricted by the inherent physical constraint that all computations are performed in the robotic network, with knowledge sharing being limited to the collective storage in the network. Cloud robotics, which allows robots to benefit from the rich storage, computation, and communication resources of modern data centers, is widely accepted as a promising approach to efficient robot cooperation in applications, such as disaster management. In this paper, we study robotic cooperation in cloud robotics. We first give a conceptual view of the nature of this cooperation. We then propose three novel robotic cooperation frameworks for cloud robotics: robotic knowledge sharing cooperation, robotic physical-task cooperation, and robotic computation task cooperation. Finally, we identify several critical challenges, and illustrate the potential benefits of robotic cooperation in cloud robotics. INDEX TERMS Cloud robotics, multi-robot system, robot cooperation, quality of service.
Cloud robotics architecture and challenges on disaster management
HIGH-ENERGY PROCESSES IN CONDENSED MATTER (HEPCM 2020): Proceedings of the XXVII Conference on High-Energy Processes in Condensed Matter, dedicated to the 90th anniversary of the birth of RI Soloukhin, 2020
Disaster management requires fast and efficient handling of information. To deal with this issue, it does not only involve humans as the primary resource but also the various tools involved, such as robots. Cloud robotics extends the computation and information sharing among several types of robots so that robots can work as teamwork to complete a mission. This paper provides an overview of cloud robotics implementation to help deal with disasters that occur. Robots are used to mapping disaster-affected areas, identifying objects, and sending data real-time to the cloud server. This paper also discusses the implementation of typical network architecture on disaster case using fog computing paradigm. We propose using the MQTT protocol for information distribution from the robot side to the cloud server. We compare its performance with the HTTP protocol. The latest challenges and problems are also discussed in this paper.
Five Traits of Performance Enhancement using Cloud Robotics: A Survey
Recently, robots and automation systems have been at the front of research with the majority of systems still operating independently using onboard computation, memory manipulation and communication. With improvements in communication technology and the increasing availability of network, new approaches where robot and automation processing is performed remotely with access to large scale datasets, support a range of functions. Cloud Robotics supplements performance enhancement of robotics and autonomous systems by providing a global infrastructure in innovative ways. This paper summarizes recent research into five traits of Cloud Robotics for performance enhancement in robotics and autonomous systems: 1) Remote Brain, 2) Big Data and Shared Knowledge-base, 3) Collective Learning, 4) Intelligence and Behavior, and 5) Cloud architectures. Towards the end, in this survey, we present future directions for research in cloud robotics.
Robotic Technology-Based Cloud Computing for Improved Services
Springer Nature Singapore Pte Ltd, 2020
Cloud is Internet-based utility computing which offers a choice to rent and use the computational resources on subscription basis. It has the service-oriented and deployment models which would provide suitable services to the end-users. Robotics can be defined as a machine which would do some useful works as given by the user which would reduce his/her work and make smooth transactions. Cloud robotics is an upcoming technological area with a combination of cloud computing and robotics. Interconnection of these two fields would profit the mankind. Many more different varieties of applications are emerging as on today. In coming of the days, cloud robots will have major role to perform, such as to improve the 'public utility of technology'. This paper describes the laws of robotics, characteristics of robotics, need for cloud robotics, initial steps for cloud robotics, available cloud robotic technologies, constraints on cloud robotics, cloud robotics architectures, addressing needs of cloud robotics, cloud robotic algorithm-SLAM. This present paper would bring out a review of all such aspects which would benefit the reader with a fine tune.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.