Cloud Robotic System Research Articles

Design of a cloud robotic system to support senior citizens: the KuBo experience

This paper describes a system based on a robot, called KuBo, which relies on cloud resources to extend its capabilities for human interaction and environmental sensing to provide services for independent living. The robot uses text-to-speech and speech recognition services as well as an electronic agenda and web resources to perform several tasks. Moreover it retrieves smart environmental data from a DataBase to be aware of the context. In this paper, the cloud robotics approach is used to increase the skills of a robot, endowing the system with abilities for human–robot interaction and environmental sensing. The robotic services have been defined with a focus group involving 19 elderly volunteers and the system has been tested in a real environment with a couple of elderly users for five days. The aim of the experiment was to test the technical feasibility of the proposed cloud services using quantitative tools. The technical results show a success rate of 86.2 % for the navigation task and more than 90 % for the speech capabilities. Furthermore, the robustness of the system was also confirmed by users’ qualitative feedback.

A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems.

Cloud computing enables users to share computing resources on-demand. The cloud computing framework cannot be directly mapped to cloud robotic systems with ad hoc networks since cloud robotic systems have additional constraints such as limited bandwidth and dynamic structure. However, most multirobotic applications with cooperative control adopt this decentralized approach to avoid a single point of failure. Robots need to continuously update intensive data to execute tasks in a coordinated manner, which implies real-time requirements. Thus, a resource allocation strategy is required, especially in such resource-constrained environments. This paper proposes a hierarchical auction-based mechanism, namely link quality matrix (LQM) auction, which is suitable for ad hoc networks by introducing a link quality indicator. The proposed algorithm produces a fast and robust method that is accurate and scalable. It reduces both global communication and unnecessary repeated computation. The proposed method is designed for firm real-time resource retrieval for physical multirobot systems. A joint surveillance scenario empirically validates the proposed mechanism by assessing several practical metrics. The results show that the proposed LQM auction outperforms state-of-the-art algorithms for resource allocation.

Cloud Robotics: Current Status and Open Issues

With the development of cloud computing, big data, and other emerging technologies, the integration of cloud technology and multi-robot systems makes it possible to design multi-robot systems with improved energy efficiency, high real-time performance, and low cost. In order to address the potential of clouds in enhancing robotics for industrial systems, this paper describes the basic concepts and development process of cloud robotics and the overall architecture of these systems. Then, the major driving forces behind the development of cloud robotics are carefully analyzed from the point of view of cloud computing, big data, open source resources, robot cooperative learning, and network connectivity. Subsequently, the key issues and challenges in the current cloud robotic systems are proposed, and some possible solutions are also given. Finally, the potential value of cloud robotic systems in different practical applications is discussed.

Real-Time Multisensor Data Retrieval for Cloud Robotic Systems

Cloud technology elevates the potential of robotics with which robots possessing various capabilities and resources may share data and combine new skills through cooperation. With multiple robots, a cloud robotic system enables intensive and complicated tasks to be carried out in an optimal and cooperative manner. Multisensor data retrieval (MSDR) is one of the key fundamental tasks to share the resources. Having attracted wide attention, MSDR is facing severe technical challenges. For example, MSDR is particularly difficult when cloud cluster hosts accommodate unpredictable data requests triggered by multiple robots operating in parallel. In these cases, near real-time responses are essential while addressing the problem of the synchronization of multisensor data simultaneously. In this paper, we present a framework targeting near real-time MSDR, which grants asynchronous access to the cloud from the robots. We propose a market-based management strategy for efficient data retrieval. It is validated by assessing several quality-of-service (QoS) criteria, with emphasis on facilitating data retrieval in near real-time. Experimental results indicate that the MSDR framework is able to achieve excellent performance under the proposed management strategy in typical cloud robotic scenarios.

Peer to peer trade in HTM5 meta model for agent oriented cloud robotic systems

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.

Dynamic electronic institutions in agent oriented cloud robotic systems.

The dot-com bubble bursted in the year 2000 followed by a swift movement towards resource virtualization and cloud computing business model. Cloud computing emerged not as new form of computing or network technology but a mere remoulding of existing technologies to suit a new business model. Cloud robotics is understood as adaptation of cloud computing ideas for robotic applications. Current efforts in cloud robotics stress upon developing robots that utilize computing and service infrastructure of the cloud, without debating on the underlying business model. HTM5 is an OMG’s MDA based Meta-model for agent oriented development of cloud robotic systems. The trade-view of HTM5 promotes peer-to-peer trade amongst software agents. HTM5 agents represent various cloud entities and implement their business logic on cloud interactions. Trade in a peer-to-peer cloud robotic system is based on relationships and contracts amongst several agent subsets. Electronic Institutions are associations of heterogeneous intelligent agents which interact with each other following predefined norms. In Dynamic Electronic Institutions, the process of formation, reformation and dissolution of institutions is automated leading to run time adaptations in groups of agents. DEIs in agent oriented cloud robotic ecosystems bring order and group intellect. This article presents DEI implementations through HTM5 methodology.

Real-time Face Recognition for Cloud Robot

This paper discusses our work with the design and implementation of real-time face recognition for cloud robot. Face recognition has been widely used for surveillance and airport security. Robot systems which support face recognition are available too. As for robots, real-time feedbacks from sensors are always required. In order to recognize a face in real time, the images captured by camera have to be stored and then processed by face recognition algorithm. This processing procedure runs several times per second which is computationally intensive tasks for usual robot systems. Benefit from cloud computing technology, robot can offload computational tasks to cloud server which composes of several computational units (PCs) and is capable of executing distributed tasks. In our approach, real-time images will be captured and sent back to the cloud server. PCs in cloud side process the images in parallel. Face detection and recognition algorithm will be applied to each image and then the recognition results will be returned to the robot as the response. Our experimental results demonstrate that as a implementation of cloud robotics system, the proposed cloud robot is capable of doing real-time face recognition.

A Construction Strategy for Cloud Robotic System

A Construction Strategy for Cloud Robotic System