Real-Time Multisensor Data Retrieval for Cloud Robotic Systems

Abstract

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.Note to Practitioners-This paper was motivated by the problem of sharing resources in cloud robotic systems efficiently for accomplishing real-time tasks. Existing approaches to cloud robotics bear very strict assumptions that the resources are unconstrained and ubiquitous. However, there are technical challenges for multirobot systems to access the cloud and retrieve resources in near real-time. This paper presents a general framework for setting up cloud robotic system with a novel resource management strategy. We mathematically formulate the problem of multisensor data retrieval through the cloud as a Stackelberg game, and propose an optimal solution with proof. We then define the QoS criteria for evaluation considering the constraints of robotic tasks. In the experimental scenarios, our management mechanism significantly improves the performance for multisensor data retrieval in the evaluation of QoS, CPU load, and bandwidth usage.

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