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 knowledge and mix new skills through cooperation. With multiple robots, a cloud robotic system allows intensive and sophisticated tasks to be disbursed in an optimal and cooperative manner. Multisensor knowledge retrieval (MSDR) is one in all the key elementary tasks to share the resources. Having attracted wide attention, MSDR is facing severe technical challenges. As an example, MSDR is particularly difficult when cloud cluster hosts accommodate unpredictable knowledge requests triggered by multiple robots operating in parallel. In these cases, close to real-time responses are essential whereas addressing the matter of the synchronization of multisensor information simultaneously. In this paper, we gift a framework targeting close to real-time MSDR, which grants asynchronous access to the cloud from the robots. We tend to propose a market-based management strategy for economical information retrieval. It's validated by assessing many 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 realize wonderful performance beneath the proposed management strategy in typical cloud robotic scenarios.

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