Cloud robotics paradigm for enhanced navigation of autonomous vehicles in real world industrial applications

Autonomous vehicles require advances sensing technologies, in order to be able to safely share the environment with human operators. Those sensing technologies are in fact necessary for identifying the presence of unforeseen objects, and measuring their position and velocity. Furthermore, classification is necessary for effectively predicting their behavior. In this paper we consider the presence of sensing systems both on-board each vehicle, and installed on infrastructural elements. While the simultaneous presence of multiple sources of information heavily improves the amount (and quality) of available data, it generates the need for effective data fusion and storage systems. Hence, we introduce a centralized cloud service, that is in charge of receiving and merging data acquired by different sensing systems. Those data are then distributed to the autonomous vehicles, that exploit them for implementing advanced navigation strategies. The proposed methodology is validated in a real industrial environment to safely perform obstacle avoidance with an autonomously driven forklift.