An intern-sufficient cloud for large-scale multi-robot systems and its application in multitarget navigation

Abstract

Due to generally limited computing capability of an individual robot, cloud-based robotic systems are increasingly used. However, applications in large-scale multi-robot systems will be hindered by communication congestion and consequent lack of computing resources. In this study, an intern-sufficient cloud is investigated to alleviate the burden of communication and thus support more robots. At the same time, it enables heterogeneously idle computing resources of robots inside the system to be shared on demand, instead of relying on cloud servers and communication infrastructures, to make the scope of application wider. To this end, a hierarchical communication mechanism and a resource schedule algorithm are proposed. In the mechanism, the transmission power, signal-to-noise ratio, available bandwidth, and other relevant features are taken into account to estimate link quality for data transmission. Then, the constrained communication conditions and heterogeneous computing resources are balanced by the resource scheduling algorithm, so that the most appropriate computing resources of the robots are contributed to the mobile cloud. Furthermore, a multitarget navigation task is applied on the cloud to validate the work. Thereby, simulations and experiments are performed. The results show that the proposed intern-sufficient cloud can provide stable resources of communication and computation for a multi-robot system with 20 physical robots while achieving more effective multitarget navigation.