Network Intelligence Empowered Industrial Robot Control in the F-RAN Environment

Abstract

Industrial robots are widely adopted in modern industries, including automobile manufacturing, warehousing, and so on. The industrial robot with cellular network connection will be more flexible and intelligent than that without a network connection or with a conventional industrial communication network connection (e.g., fieldbus, real-time Ethernet, and WiFi). This is seen as the direction of development of the next generation of industrial robots. As a promising candidate for the next generation of cellular networks, fog radio access network (F-RAN) can provide artificial intelligence (AI) on the network edge, which is equipped with some computing and storage facilities, thus promoting the development of industrial robots in the future. In this article, we first propose a local-network cooperative control architecture for industrial robots in the F-RAN environment, which divides the robot controller into an onboard controller and a network controller, in order to decouple the basic control functions and application-dependent functions. The network controller is implemented on the fog access node (F-AP), and it possibly uses AI to bring advanced capabilities to the robot control. Then, based on the proposed architecture, a testbed for multiple automated guided vehicle (AGV) coordination in F-RAN has been designed and implemented, and a recurrent neural network (RNN)-empowered multi-AGV coordination policy is also developed. Finally, its performance is examined by several experiments. The results show that network intelligence deployed in F-RAN can improve the scheduling efficiency up to 35 percent, and the proposed control architecture can save huge backhaul traffic compared to the conventional cloud-based control architecture for industrial robots.