Evaluation of 5G-capable framework for highly mobile, scalable human-machine interfaces in cyber-physical production systems

Abstract

Human cyber-physical production systems (HCPS) - as an extension of cyber-physical production systems (CPPS) - focus on the human being in the system and the development of socio-technical systems. Humans and therefore anthropogenic behavior have to be integrated into the manufacturing system and its processes. Due to the shift towards customized products, CPPS require high reconfigurability, flexibility and individual manufacturing processes. As a result, the role of and requirements for humans are fundamentally changing, creating the necessity for new interfaces to interact with machines within reconfigurable manufacturing process. Scalable human machine interfaces (HMI) are needed that incorporate emerging technologies as well as allowing mobility for the operator while interacting with different machines. Therefore, new approaches for HMI in the context of CPPS and HCPS are needed, which are simultaneously sufficiently mobile, scalable, and modular as well as human centered. High mobility of HMIs can be ensured by using the 5G communication standard that enables wireless migration of computational resources to an edge server with high reliability, low latency, and high data rates. This paper develops, implements, and evaluates a 5G-based, framework for highly mobile, scalable HMI in CPPS by utilizing the new 5G communication technology. The capabilities of the framework and of the new communication standard are demonstrated and evaluated for a use case, where a brain-computer interface (BCI) is used to control a robot arm. For better accuracy, the BCI is supported by an eye tracker and visual feedback is received via an augmented reality environment, and all devices are embedded via 5G communication. In particular, the influence of 5G communication on the system performance is examined, evaluated, and discussed. For this purpose, experiments are designed and conducted with different network configurations.