Real-Time Predictive Scheduling for Networked Robot Control Using Digital Twins and OpenRAN

Abstract

Safe and efficient real-time robotics control is highly delay-sensitive. Enabling such critical applications via mobile communication networks, therefore, hinges on reliably provisioning radio resources at low latency. Here, employing the Open Radio Access Network (O-RAN) concept, networks can be built with adaptive intelligent features, such as Artificial Intelligence (AI)-based scheduling policies for optimized resource management. By harnessing the innovative concept of distributed Applications (dApps) deployed inside the Open RAN Distributed Unit (O-DU), predictive resource allocation can reliably provide low latencies for robot control at increased spectral efficiency. This work demonstrates the Key Performance Indicators (KPIs) achieved with a proposed real-time proactive scheduling dApp employing AI methods. Results are derived from a real-world testbed that integrates predictive communication with a digital twin of the two-wheeled inverted pendulum robot evoBOT, designed for intralogistics. The closed-loop locomotion control, also providing upright stability control, is performed on the mobile edge via an evolved O-RAN system hosting the proposed dApp. Relative to optimized reactive network slicing, our approach yields a 34% mean reduction for uplink delays. Moreover, radio resource usage is reduced by up to 47% compared to highly optimized reactive scheduling, exhibiting similar control performance.