Intelligent Radio Resource Allocation for Human-Robot Collaboration

Abstract

The recent surge in human-controlled robotics and haptic devices research is expediting a paradigm shift in today’s communication networks towards human-to-robot (H2R) centric technologies that support Industrial Internet of Things (IIoT) and Industry 5.0. In both IIoT and Industry 5.0, human skills are extended through collaboration with robots that are geographically separated from the human. Depending on the dynamicity of the actual use case, human-to-robot communications necessitate low-latency networking. While Long Term Evolution (LTE) cellular technology has been successful in fulfilling the bandwidth demands of massively-connected sensors and devices of Industry 4.0, it is insufficient to meet the low latency demands of the future Industry 5.0 where dynamic interactions between humans and robots are paramount. In reducing the latency caused by radio resource contention in wireless H2R communications, in this work, we propose a novel approach that exploits an Attention-based Recurrent Neural Network (Att-RNN) to improve the Semi-Persistent Scheduling (SPS) resource allocation scheme adopted by LTE and new radio (NR) standards developed for the fifth generation (5G) mobile networks. We conduct a series of real haptic experiments to collect H2R traffic traces to train, test and evaluate the accuracy of Att-RNN in predicting H2R traffic. Then, with extensive simulations based on the empirical H2R traffic traces, we show that our proposed Att-RNN SPS scheme outperforms classic SPS and other existing resource allocation schemes in terms of reduced latency and improved resource allocation efficiency, thus making Att-RNN SPS a suitable candidate in future Industry 5.0 deployments.