FoReCo: a forecast-based recovery mechanism for real-time remote control of robotic manipulators

TL;DR

FoReCo employs machine learning to predict and recover lost commands in wireless robotic control, significantly improving trajectory accuracy under interference in real-time applications.

Contribution

The paper introduces FoReCo, a novel forecast-based recovery mechanism that enhances real-time remote control of robotic manipulators over wireless links affected by interference.

Findings

Trajectory error reduced by 18 times in simulation

Trajectory error reduced by 2 times in experiments

FoReCo is lightweight and deployable on existing hardware

Abstract

Wireless communications represent a game changer for future manufacturing plants, enabling flexible production chains as machinery and other components are not restricted to a location by the rigid wired connections on the factory floor. However, the presence of electromagnetic interference in the wireless spectrum may result in packet loss and delay, making it a challenging environment to meet the extreme reliability requirements of industrial applications. In such conditions, achieving real-time remote control, either from the Edge or Cloud, becomes complex. In this paper, we investigate a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses Machine Learning (ML) to infer lost commands caused by interference in the wireless channel. FoReCo is evaluated through both simulation and experimentation in interference prone IEEE 802.11 wireless links, and using a commercial research robot that performs pick-and-place tasks. Results show that in case of interference, FoReCo trajectory error is decreased by x18 and x2 times in simulation and experimentation, and that FoReCo is sufficiently lightweight to be deployed in the hardware of already used in existing solutions.