  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.
