  The paper proposes a framework to identify and avoid the coverage hole in an
indoor industry environment. We assume an edge cloud co-located controller that
followers the Automated Guided Vehicle (AGV) movement on a factory floor over a
wireless channel. The coverage holes are caused due to blockage, path-loss, and
fading effects. An AGV in the coverage hole may lose connectivity to the
edge-cloud and become unstable. To avoid connectivity loss, we proposed a
framework that identifies the position of coverage hole using a Support- Vector
Machine (SVM) classifier model and constructs a binary coverage hole map
incorporating the AGV trajectory re-planning to avoid the identified coverage
hole. The AGV's re-planned trajectory is optimized and selected to avoid
coverage hole the shortest coverage-hole-free trajectory. We further
investigated the look-ahead time's impact on the AGV's re-planned trajectory
performance. The results reveal that an AGV's re-planned trajectory can be
shorter and further optimized if the coverage hole position is known ahead of
time
