  With the substantial growth of logistics businesses the need for larger and
more automated warehouses increases, thus giving rise to fully robotized
shop-floors with mobile robots in charge of transporting and distributing
goods. However, even in fully automatized warehouse systems the need for human
intervention frequently arises, whether because of maintenance or because of
fulfilling specific orders, thus bringing mobile robots and humans ever closer
in an integrated warehouse environment. In order to ensure smooth and efficient
operation of such a warehouse, paths of both robots and humans need to be
carefully planned; however, due to the possibility of humans deviating from the
assigned path, this becomes an even more challenging task. Given that, the
supervising system should be able to recognize human intentions and its
alternative paths in real-time. In this paper, we propose a framework for human
deviation detection and intention recognition which outputs the most probable
paths of the humans workers and the planner that acts accordingly by replanning
for robots to move out of the human's path. Experimental results demonstrate
that the proposed framework increases total number of deliveries, especially
human deliveries, and reduces human-robot encounters.
