  With the substantial growth of logistics businesses the need for larger
warehouses and their automation arises, thus using robots as assistants to
human workers is becoming a priority. In order to operate efficiently and
safely, robot assistants or the supervising system should recognize human
intentions in real-time. Theory of mind (ToM) is an intuitive human conception
of other humans' mental state, i.e., beliefs and desires, and how they cause
behavior. In this paper we propose a ToM based human intention estimation
algorithm for flexible robotized warehouses. We observe human's, i.e., worker's
motion and validate it with respect to the goal locations using generalized
Voronoi diagram based path planning. These observations are then processed by
the proposed hidden Markov model framework which estimates worker intentions in
an online manner, capable of handling changing environments. To test the
proposed intention estimation we ran experiments in a real-world laboratory
warehouse with a worker wearing Microsoft Hololens augmented reality glasses.
Furthermore, in order to demonstrate the scalability of the approach to larger
warehouses, we propose to use virtual reality digital warehouse twins in order
to realistically simulate worker behavior. We conducted intention estimation
experiments in the larger warehouse digital twin with up to 24 running robots.
We demonstrate that the proposed framework estimates warehouse worker
intentions precisely and in the end we discuss the experimental results.
