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

Human Intention Estimation based on Hidden Markov Model Motion Validation for Safe Flexible Robotized Warehouses