Abstract
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.
Highlights
Substantial growth of logistics business in recent years has generated the need for larger and more efficient warehouse systems
We propose an efficient warehouse worker intention estimation algorithm for safe flexible robotized warehouses motivated by the Bayesian Theory of Mind approach
The present paper draws upon our earlier work [45], where we have presented the preliminary version of the human intention estimation algorithm
Summary
Substantial growth of logistics business in recent years has generated the need for larger and more efficient warehouse systems. We propose an efficient warehouse worker intention estimation algorithm for safe flexible robotized warehouses motivated by the Bayesian Theory of Mind approach. Authors assert that such techniques operate offline and imply that at least one example of every possible motion pattern is contained in the learning data set which does not hold in practice They propose using growing hidden Markov models (GHMM) for predicting human motion, a problem which we consider dual to the human intention estimation in the warehouse domain. In the present paper we propose a worker intention estimation algorithm for safe flexible robotized algorithm that solves the aforementioned problem by creating first a generalized Voronoi diagram of the warehouse and running the D* algorithm in order to find the optimal path between each two nodes. The results corroborate that the proposed framework estimates warehouse worker’s desires precisely and in an intuitive manner
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