A probabilistic methodology for predicting injuries to human operators in automated production lines

Authors:
Ruslan Asaula;Daniele Fontanelli;Luigi Palopoli
Affiliations:
Department of Engineering and Information Science, University of Trento, Trento, TN, Italy;Department of Engineering and Information Science, University of Trento, Trento, TN, Italy;Department of Engineering and Information Science, University of Trento, Trento, TN, Italy
Venue:
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Year:
2009

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Abstract

Mobile robots are increasingly utilised in automated plants to the purpose of moving wares and material between the different production lines and logistic areas. In this context, the presence of human operators in the facility is frequently allowed to carry out or supervise some phases of the production. The problem arises of how to make the coexistence possible with controlled risks for the operator and without affecting the productivity with frequent interruptions. In this paper we propose a solution to this problem based on a probabilistic technique. A system of visual sensor (mounted on the mobile robots) detects the presence of a human operator and a discrete abstraction (essentially a discrete-time Markov chain) is used to predict his/her motion and hence find the probability of an accidental injury. For the computation of the latter, we combine the probability of having a collision with a given speed with the probability of receiving an injury out of the collision (taken from physiological models suggested by the automotive literature).