Generation of restricted zones for AGVs routes by clustering algorithms

Automated Guided Vehicles (AGVs) and autonomous robots share their workspace with humans and other manned industrial vehicles. This may not only cause unexpected stops and losses of performance but, still more important, compromise the safety of people and other vehicles. To prevent them from col-liding with people or things, it is possible to define restricted zones through which AGVs cannot circulate in any case. In this work, an architecture to update re-stricted areas of an AGV trajectory is designed. This safety system is based on machine learning techniques. Specifically, different clustering methods have been applied. The clusters are shaped as ellipses by a Gaussian mixture model distribution. Three clustering methods are compared regarding some metrics, such as wasted space and places non-covered by the forbidden zones. Results show how the best performance is obtained with the Gaussian method.