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With the substantial growth of logistics businesses the need for larger andmore automated warehouses increases, thus giving rise to fully robotizedshop-floors with mobile robots in charge of transporting and distributinggoods. However, even in fully automatized warehouse systems the need for humanintervention frequently arises, whether because of maintenance or because offulfilling specific orders, thus bringing mobile robots and humans ever closerin an integrated warehouse environment. In order to ensure smooth and efficientoperation of such a warehouse, paths of both robots and humans need to becarefully planned; however, due to the possibility of humans deviating from theassigned path, this becomes an even more challenging task. Given that, thesupervising system should be able to recognize human intentions and itsalternative paths in real-time. In this paper, we propose a framework for humandeviation detection and intention recognition which outputs the most probablepaths of the humans workers and the planner that acts accordingly by replanningfor robots to move out of the human's path. Experimental results demonstratethat the proposed framework increases total number of deliveries, especiallyhuman deliveries, and reduces human-robot encounters.

Human Intention Recognition for Human Aware Planning in Integrated Warehouse Systems