Robot-human-learning for robotic picking processes

Abstract

Finding staff for carrying out logistic tasks is getting harder and harder for companies as a survey of Kohl and Pfretzschner (2017) showed. Combined with developments in engineering and Artificial Intelligence there is a trend to integrate machines into the execution of logistic tasks, either to support workers or to automate them completely (Schneider, et al. 2018). Different to tasks for transport or manufacturing standardization, it is more challeng ing in picking tasks because a high amount of flexibility is needed to com plete these tasks. This is the main reason why there is a low level of autom atization in picking processes of just 5% in warehouses, 15% are mecha nized and 80% are still run manually (Bonkenburg, 2016). Fully automated picking processes, besides fully automated storage, offer several ad vantages: saving of space and labor cost, availability of personnel instead of robots, savings on operational cost as heating or lighting (de Koster, 2018) and facing lack of personnel in logistics.