Machine Learning and Computer Vision for the automation of processes in advanced logistics: the Integrated Logistic Platform (ILP) 4.0

Abstract

The growing complexity of the logistics chain, the need to take on an ever longer and distributed logistic chain, which in some cases even reaches the final stages of the production of the same goods, highlights the problem of managing the post-production phases of products and goods, but also of the personnel who work on these activities, charged to the companies that deal with logistics. We present in this paper the Integrated Logistic Platform (ILP 4.0), a software architectural model, whose purpose is to lead warehouse logistics to new levels of efficiency, not reachable as the sum of the operational functions within a single logistic company, but reachable only through a unique and cross-functional system, which surpasses the usual fragmented vision and consequent approaches, in favor a strategic coordination of all activities. In this paper it is described the general architecture of the realized platform, with its research contributions and innovations, in particular related to some typical problems of warehouse environments, solved with the help of machine learning and computer vision approaches, but also with the integration of augmented reality (AR) and virtual reality (VR) devices. The aim of the integration of these technologies in the project is the definition of a “smart” warehouse model environment, capable of mitigate problems of warehouse logistics such as: automation of the inventory process (through an UAV), control of warehouse movements, and management of logical / physical security of the premises. Based on the experiences made on the project, and focusing the attention on the safety and security aspects in logistics, that the platform covers, we discuss further a possible federated learning approach in order to mitigate the lack of data in order to solve some security and safety problems in the logistic area.