HyMat

About the project

Logistics systems of the future face major challenges in terms of resilience, flexibility, efficiency and speed. The relevance of functioning logistics chains became apparent not least during the first wave of the coronavirus pandemic. In order to supply retailers with goods in line with demand, a complex supply chain is required, from the producer to the disposition of goods to the filling of shelves in stores. In order to meet the demands of flexibility and efficiency and to create a versatile system, autonomous mobile robots must work in cooperation with humans and intelligently network machines with each other through a hybrid material flow. To achieve this overall goal, a learning control system is being developed that orchestrates, optimizes and executes the interaction of stationary and mobile robots in collaboration with human workers and can react to dynamic changes. The central components of this project are stationary robot cells that use gripper solutions and machine learning to automate the picking process in the food retail sector and can thus serve the orders of individual stores. The central element is a higher-level learning control system to which the mobile resources are subordinate. In addition to the classic tasks of a control system, such as order scheduling and resource allocation, the control system is expanded to include monitoring and optimization based on real-time data. To increase resilience, new open standards such as VDA 5050 or omlox are used and further developed. The mobile resources, in the form of autonomous mobile robots or employees, physically represent the material flow of the robot cells, which are based on an existing picking system that has been expanded to include cognitive capabilities, and are part of the modular, networked overall system.