Collaborative embedded systems will have a strong impact on significant technological development and re-define current factory processes. Tomorrow’s factories will be able to flexibly react to changing production factors thanks to adaptive system architectures. Learning machines will take over the work of central order management systems and organise their transport order coordination autonomously. InSystems Automation is one of 22 international partners from industry and science who take part in the research project CrESt (Collaborative Embedded Systems). This government- and DLR-initiated endeavour is meant to run for three years and has a total budget of over 24 million euros.
Collaborative Embedded Systems – Machines link to become learning teams
What does CrESt cover?
The research project CrESt is divided in six co-called „Engineering Challenges“ and six interdisciplinary topics. The first three Engineering Challenges are concerned with the overarching question of how to best design the architecture of flexible, dynamic and adaptive systems.
The term flexible architecture refers to a factory’s ability to adapt to changes in production conditions (adaptable factory). How could, for example, a new machine with new features be integrated in a pre-existing row of machines in order to improve the whole factory’s output? In this scenario, it is assumed that the production process is put on hold while the re-fitting works take place.
What are collaborative embedded systems and what does InSystems research?
In the project aspect of dynamic architectures, it is, among others, InSystems’ task to investigate options to integrate a robot into a pre-existing fleet using simple mechanisms such as Plug & Play without the need to pause the whole production process. Once integrated, this robot or machine is then supposed to take all the information it needs from the overarching system and automatically receives its driving plans. In another project titled „adaptive systems“ , methods and technologies for decentralized order management are being developed.
Another one of CrESt’s topics is that of environmental examination, or contextualization. In InSystems’ case, this concerns the question of how one might optimize transport robots so that they are able to keep up their transport work even if their environment changes drastically. In factories, the location of goods placements, shelves or even machines may change frequently. If this is the case, transport robots must be enabled to quickly record these changes before any collisions or other errors occur.
Therefore it is the project’s goal to make it so that every individual robot is able to monitor its own battery status and calculate the cost a transport order would have for it. The decision of which robot should take on which transport order then no longer lies with a centralized fleet management system, but with the robots themselves who are meant to communicate among each other and determine which one of them is best suited to fulfill an order depending on the system goal.
Objectives of the research project
CrESt, as a research project funded by the Federal Ministry for Education and Research, is concerned with the development of collaborative embedded systems, with a focus on dynamic software systems. The project’s goal is to define methods for model-based descriptions of dynamic, scalable applications. The cases where such technology would be useful are many: autonomous robots, learning control systems and production machines are only a few examples. Such systems are meant to safely collaborate and take over repetitive work previously done by humans to optimize these processes in a goal-oriented manner. Based on the model-based system designs, analyses can be run to record all features a system will need and find application in InSystems Automation GmbH’s transport robots.