it’s OWL-RuMorS
- it's OWL-RuMorS
- 01.10.2012 - 30.09.2014
- Federal Ministry of Education and Research
- Innovation project in the leading-edge cluster it's OWL
- 1.2 million euros
- Prof. Dr. Wilhelm Nüßer
- Daniel Hintze, Sebastian Scholz
- CLAAS CSE GmbH, MS Agricultural Consulting (associated)
Intelligent machines and processes increase efficiency in agriculture
it’s OWL-RuMorS project: Modeling and runtime support for hybrid value creation in semi-autonomous and mobile agricultural machinery
In the technology network “Intelligent Technical Systems OstWestfalenLippe” (it’s OWL), companies, universities and research institutions are developing intelligent products and production systems. In collaboration with industry partner Claas Selbstfahrende Erntemaschinen GmbH, the FHDW is involved in the RuMorS (Runtime and Modeling Environment for mobile running Services) project.
The Leading-Edge Cluster project dealt with modeling and runtime support for hybrid value creation in semi-autonomous and mobile agricultural machinery. Approaches from previous FHDW projects were used. The question was: How can hybrid services be offered on complex machines using closely linked software and services? The assumption was that the highly integrated combination of different subsystems would make it possible to better exploit the potential of the machines and offer the user simpler and more efficient management.
Aim of the project: Creating a software environment that enables agricultural machinery manufacturers to offer hybrid service bundles
When it comes to agricultural machinery, it is particularly clear that the largest machines do not necessarily provide the best performance for their users: Size alone is no longer enough. Nowadays, the most important thing is that the machines do exactly what the user needs them to do. In agriculture in particular, however, it is almost impossible to determine all of these requirements in advance: the conditions on farms are too varied and the natural conditions, such as weather, soil conditions, etc., are too variable and difficult to plan.
In order to provide users with the best possible result, the agricultural machine must therefore adapt to the respective situation largely independently and intelligently. This adaptation requires flexibility, which today can be achieved primarily through software. Only the machine and the software that adapts to the new situation form a unit, a “hybrid performance bundle” that delivers optimum performance for the user.
The aim of the project is therefore to create a software environment that will enable manufacturers of agricultural machinery – but also other sectors later on – to efficiently create such hybrid service bundles and offer different service bundles. Users of the machines should also be able to select and execute the service bundles that suit their needs and situation. This approach takes the familiar idea of marketplaces for smartphone apps much further, as the machines and therefore also the applications are much more complex and the dependency on the specific situation is much more pronounced. It breaks new ground in terms of methodology and technology.
On the basis of such a development and execution environment, the machines not only perform better and thus increase customer satisfaction. New sales opportunities arise for manufacturers through the sale of technically coordinated and integrated service packages. The considerable technical know-how of German companies in particular can thus be used economically in a direct way. In addition, the close link between product and service can protect against product piracy.
RuMorS architecture: The aim is to achieve more efficient and more powerful production processes in agriculture. The solution approach of the RuMorS project is to link services, software (apps) and machines, which are essentially available as separate products on the market today, more closely together. In practical terms, this means, for example, that the farmer can centrally control and monitor all harvesting processes on different fields with the respective harvesting and utility vehicles involved. At the same time, services such as precise, location-based weather forecasts can be automatically integrated into the process flow.
The FHDW and CLAAS deliberately pursued a complementary path to the majority of other research approaches. These primarily investigated how the machine itself could be optimized and thus made more intelligent – a challenge in terms of mechanical and electrical engineering. The RuMorS project, on the other hand, regarded the machine as a given and attempted to achieve greater machine efficiency through supplementary software, which, among other things, should also enable better process integration of the machine. This approach was justified not least by the fact that the typical development times for agricultural machinery are several years, while smaller software modules that implement a customer request can often be used productively in just a few months.
In cooperation with CLAAS, concrete results were developed on the architecture of the new system, on simulation methods and on fundamental questions of possible use. The findings were published in recognized international journals.
The first transfer phase of the Leading-Edge Cluster began at the end of 2013. As part of small, focused transfer projects, innovative ideas, processes and results from previous Leading-Edge Cluster projects were also made available to other, often smaller, companies. The results that the FHDW was able to develop in the RuMorS innovation project were made available to a start-up company in the region as part of such a transfer project – topocare GmbH from Gütersloh, which manufactures and sells machines and systems for hydraulic and civil engineering. The collaboration with topocare and a Fraunhofer Institute as a further project partner enabled the FHDW to sustainably transfer the innovative findings into business practice.
Publication
Th. Steckel, Th. Kersting, W. Nüßer, Towards Supporting Mobile Business Processes in Non-Deterministic Agricultural Environments by using Agent-based Technologies, KI – Künstliche Intelligenz 27 (4), pp. 359 – 362, July 2013.
