Self-learning control systems for mobile machines
Project Status: active
Through the use of machine learning, an agricultural work process consisting of tractor and auxiliary equipment is to be optimized and automated. The self-learning system optimizes the processes with regard to defined target functions, for example performance (ha/h) and efficiency (l/ha). The system can be evaluated by measurements on the chassis dynamometer and field tests using the exemplary work processes "ploughing" and "rotary harrows". The system architecture is designed in such a way that the procedure can be transferred with specific adaptations to other mobile machines.
Funding: MOBIMA e.V.