Artificial Intelligence (AI) is one of the most auspicious technologies in the mobile machine domain. It promises to optimize the machine operation to re-duce energy consumption or provide an assistant function to support the opera-tor in challenging machine movements. A large amount of machine data is re-quired to train and build AI models. These data sets are often not available due to missing or faulty sensors in the machine. However, construction machines are partly equipped with temporary sensors for data collection so that small data sets are available. Nevertheless, these data sets are very small and must be ex-tended with more realistic data. Generating synthetic data to enrich real data is a promising approach to overcome the obstacle of small data. This paper presents a data generator to produce synthetic, physically-informed data for the pendu-lum trajectory of a flexible attachment tool on a construction machine. The data generator calculates a reference trajectory based on a physical model of the ma-chine. This reference trajectory is generated by solving an optimization problem to cover the machine movement that an experienced machine operator would drive. Reasonable deviations of these trajectories are generated by varying ma-chine characteristics and adding external forces to the physical model to simu-late rough environmental conditions. The data generator is implemented for the grab system movement of a civil engineering machine.     «

Artificial Intelligence (AI) is one of the most auspicious technologies in the mobile machine domain. It promises to optimize the machine operation to re-duce energy consumption or provide an assistant function to support the opera-tor in challenging machine movements. A large amount of machine data is re-quired to train and build AI models. These data sets are often not available due to missing or faulty sensors in the machine. However, construction machines are partly equipped with temporary s...     »