Dynamic optimization of track sectioning in mechatronic systems for a hybrid planner

For railbound autonomous systems a hybrid planner chooses a path along track sections. The vehicle's propulsion uses a linear motor that needs trajectories for an air gap adjustment system. To model these, physical and fuzzy models as well as historical data of the air gap are used to predict the course of the air gap. The planner uses simulation to evaluate actions that have complete track sections as a granularity basis. The plan quality is improved by optimizing the choice of track sections after an analysis of the perceived and expected air gap gradient as that constitutes a major part of the system's overall performance.