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Mobile robots become increasingly important for many industrial and logistics applications. Especially omnidirectional mobile robots are interesting due to their flexibility as they are able to move in any direction regardless of their orientation. In this context, we developed a robot platform for research on said types of robot. It turns out that this robot shows interesting behavior, which simple first principle models commonly used in the field fail to reproduce. Additionally, different robots of the same platform show different behavior although they should be identical, necessitating adaptive modeling techniques. As the sources of the mismatches are unknown, we opt for nonparametric Gaussian process regression to train a model of our robots based on carefully selected input-output data, which shows promising results. With an accurate prediction of the robot’s future position and orientation, it is possible to plan the trajectory of the robot several time steps ahead allowing for a good open-loop performance. This can be useful, e.g., if in industrial applications not the entire warehouse is covered with optical position feedback. We then proceed to develop an optimization-based ancillary controller, that drives the initially unknown system dynamics to the nominal one.
Eschmann et al. (Fri,) studied this question.