Robot navigation in uncertain environment via AI & Control

Nowadays there are plenty of scenarios, where autonomous robots meet humans or other robots, and where a safe supervision is required for machines with different levels of collaboration. For example, self-driving cars on the streets, mobile robots in modern automated factories or platoons of flying vehicles provide a general image of such scenarios. All of them have common characteristics dealing with presence of humans (or human controlled agents) together with autonomous robots, and also with uncertainty in the models describing other participants and in the goals they are pursuing, with highly varying dynamic environments and a need in harmless and precise coordination of all mobile robots. The goal of this PhD work is to design algorithms for robot navigation in a vastly uncertain conditions, with the presence of other robots and humans, which penalize the risks of an error and its cost. The complexity of the posed problem requires interdisciplinary approaches for its solution, as considered in this project, which belongs to an intersection of machine learning and automatic control domains. It is intended to combine the reinforcement learning and model predictive control approaches for realization of path-planning algorithms.