I. Introduction
Safety is one of the most fundamental challenges in the operation of robots and autonomous systems in practical environments, which are uncertain and dynamic. In particular, the unpredicted motion of objects and agents often risks the collision-free navigation of mobile robots. To gather information about an obstacle’s uncertain movement, it is typical to use (historical) sample data of its motion. The main goal of this work is to develop an optimization-based method for risk-aware motion planning and control by incorporating data about moving obstacles into the robot’s decision-making in a distributionally robust manner.