From robotic lawnmowers to self-driving cars, autonomous robots are entering more and more parts of our lives. Many robotic systems such as driverless trains, vacuum cleaner robots, and high-tech welding robots in the automotive industry are already relatively commonplace. And the impact of future autonomous robots on our quality of life and the productivity of our economy is almost unimaginable.
Whether it is a flexible production robot in the factory of the future or an autonomous quadrotor delivering packages, what makes robots amazing and efficient is their intelligence. They have to react to changing environments, find their way around obstacles, and decide how to best perform their set task. At the same time, robots have only limited computational resources available and have to be energy efficient, especially if they are mobile and rely on batteries for power. These constraints require new, systematic approaches to designing decision systems for robots to make sure our future helpers are as intelligent and efficient as possible.