Michael Arndt

• When designing autonomous mobile robotic systems, there usually is a trade-off between the three opposing goals of safety, low-cost and performance. If one of these design goals is approached further, it usually leads to a recession of one or even both of the other goals. If for example the performance of a mobile robot is increased by making use of higher vehicle speeds, then the safety of the system is usually decreased, as, under the same circumstances, faster robots are often also more dangerous robots. This decrease of safety can be mitigated by installing better sensors on the robot, which ensure the safety of the system, even at high speeds. However, this solution is accompanied by an increase of system cost. In parallel to mobile robotics, there is a growing amount of ambient and aware technology installations in today's environments - no matter whether in private homes, offices or factory environments. Part of this technology are sensors that are suitable to assess the state of an environment. For example, motion detectors that are used to automate lighting can be used to detect the presence of people. This work constitutes a meeting point between the two fields of robotics and aware environment research. It shows how data from aware environments can be used to approach the abovementioned goal of establishing safe, performant and additionally low-cost robotic systems. Sensor data from aware technology, which is often unreliable due to its low-cost nature, is fed to probabilistic methods for estimating the environment's state. Together with models, these methods cope with the uncertainty and unreliability associated with the sensor data, gathered from an aware environment. The estimated state includes positions of people in the environment and is used as an input to the local and global path planners of a mobile robot, enabling safe, cost-efficient and performant mobile robot navigation during local obstacle avoidance as well as on a global scale, when planning paths between different locations. The probabilistic algorithms enable graceful degradation of the whole system. Even if, in the extreme case, all aware technology fails, the robots will continue to operate, by sacrificing performance while maintaining safety. All the presented methods of this work have been validated using simulation experiments as well as using experiments with real hardware.