It is a common goal to improve safety and performance of mobile indoor robots by predicting the movements of people in the surroundings. In contrast to many related works which exclusively employ sensors mounted on mobile robots, this work shows a method to achieve this goal in a smart environment where external sensors are used to sense people's positions. By using probabilistic models and filters, the evolution of the environment's state is predicted and optimal paths with respect to safety and performance are planned. Experiments in reality and in a simulation environment show the applicability in real-world scenarios and the advantages over classical path planning approaches.

This work describes the combination of three software frameworks from two different domains: robotics and smart environments. The two robotic frameworks MCA2 and SimVis-3D that have been in use for several years on a multitude of different robotic systems and TinySEP, a modular framework for smart environments were combined to create a win-win-situation for both roboticists and ubiquitous computing researchers. The possibilities and advantages this combination can offer are discussed, especially in situations where mobile robots and smart environments coexist next to each other. This work is concluded by an experiment that shows the feasibility and the strengths of the proposed approach.