Long distance prediction and short distance control in human-robot systems

The study of the interaction between autonomous robots and human agents in common working areas is an emerging field of research. Main points thereby are human safety, system stability, performance and optimality of the whole interaction process. Two approaches to deal with human-robot interaction can be distinguished: Long distance prediction which requires the recognition of intentions of other agents, and short distance control which deals with actions and reactions between agents and mutual reactive control of their motions and behaviors. In this context obstacle avoidance plays a prominent role. In this paper long distance prediction is represented by the identification of human intentions to use specific lanes by using fuzzy time clustering of pedestrian tracks. Another issue is the extrapolation of parts of both human and robot trajectories in the presence of scattered/uncertain measurements to guarantee a collision-free robot motion. Short distance control is represented by obstacle avoidance between agents using the method of velocity obstacles and both analytical and fuzzy control methods.