  When mobile robots maneuver near people, they run the risk of rudely blocking
their paths; but not all people behave the same around robots. People that have
not noticed the robot are the most difficult to predict. This paper
investigates how mobile robots can generate acceptable paths in dynamic
environments by predicting human behavior. Here, human behavior may include
both physical and mental behavior, we focus on the latter. We introduce a
simple safe interaction model: when a human seems unaware of the robot, it
should avoid going too close. In this study, people around robots are detected
and tracked using sensor fusion and filtering techniques. To handle
uncertainties in the dynamic environment, a Partially-Observable Markov
Decision Process Model (POMDP) is used to formulate a navigation planning
problem in the shared environment. People's awareness of robots is inferred and
included as a state and reward model in the POMDP. The proposed planner enables
a robot to change its navigation plan based on its perception of each person's
robot-awareness. As far as we can tell, this is a new capability. We conduct
simulation and experiments using the Toyota Human Support Robot (HSR) to
validate our approach. We demonstrate that the proposed framework is capable of
running in real-time.
