Social interactive robot navigation based on human intention analysis from face orientation and human path prediction

Robot navigation in a human environment is challenging because human moves according to many factors such as social rules and the way other moves. By introducing a robot to a human environment, many situations are expected such as human want to interact with robot or humans expect robot to avoid collision. Robot navigation modeling have to take these factors into consideration. This paper presents a Social Navigation Model (SNM) as a unified navigation and interaction model that allows a robot to navigate in a human environment and response to human according to human intentions, in particular during a situation where the human encounters a robot and human wants to avoid, unavoid (maintain his/her course), or approach (interact) the robot. The proposed model is developed based on human motion and behavior (especially face orientation and overlapping personal space) analysis in preliminary experiments of human-human interaction. Avoiding, unavoiding, and approaching trajectories of humans are classified based on the face orientation and predicted path on a modified social force model. Our experimental evidence demonstrates that the robot is able to adapt its motion by preserving personal distance from passers-by, and interact with persons who want to interact with the robot with a success rate of 90 %. The simulation results show that robot navigated by proposed method can operate in populated environment and significantly reduced the average of overlapping area of personal space by 33.2 % and reduced average time human needs to arrive the goal by 15.7 % compared to original social force model. This work contributes to the future development of a human-robot socialization environment.