The ability of robots to understand human characteristics and make themselves socially accepted by humans are important issues if smooth collision avoidance between humans and robots is to be achieved. When discussing smooth collision avoidance, robot should understand not only physical components such as human position, but also social components such as body pose, face orientation and proxemics (personal space during motion). We integrated these components in a modified social force model (MSFM) which allows robots to predict human motion and perform smooth collision avoidance. In the modified model, short-term intended direction is described by body pose, and a supplementary force related face orientation is added for intention estimation. Face orientation is also the best indication of the direction of personal space during motion, which was verified in preliminary experiments. Our approach was implemented and tested on a real humanoid robot in a situation in which a human is confronted with the robot in an indoor environment. Experimental results showed that better human motion tracking was achieved with body pose and face orientation tracking. Being provided with the face orientation as an indication of the intended direction, and observing the laws of proxemics in a human-like manner, the robot was able to perform avoidance motions that were more human-like when compared to the original social force model (SFM) in a face-to-face confrontation.
An Integrated Pedestrian Behavior Model Based on Extended Decision Field Theory and Social Force Model
