Comfortable Crossing Strategies for Robots

Abstract

Increasingly often robots are deployed in human environments, where they will encounter people. An example of a challenge robots encounter is crossing paths with a human. Based on human-robot proxemics research one would expect that people would keep a certain distance to maintain an appropriate comfort level. However it is unclear whether this also holds for crossing scenarios between a robot and a person. In the first experiment presented in this paper, a humanoid robot crossed paths with a person in which the crossing angle and acceleration of the robot were manipulated. Results showed that participants deviated more from a straight path when the robot arrived earlier at the crossing point compared to the other trials and when it accelerated or when the robot itself deviated from a straight path. If participants had to deviate from their path, it was regarded as less comfortable and it required more effort. In the second experiment, an autonomous guided vehicle was used, and we tested the moving speed of the robot. Similar to the first experiment, when the robot kept a straight path or stopped, it was regarded as the most comfortable. The results show that it is more comfortable if a robot does not change its direction while crossing paths with the robot. These findings indicate that perceived comfort is not merely determined by distance, but is more strongly affected by how predictable the robot is.