  Mobile robots are increasingly being used in noisy environments for social
purposes, e.g. to provide support in healthcare or public spaces. Since these
robots also operate beyond human sight, the question arises as to how different
robot types, ambient noise or cognitive engagement impacts the detection of the
robots by their sound. To address this research gap, we conducted a user study
measuring auditory detection distances for a wheeled (Turtlebot 2i) and
quadruped robot (Unitree Go 1), which emit different consequential sounds when
moving. Additionally, we also manipulated background noise levels and
participants' engagement in a secondary task during the study. Our results
showed that the quadruped robot sound was detected significantly better (i.e.,
at a larger distance) than the wheeled one, which demonstrates that the
movement mechanism has a meaningful impact on the auditory detectability. The
detectability for both robots diminished significantly as background noise
increased. But even in high background noise, participants detected the
quadruped robot at a significantly larger distance. The engagement in a
secondary task had hardly any impact. In essence, these findings highlight the
critical role of distinguishing auditory characteristics of different robots to
improve the smooth human-centered navigation of mobile robots in noisy
environments.
