  Mobile robots have become more and more popular in large-scale and crowded
environments, such as airports, shopping malls, etc. However, due to sparse
landmarks and crowd noise, localization in this environment is a great
challenge. Furthermore, it is unreliable for the robot to navigate safely in
crowds while considering human comfort. Thus, how to navigate safely with
localization precision in that environment is a critical problem. To solve this
problem, we proposed a curiosity-based framework that can find an effective
path with the consideration of human comfort and crowds, localization
uncertainty, and the cost-to-go to the target. Three parts are involved in the
proposed framework: the distance assessment module, the Curiosity for Positive
Content (CPC), namely information-rich areas, and the Curiosity for Negative
Content (CNC), namely crowded areas. CPC is introduced when the real-time
localization uncertainty evaluation is not satisfied. This factor is predicted
through the propagation of uncertainty along the candidate trajectory to
provoke the robot to approach localization-referenced landmarks. The Human
Comfort and Crowd Density Map (HCCDM) based on the Gaussian Mixture Model (GMM)
is established to calculate CNC, which drives the robot to bypass the crowd and
consider human comfort. The evaluation is conducted in a series of large-scale
and crowded environments. The results show that our method can find a feasible
path that can consider the localization uncertainty while simultaneously
avoiding the crowded area.
