  This work is dedicated to the study of how uncertainty estimation of the
human motion prediction can be embedded into constrained optimization
techniques, such as Model Predictive Control (MPC) for the social robot
navigation. We propose several cost objectives and constraint functions
obtained from the uncertainty of predicting pedestrian positions and related to
the probability of the collision that can be applied to the MPC, and all the
different variants are compared in challenging scenes with multiple agents. The
main question this paper tries to answer is: what are the most important
uncertainty-based criteria for social MPC? For that, we evaluate the proposed
approaches with several social navigation metrics in an extensive set of
scenarios of different complexity in reproducible synthetic environments. The
main outcome of our study is a foundation for a practical guide on when and how
to use uncertainty-aware approaches for social robot navigation in practice and
what are the most effective criteria.
