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

Social Robot Navigation through Constrained Optimization: a Comparative Study of Uncertainty-based Objectives and Constraints