Social robot navigation through constrained optimization: A comprehensive study of uncertainty-based objectives and constraints in the simulated and real world

This paper provides an empirical evaluation, in both simulation and real scenarios, of the social navigation problem when considering human motion prediction and its stochastic effects. To this end, we study several different optimization criteria and constraints related to the uncertainty of predicting pedestrians’ motion, embedded into the Model Predictive Control (MPC) scheme.

The main research question of this work is the following: what are the most important uncertainty-based criteria for the social MPC both in simulated and real-world environments? In order to achieve a solid answer to this question, we extend the results previously obtained from our work (Akhtyamov et al., 2023) in the simulated environments and provide a real-world setting that mimics similar conditions, for a fair comparison of the qualitative and quantitative results.

The main conclusions supported by both of the evaluation environments are the advantages of using adaptive constraints as a clear undisputed enhancement and the problems raised when considering uncertainty-aware criteria. We hope this paper is of interest to the community for deciding and designing uncertainty-aware approaches for social robot navigation.