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.

翻译：本研究致力于探讨如何将人体运动预测的不确定性估计嵌入到约束优化技术（如模型预测控制）中，以实现社交机器人导航。我们提出了多种基于行人位置预测不确定性、与碰撞概率相关的代价目标和约束函数，这些函数可应用于模型预测控制，并在包含多个智能体的复杂场景中对所有不同变体进行了比较。本文试图回答的核心问题是：对于社交型模型预测控制而言，哪些基于不确定性的准则最为重要？为此，我们在可复现的合成环境中，针对不同复杂度的广泛场景，结合多种社交导航指标对提出的方法进行了评估。本研究的主要成果是为实践提供了一份基础指南，阐明在社交机器人导航中何时以及如何使用不确定性感知方法，以及哪些准则最为有效。