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中，例如用于社交机器人导航的模型预测控制。我们提出了若干从行人位置预测不确定性中获取的成本目标与约束函数，这些函数与碰撞概率相关，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。本文试图回答的主要问题是：哪些基于不确定性的标准对社交模型预测控制最为重要？为此，我们在可重现的合成环境中，针对一系列不同复杂度的场景，使用多种社交导航指标对所提出的方法进行了评估。本研究的主要成果是为实际应用提供了一份实践指南的基础，阐明了在社交机器人导航中何时及如何使用不确定性感知方法，以及哪些是最有效的标准。