In this paper, we introduce a probabilistic approach to risk assessment of robot systems by focusing on the impact of uncertainties. While various approaches to identifying systematic hazards (e.g., bugs, design flaws, etc.) can be found in current literature, little attention has been devoted to evaluating risks in robot systems in a probabilistic manner. Existing methods rely on discrete notions for dangerous events and assume that the consequences of these can be described by simple logical operations. In this work, we consider measurement uncertainties as one main contributor to the evolvement of risks. Specifically, we study the impact of temporal and spatial uncertainties on the occurrence probability of dangerous failures, thereby deriving an approach for an uncertainty-aware risk assessment. Secondly, we introduce a method to improve the statistical significance of our results: While the rare occurrence of hazardous events makes it challenging to draw conclusions with reliable accuracy, we show that importance sampling -- a technique that successively generates samples in regions with sparse probability densities -- allows for overcoming this issue. We demonstrate the validity of our novel uncertainty-aware risk assessment method in three simulation scenarios from the domain of human-robot collaboration. Finally, we show how the results can be used to evaluate arbitrary safety limits of robot systems.

翻译：本文提出了一种概率化方法，聚焦于不确定性对机器人系统风险评估的影响。尽管现有文献中已有多种识别系统性危害（如程序缺陷、设计缺陷等）的方法，但鲜有研究以概率方式评估机器人系统风险。现有方法依赖于危险事件的离散化定义，并假设其后果可通过简单的逻辑运算描述。本研究将测量不确定性视为风险演变的主要因素之一，具体分析了时间与空间不确定性对危险失效发生概率的影响，从而推导出一种不确定性感知的风险评估方法。其次，我们提出了一种提升结果统计显著性的方法：尽管危险事件的罕见性导致难以获得可靠精度的结论，但研究表明重要性采样——一种在稀疏概率密度区域逐次生成样本的技术——可有效解决此问题。我们通过人机协作领域的三个仿真场景验证了新型不确定性感知风险评估方法的有效性。最后，展示了如何利用该评估结果判定机器人系统的任意安全限值。