Advanced Air Mobility (AAM) is a growing field that demands a deep understanding of legal, spatial and temporal concepts in navigation. Hence, any implementation of AAM is forced to deal with the inherent uncertainties of human-inhabited spaces. Enabling growth and innovation requires the creation of a system for safe and robust mission design, i.e., the way we formalize intentions and decide their execution as trajectories for the Unmanned Aerial Vehicle (UAV). Although legal frameworks have emerged to govern urban air spaces, their full integration into the decision process of autonomous agents and operators remains an open task. In this work we present ProMis, a system architecture for probabilistic mission design. It links the data available from various static and dynamic data sources with legal text and operator requirements by following principles of formal verification and probabilistic modeling. Hereby, ProMis enables the combination of low-level perception and high-level rules in AAM to infer validity over the UAV's state-space. To this end, we employ Hybrid Probabilistic Logic Programs (HPLP) as a unifying, intermediate representation between perception and action-taking. Furthermore, we present methods to connect ProMis with crowd-sourced map data by generating HPLP atoms that represent spatial relations in a probabilistic fashion. Our claims of the utility and generality of ProMis are supported by experiments on a diverse set of scenarios and a discussion of the computational demands associated with probabilistic missions.

翻译：先进空中交通（AAM）是一个不断发展的领域，它要求对导航中的法律、空间和时间概念有深入的理解。因此，任何AAM的实施都必须处理人类居住空间固有的不确定性。为了促进增长和创新，需要建立一个安全、鲁棒的任务设计系统，即我们如何形式化意图并将其执行为无人机的轨迹。尽管已经出现了管理城市空域的法律框架，但将其完全整合到自主智能体和操作员的决策过程中仍然是一个开放的任务。在这项工作中，我们提出了ProMis，一个用于概率任务设计的系统架构。它遵循形式化验证和概率建模的原则，将来自各种静态和动态数据源的数据与法律文本和操作员要求联系起来。由此，ProMis使得AAM中的低层感知与高层规则能够结合，以推断无人机状态空间的有效性。为此，我们采用混合概率逻辑程序作为感知与行动之间的统一中间表示。此外，我们提出了将ProMis与众包地图数据连接的方法，通过生成以概率方式表示空间关系的HPLP原子来实现。我们通过在不同场景上的实验以及对概率任务相关计算需求的讨论，支持了关于ProMis实用性和通用性的主张。