This paper addresses robotic system engineering for safety- and mission-critical applications by bridging the gap between high-level objectives and formal, executable specifications. The proposed method, Robotic System Task to Model Transformation Methodology (RSTM2) is an ontology-driven, hierarchical approach using stochastic timed Petri nets with resources, enabling Monte Carlo simulations at mission, system, and subsystem levels. A hypothetical case study demonstrates how the RSTM2 method supports architectural trades, resource allocation, and performance analysis under uncertainty. Ontological concepts further enable explainable AI-based assistants, facilitating fully autonomous specification synthesis. The methodology offers particular benefits to complex multi-robot systems, such as the NASA CADRE mission, representing decentralized, resource-aware, and adaptive autonomous systems of the future.

翻译：本文通过弥合高层目标与形式化可执行规范之间的鸿沟，致力于面向安全关键与任务关键应用的机器人系统工程。所提出的方法——机器人系统任务到模型转换方法学（RSTM2）——是一种本体驱动的层次化方法，它采用带资源的随机时间Petri网，支持在任务、系统和子系统层面进行蒙特卡洛仿真。一个假设性案例研究展示了RSTM2方法如何支持架构权衡、资源分配以及不确定性下的性能分析。本体概念进一步赋能了可解释的基于人工智能的辅助工具，促进了完全自主的规范综合。该方法学对复杂的多机器人系统（如NASA CADRE任务）具有特殊优势，这类系统代表了未来去中心化、资源感知和自适应的自主系统。