Abstract
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.
Abstract (translated)
本文探讨了在安全性和任务关键应用中机器人系统工程的问题，通过连接高层次目标和形式化、可执行规范之间的差距来解决这一问题。提出的方法是“机器人系统任务到模型转换方法”（Robotic System Task to Model Transformation Methodology, 简称RSTM2），这是一种以本体论为驱动的分层方法，使用带有资源的随机定时Petri网，支持在任务、系统和子系统的各个层次上进行蒙特卡洛模拟。一个假设案例研究展示了RSTM2如何支持架构选择、资源配置以及不确定条件下的性能分析。通过运用本体论概念，还可以实现基于解释性人工智能的辅助工具，从而促进完全自主规范合成。该方法特别适用于复杂的多机器人系统，例如NASA的CADRE任务，它代表了未来去中心化、资源感知型和自适应自动系统的趋势。
URL
https://arxiv.org/abs/2602.05456
