用知识图谱和随机时间佩特尼网，把机器人任务自动转为可仿真执行的规范。
Ontology-Driven Robotic Specification Synthesis
- 基于本体的分层方法，用带资源的随机时间佩特尼网建模
- 可在任务、系统、子系统层级进行蒙特卡洛仿真分析
- 适合复杂多机器人系统，支持自主规范生成与解释
本文针对安全与任务关键型机器人系统工程，弥合高层目标与形式化可执行规范之间的差距。提出的机器人系统任务到模型转换方法（RSTM2）是一种基于本体的分层方法，采用带资源的随机时间佩特尼网，支持在任务、系统及子系统层级进行蒙特卡洛仿真。一个假设性案例研究展示了该方法如何支持架构权衡、资源分配及不确定性下的性能分析。本体概念进一步使可解释的人工智能助手成为可能，实现完全自主的规范合成。该方法对复杂多机器人系统尤其有益，如美国宇航局CADRE任务所示，代表了未来去中心化、资源感知且自适应的自主系统。
原文摘要 · Abstract (English)
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.
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