Ontology-Driven Robotic Specification Synthesis

2026-02-05

AI Summary

This work proposes RSTM2, a novel approach to bridge the semantic gap between high-level mission objectives and executable formal specifications in safety- and mission-critical robotic systems. RSTM2 uniquely integrates ontology-driven hierarchical modeling with resource-aware stochastic timed Petri nets, enabling Monte Carlo simulation across task, system, and subsystem levels. The framework supports fully automated synthesis of formal specifications under interpretable AI guidance. It effectively facilitates architectural trade-off analysis, resource allocation, and decision-making under uncertainty for complex multi-robot systems. The method’s efficacy in performance evaluation and architecture optimization has been validated in decentralized, resource-constrained scenarios such as NASA’s CADRE mission.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• robotic specification synthesis
• safety-critical systems
• mission-critical applications
• multi-robot systems
• formal specifications

Innovation

• ontology-driven
• stochastic timed Petri nets
• specification synthesis
• Monte Carlo simulation
• multi-robot systems