Ontology-Driven Robotic Specification Synthesis
This work addresses robotic system engineering for safety- and mission-critical applications like NASA's CADRE mission, representing an incremental advancement in specification synthesis.
This paper tackles the challenge of translating high-level objectives into formal specifications for safety-critical robotic systems by introducing RSTM2, an ontology-driven methodology using stochastic timed Petri nets with resources that enables Monte Carlo simulations at multiple levels, demonstrated through a hypothetical case study for architectural trades and performance analysis.
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.