Ontology-Driven Robotic Specification Synthesis

TL;DR

This paper introduces RSTM2, an ontology-driven hierarchical methodology for synthesizing robotic specifications from high-level goals to formal models, supporting autonomous, resource-aware, and adaptive multi-robot systems.

Contribution

It presents a novel ontology-based approach using stochastic Petri nets for formal specification synthesis in complex robotic systems.

Findings

Supports architectural trade-offs and resource allocation under uncertainty.

Enables explainable AI assistants for autonomous specification synthesis.

Applicable to complex multi-robot missions like NASA CADRE.

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.