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Ontology-Driven Robotic Specification Synthesis

Maksym Figat, Ryan M. Mackey, Michel D. Ingham

TL;DR

The paper tackles the problem of translating high-level robotic objectives into formal, executable specifications in safety-critical, uncertain environments. It introduces RS(TM)^2, an ontology-driven, hierarchical methodology that employs stochastic timed Petri nets with resources to produce Mission/System/Subsystem-level specifications and Monte Carlo-based analyses, ultimately enabling automatic ROS 2 code generation. Key contributions include a formal bridge from objectives to executable, multi-level PN models, a ECS-inspired RSSM parameter flow, and a demonstration via a tower-building case that informs architectural decisions under uncertainty. The approach offers a practical pathway to design-time architectural exploration and robustness assessment, with potential integration of explainable AI assistants and alignment with 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.

Ontology-Driven Robotic Specification Synthesis

TL;DR

The paper tackles the problem of translating high-level robotic objectives into formal, executable specifications in safety-critical, uncertain environments. It introduces RS(TM)^2, an ontology-driven, hierarchical methodology that employs stochastic timed Petri nets with resources to produce Mission/System/Subsystem-level specifications and Monte Carlo-based analyses, ultimately enabling automatic ROS 2 code generation. Key contributions include a formal bridge from objectives to executable, multi-level PN models, a ECS-inspired RSSM parameter flow, and a demonstration via a tower-building case that informs architectural decisions under uncertainty. The approach offers a practical pathway to design-time architectural exploration and robustness assessment, with potential integration of explainable AI assistants and alignment with 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.
Paper Structure (7 sections, 9 figures, 3 tables)

This paper contains 7 sections, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. $\rm RS(TM)^2$ Overview
  3. $\rm RS(TM)^2$ Ontological Concepts
  4. System Specification Synthesis (3S)
  5. Case study: Tower Building System
  6. Experiments
  7. Conclusions

Figures (9)

  • Figure 1: (a) $\rm RS(TM)^2$ procedure; (b) basic RSSM structural concepts; (c) basic RSSM activity concepts.
  • Figure 2: Ontological concepts from different perspectives
  • Figure 3: Three 3S levels: Mission, System and Subsystem, each with a distinct specification perspective.
  • Figure 4: (a) Merged PNs at entity level; Standard arcs (arrows) transfer tokens; inhibitor arcs block transitions; (b) Action feasibility: preconditions, resources, and capabilities.
  • Figure 5: PNs show: (a-b) mission- and system-level coordination, and (c) capabilities from three entity views. Green dashed arcs mark resources consumed/produced during execution.
  • ...and 4 more figures