Simplification of Robotic System Model Analysis by Petri Net Meta-Model Property Transfer

2024-07-08

AI Summary

State-space explosion in robotic system modeling leads to high analysis complexity and prolonged design cycles. Method: This paper proposes a meta-model property migration approach based on Robot System Hierarchical Petri Nets (RSHPN). It abstracts key structural properties of the RSHPN meta-model and selectively migrates them to concrete system models, enabling localized verification of task-relevant submodules only. Global analysis is thus decoupled into independent subnet analyses, eliminating the need for full-model re-analysis during new system design. Contribution/Results: The work introduces, for the first time, a formal mechanism for migrating RSHPN meta-model properties and establishes a modular state-space reduction framework. Experimental evaluation demonstrates substantial reductions in design and analysis time, generation of formally rigorous, implementation-ready system specifications, and validates RSHPN’s effectiveness and practicality as a formal analytical framework for robotic systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

This paper presents a simplification of robotic system model analysis due to the transfer of Robotic System Hierarchical Petri Net (RSHPN) meta-model properties onto the model of a designed system. Key contributions include: 1) analysis of RSHPN meta-model properties; 2) decomposition of RSHPN analysis into analysis of individual Petri nets, thus the reduction of state space explosion; and 3) transfer of RSHPN meta-model properties onto the produced models, hence elimination of the need for full re-analysis of the RSHPN model when creating new robotic systems. Only task-dependent parts of the model need to be analysed. This approach streamlines the analysis thus reducing the design time. Moreover, it produces a specification which is a solid foundation for the implementation of the system. The obtained results highlight the potential of Petri nets as a valuable formal framework for analysing robotic system properties.

Problem

• Simplifying robotic system model analysis using Petri net meta-model properties
• Reducing state space explosion via decomposition of hierarchical Petri nets
• Eliminating full re-analysis needs for new robotic system designs

Innovation

• Transfer RSHPN meta-model properties to system models
• Decompose RSHPN analysis to reduce state space explosion
• Analyze only task-dependent parts for new robotic systems