用于细化网络物理系统中仿真证据影响知识的概念框架
A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems
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中文总结 AI 辅助
针对网络物理系统中环境介导交互未建模导致的仿真结果理解不足问题，提出基于“影响”概念的迭代细化仿真的概念框架，通过移动机器人案例验证了方法有效性。
中文摘要 AI 辅助
网络物理系统（CPS）通常由多个利益相关方开发，他们会生成适配自身专业领域的人工制品。这些系统的行为源于这些人工制品与其运行环境的交互。仿真与协同仿真已成为分析CPS行为的重要方法，开发者可通过仿真活动探索系统在变化条件下的响应，包括与环境的交互。然而，部分环境介导交互（通常是超出直接感知与执行范围的交互）因复杂性、时间不足或领域经验匮乏而未被建模，相关细节与认知的缺失阻碍了对仿真结果的正确理解与利用。为解决这些局限，我们提出一种概念框架，利用“影响”这一新颖概念支持仿真活动的迭代与增量细化，加深对系统行为的理解。我们通过一个使用Simulink/Gazebo协同仿真实现的移动机器人案例研究来演示所提方法。
英文摘要
Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise. The behaviour of these systems emerges from the interaction between those artefacts and their operational environment. Simulation and co-simulation have become essential approaches for analysing CPS behaviour and, through simulation campaigns, developers can explore system responses under changing conditions, including interactions with the environment. However, the lack of details and understanding of some environmentmediated interactions (typically the ones beyond direct sensing and actuation), which remain unmodelled due to their complexity, a lack of time, or a lack of domain experience, hinders the proper comprehension and exploitation of simulation results. To address these limitations, we propose a conceptual framework leveraging the novel concept of Influences to support the iterative and incremental refinement of simulation campaigns and deepen the understanding of the system behaviour. We demonstrate the proposed approach through a case study involving a mobile robot implemented using Simulink/Gazebo co-simulation.
发表机构
- Université Côte d’Azur（蔚蓝海岸大学）
- I3S/INRIA Kairos
机构由 AI 辅助整理，请以论文原文为准。
