用数字孪生优化人机协作机器人布局，减少试错成本。
Optimizing Collaborative Robotics since Pre-Deployment via Cyber-Physical Systems' Digital Twins
- 通过数字孪生+贝叶斯优化，在部署前找到最优机器人布局。
- 实测表明能提升安全性、效率与环境适应性。
- 适合制造业部署前设计优化，降低原型试错成本。
人机协作不仅要求机器人在感知、推理和行动上革新，也需重构机器人工作单元的设计范式。本文提出一种基于数字孪生的协同机器人系统预部署优化框架，利用贝叶斯优化在若干轮迭代后寻找最优布局，克服经验驱动带来的次优设计问题。通过将生产关键绩效指标（KPI）融入黑箱优化框架，数字孪生支持数据驱动决策，减少昂贵原型需求，并因算法的持续学习特性实现系统动态优化。论文以案例研究展示该方法在构建更安全、高效、可适应的人机协作环境中的应用潜力。
原文摘要 · Abstract (English)
The collaboration between humans and robots re-quires a paradigm shift not only in robot perception, reasoning, and action, but also in the design of the robotic cell. This paper proposes an optimization framework for designing collaborative robotics cells using a digital twin during the pre-deployment phase. This approach mitigates the limitations of experience-based sub-optimal designs by means of Bayesian optimization to find the optimal layout after a certain number of iterations. By integrating production KPIs into a black-box optimization frame-work, the digital twin supports data-driven decision-making, reduces the need for costly prototypes, and ensures continuous improvement thanks to the learning nature of the algorithm. The paper presents a case study with preliminary results that show how this methodology can be applied to obtain safer, more efficient, and adaptable human-robot collaborative environments.
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