用预测控制提升移动打印机器人安全与精度
A Model Predictive Control Framework to Enhance Safety and Quality in Mobile Additive Manufacturing Systems
- 基于模型预测控制实现动态环境下的路径与打印协同优化
- 三组案例验证系统在复杂环境下保持高质量打印的能力
- 适合需要现场定制化生产且对精度有要求的制造场景
近年来，制造业对定制化、按需生产的需求不断增长。增材制造（AM）作为一种具有高度灵活性、缩短生产周期和高效利用材料的先进技术，正日益受到关注。然而，传统AM系统受限于固定布局和人工依赖，导致生产周期长、扩展性差。移动机器人可通过在动态环境中运送工件，提升生产系统的灵活性。将AM系统与移动机器人结合，可优化准备任务和分布式打印的移动时间。尽管移动AM机器人已在大型结构现场制造中应用，但常忽视表面粗糙度等关键打印质量指标，且难以满足小型精密部件的高精度要求。本文提出一种面向移动AM平台的模型预测控制框架，确保在工厂内安全导航的同时，在动态环境中维持高质量打印。通过三个案例研究验证了该系统的可行性和可靠性。
原文摘要 · Abstract (English)
In recent years, the demand for customized, on-demand production has grown in the manufacturing sector. Additive Manufacturing (AM) has emerged as a promising technology to enhance customization capabilities, enabling greater flexibility, reduced lead times, and more efficient material usage. However, traditional AM systems remain constrained by static setups and human worker dependencies, resulting in long lead times and limited scalability. Mobile robots can improve the flexibility of production systems by transporting products to designated locations in a dynamic environment. By integrating AM systems with mobile robots, manufacturers can optimize travel time for preparatory tasks and distributed printing operations. Mobile AM robots have been deployed for on-site production of large-scale structures, but often neglect critical print quality metrics like surface roughness. Additionally, these systems do not have the precision necessary for producing small, intricate components. We propose a model predictive control framework for a mobile AM platform that ensures safe navigation on the plant floor while maintaining high print quality in a dynamic environment. Three case studies are used to test the feasibility and reliability of the proposed systems.
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