通过自动优化铺放计划，提升机器人复合材料铺放的效率与鲁棒性。
Automated Plan Refinement for Improving Efficiency of Robotic Layup of Composite Sheets
- 结合专家经验与数据驱动方法，动态调整铺放路径。
- 纠正路径减少超70%，时间效率显著提升。
- 适合需要高稳定性复合材料自动化生产的场景。
复合材料铺放的自动化对满足各行业日益增长的需求至关重要。然而，机器人铺放的铺放计划缺乏鲁棒性：在某一工况下有效的计划，在另一工况下可能表现不佳。材料属性或工作环境的变化会导致铺放计划性能下降。本文提出一个综合框架，基于执行表现自动优化铺放计划。该框架以最小化未压实区域为优先目标，同时提升时间效率。通过融合人类专家知识与数据驱动决策，对不同生产环境下的专家初始计划进行改进。实验验证了该方法的有效性，结果显示相比初始专家计划，所需修正路径数量显著减少。结合实证数据分析、动作有效性建模与基于搜索的优化，系统实现了更优的时间效率。实验结果证明该方法能有效优化铺放流程，推动复合材料制造自动化的技术进步。
原文摘要 · Abstract (English)
The automation of composite sheet layup is essential to meet the increasing demand for composite materials in various industries. However, draping plans for the robotic layup of composite sheets are not robust. A plan that works well under a certain condition does not work well in a different condition. Changes in operating conditions due to either changes in material properties or working environment may lead a draping plan to exhibit suboptimal performance. In this paper, we present a comprehensive framework aimed at refining plans based on the observed execution performance. Our framework prioritizes the minimization of uncompacted regions while simultaneously improving time efficiency. To achieve this, we integrate human expertise with data-driven decision-making to refine expert-crafted plans for diverse production environments. We conduct experiments to validate the effectiveness of our approach, revealing significant reductions in the number of corrective paths required compared to initial expert-crafted plans. Through a combination of empirical data analysis, action-effectiveness modeling, and search-based refinement, our system achieves superior time efficiency in robotic layup. Experimental results demonstrate the efficacy of our approach in optimizing the layup process, thereby advancing the state-of-the-art in composite manufacturing automation.
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