Hand layup is a commonly used process for making composite structures from several plies of carbon-fiber prepreg. The process involves multiple human operators manipulating and conforming layers of prepreg to a mold. The manual layup process is ergonomically challenging, tedious, and limits throughput. Moreover, different operators may perform the process differently, and hence introduce inconsistency. We have developed a multi-robot cell to automate the layup process. A human expert provides a sequence to conform to the ply and types of end-effectors to be used as input to the system. The system automatically generates trajectories for the robots that can achieve the specified layup. Using the cell requires the automated generation of different types of plans. This paper addresses two main planning problems: (a) generating plans to grasp and manipulate the ply and (b) generating feasible robot trajectories. We use a hybrid-physics based simulator coupled with a state space search to find grasp plans. The system employs a strategy that applies constraints successively in a non-linear optimization formulation to identify suitable placements of the robots around the mold so that feasible trajectories can be generated. Our system can generate plans in a computationally efficient manner, and it can handle a wide variety of complex parts. We demonstrate the automated layup by conducting physical experiments on an industry-inspired mold using the generated plans.

手工铺层是由几层碳纤维预浸料制成复合结构的常用方法。该过程涉及多个人工操作员，将预浸料的各层处理并使其适应模具。人工铺层过程在人机工程学上具有挑战性，繁琐且限制了吞吐量。此外，不同的操作员可能会不同地执行该过程，因此会导致不一致。我们已经开发了一个多机器人单元来自动完成铺叠过程。专家将提供一个序列，以符合帘布层和末端执行器的类型，以用作系统的输入。系统会自动为机器人生成可实现指定上层的轨迹。使用单元需要自动生成不同类型的计划。本文解决了两个主要的计划问题：（a）制定计划以掌握和操纵帘布层，以及（b）制定可行的机器人轨迹。我们使用基于混合物理的模拟器以及状态空间搜索来找到抓取计划。该系统采用一种策略，该策略在非线性优化公式中连续施加约束，以识别机器人在模具周围的合适位置，从而可以生成可行的轨迹。我们的系统可以以高效计算的方式生成计划，并且可以处理各种复杂的零件。我们通过使用生成的计划在行业启发的模具上进行物理实验来演示自动叠层。该系统采用一种策略，该策略在非线性优化公式中连续施加约束，以识别机器人在模具周围的合适位置，从而可以生成可行的轨迹。我们的系统可以以高效计算的方式生成计划，并且可以处理各种复杂的零件。我们通过使用生成的计划在行业启发的模具上进行物理实验来演示自动叠层。该系统采用一种策略，该策略在非线性优化公式中连续施加约束，以识别机器人在模具周围的合适位置，从而可以生成可行的轨迹。我们的系统可以以高效计算的方式生成计划，并且可以处理各种复杂的零件。我们通过使用生成的计划在行业启发的模具上进行物理实验来演示自动叠层。