Generating complex assembly sequences from 3D CAD models considering insertion relations

To reduce human intervention in the process of manually teaching assembly sequences to industrial robots, we developed an automated sequence planning method using only a 3D CAD model. In the proposed method, first, to find an assembly sequence for many parts, a genetic algorithm generates the initial chromosome based on interference, rather than merely generating a randomized initial chromosome. Second, to generate an assembly sequence satisfying the preferred insertion condition (e.g. male–female parts), we calculate an insertion matrix indicating the insertion relations between the parts. In this paper, the preferred insertion condition is defined as requiring a part with holes to be prepared first, and then other parts are inserted into the holes. In our experiments on assembly sequence generation both with and without the proposed methods, the proposed method was able to generate an assembly sequence for a product composed of 32 parts. The proposed method generated a feasible sequence every time (30 successes in 30 trials) and a preferable sequence, such as fixing by a screw after assembling the fixed part. These results demonstrate the usefulness of the proposed chromosome initialization method and the use of insertion matrices.

为减少人工示教工业机器人装配序列（assembly sequence）过程中的人工干预，我们开发了一种仅基于三维计算机辅助设计（3D CAD）模型的自动化装配序列规划方法。在所提出的方法中，首先，为求解多零件装配序列问题，遗传算法（Genetic Algorithm）将基于零件干涉情况生成初始染色体（chromosome），而非仅生成随机初始染色体；其次，为生成满足优先插入条件（例如公-母零件配合）的装配序列，我们将计算用于表征零件间插入配合关系的插入矩阵（insertion matrix）。本文将优先插入条件定义为：需先装配带孔零件，再将其余零件插入该孔内。我们分别开展了采用所提方法与未采用所提方法的装配序列生成实验，实验结果表明，所提方法可生成由32个零件组成的产品的装配序列，且每次均可生成可行装配序列（30次试验全部成功），同时能生成符合优先装配逻辑的优化序列，例如在完成固定件装配后再通过螺钉进行紧固。上述结果证实了所提出的染色体初始化方法与插入矩阵应用方案的有效性。