Improving assembly precedence constraint generation by utilizing motion planning and part interaction clusters

In this paper, we present a technique that combines motion planning and part interaction clusters to improve generation of assembly precedence constraints. In particular, this technique automatically finds, and clusters, parts that can mutually affect each other's accessibility, and hence may impose assembly constraints. This enables the generation of accurate precedence constraints without needing to examine all possible assembly sequences. Given an assembly model, our technique generates potential disassembly layers: spatial clustering is used to generate part sets. Next, motion planning based on rapidly-exploring random trees (RRT) with multiple trees is used to evaluate the interaction between these part sets. Specifically, motion planning is used to determine which part sets can be removed from the assembly. These sets are added to the first disassembly layer and removed from the assembly. Part sets that can be removed from the simplified assembly are then added to the second layer. If the process gets stuck, parts in the parent set are regrouped, and the process continues until all disassembly layers are found. The resulting structure reveals precedence relationships among part sets, which can be used to generate feasible assembly sequences for each part set and the whole assembly. We present theoretical results related to the algorithms developed in the paper. Computational results from tests on a variety of assemblies are presented to illustrate our approach.