SUMMARY

This work addresses developing optimal assembly sequences for modular building blocks. The underlying concept behind this work is an automated device that could take a virtual shape such as a CAD file, and automatically decide how to physically build the shape using simple, identical building blocks. This entails selecting where to place blocks inside the shape and generating an efficient assembly sequence that a robot could use to build the shape. This work uses blocks that are defined in a general manner such that the model can be applied to a variety of scenarios in the future. The primary focus of this work is the development of methods for generating assembly sequences in a time-feasible manner that ensure static stability at each step of the assembly. Most existing research focuses on complete enumeration of every possible assembly sequence and evaluation of many possible sequences. This is not practical for systems with a large number of parts for two reasons: the number of possible assembly sequences is exponential in the number of parts, and each static stability test is very time-consuming. The approach proposed here is to develop a multi-hierarchical rule-based approach to assembly sequences. This is accomplished by formalizing and justifying both high-level and mid-level assembly rules based on static considerations. Application of these rules helps develop assembly sequences rapidly. The assembly sequence is developed in a time-feasible manner according to the geometry of the structure, rather than evaluating statics along the way. This work only evaluates the static stability of each step of the assembly once. This work analyzes the behavior of the various rules both numerically and by theory, and develops guidelines suggesting which rules to apply. A secondary focus of this work is to introduce methods by which the inside of the structure can be optimized. This multi-objective structure optimization research is implemented by genetic algorithms.