Traditional topology optimization techniques, such as density-based and level set methods, have proven successful in identifying potential design configurations for structures and mechanisms but suffer from rapidly increasing design space dimensionality and the possibility of converging to local minima. A heuristic alternative to these approaches couples a genetic algorithm with a Lindenmayer system (L-system), which encodes design variables and governs the development of the structure when coupled with an interpreter to translate genomic information into structural topologies. This work discusses the development of a graph-based interpretation scheme referred to as spatial interpretation for the development of reconfigurable structures (SPIDRS). This framework allows for the effective exploration of mechanism design spaces using a limited number of design variables. The theory and implementation of this method are detailed, and multiple case studies are presented to demonstrate the ability of SPIDRS to generate adaptive structures capable of achieving multiple design goals.

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