Stigmergic self-assembly of prespecified artificial structures in a constrained and continuous environment

The distributed control of self-assembly processes requires local behaviors that will cause initially unorganized components to form a desired goal structure. While important strides have been made in designing methods for self-assembling various geometric structures under idealized simulated conditions, many unaddressed issues remain in extending these methods to more complex environments. In this work, we discuss the self-assembly of prespecified 3D structures from blocks of different sizes. Block movements through a continuous environment are constrained by each other and simulated gravity, adding to the problem's difficulty. We present a solution that integrates three distinct techniques from the field of swarm intelligence: stigmergic pattern recognition, force-based movement control, and coordination via local message passing and state changes. Further, we empirically demonstrate that a stochastic component in the blocks' acceleration can aid in preventing persistent interference, and that the use of collective, flock-like movements can be beneficial in situations of low block availability. This work provides insight into the dynamics of continuous-space self-assembly, and is a step towards the design of methods for the automated "growth" of useful structures in real-world environments.