Modeling Self-Assembly Across Scales: The Unifying Perspective of Smart Minimal Particles
A wealth of current research in microengineering aims at fabricating devices of increasing complexity, notably by (self-)assembling elementary components into heterogeneous functional systems. At the same time, a large body of robotic research called swarm robotics is concerned with the design and the control of large ensembles of robots of decreasing size and complexity. This paper describes the asymptotic convergence of micro/nano electromechanical systems (M/NEMS) on one side, and swarm robotic systems on the other, toward a unifying class of systems, which we denote Smart Minimal Particles (SMPs). We deﬁne SMPs as mobile, purely reactive and physically embodied agents that compensate for their limited on-board capabilities using speciﬁcally engineered reactivity to external physical stimuli, including local energy and information scavenging. In trading off internal resources for simplicity and robustness, SMPs are still able to collectively perform non-trivial, spatio-temporally coordinated and highly scalable operations such as aggregation and self-assembly (SA). We outline the opposite converging tendencies, namely M/NEMS smarting and robotic minimalism, by reviewing each field’s literature with speciﬁc focus on self-assembling systems. Our main claim is that the SMPs can be used to develop a unifying technological and methodological framework that bridges the gap between passive M/NEMS and active, centimeter-sized robots. By proposing this unifying perspective, we hypothesize a continuum in both complexity and length scale between these two extremes. We illustrate the beneﬁts of possible cross-fertilizations among these originally separate domains, with speciﬁc emphasis on the modeling of collective dynamics. Particularly, we argue that while most of the theoretical studies on M/NEMS SA dynamics belong so far to one of only two main frameworks—based on analytical master equations and on numerical agent-based simulations, respectively—alternative models developed in swarm robotics could be amenable to the task, and thereby provide important novel insights.