Title: Bio-Inspired Energy Distribution for Programmable Matter

Abstract: The goal for programmable matter [7] is to realize physical materials that can dynamically change their physical properties on command, acting autonomously or based on user input. When energy considerations are required, programmable matter systems make use of an external energy source accessible by at least one module and rely on module-to-module power transfer to supply the system with energy. This external energy can be supplied directly to one or more modules in the form of electricity, or may be ambiently available as light, heat, sound, or chemical energy in the environment. Since energy may not be uniformly accessible to all modules in the system, a strategy for energy distribution — or sharing energy between modules such that all modules eventually obtain the energy they need to function — is imperative but does not come for free. Algorithmic theory for programmable matter has largely ignored the role of energy, focusing primarily on characterizing the minimal capabilities individual modules need to collectively achieve desired system-level self-organizing behaviors. In this work, we present a local algorithm for energy distribution under the amoebot model [2, 4] that is loosely inspired by the growth behavior of Bacillus subtilis bacterial biofilms [5, 6]. We assume that all particles in the system require energy to perform their actions, but only some have access to an external energy source. Naive distribution strategies such as fully selfish or fully altruistic behaviors have obvious problems: in the former, particles with access to energy use it all and starve the others, while in the latter no particle ever knows when it is safe to use its stored energy. This necessitates a strategy in which particles shift between selfish and altruistic energy usage depending on the needs of their neighbors. Our algorithm mimics the way bacteria use long-range communication of their metabolic stress to temporarily inhibit the biofilm’s energy consumption, allowing for nutrients to reach starving bacteria and effectively solving the energy distribution problem.