Toward Task Capable Active Matter: Learning to Avoid Clogging in Confined Collectives via Collisions

Abstract

Social organisms which construct nests consisting of tunnels and chambers necessarily navigate confined and crowded conditions. Unlike low density collectives like bird flocks and insect swarms in which hydrodynamic and statistical phenomena dominate, the physics of glasses and supercooled fluids is important to understand clogging behaviors in high density collectives. Our previous work revealed that fire ants flowing in confined tunnels utilize diverse behaviors like unequal workload distributions, spontaneous direction reversals and limited interaction times to mitigate clogging and jamming and thus maintain functional flow; implementation of similar rules in a small robophysical swarm led to high performance through spontaneous dissolution of clogs and clusters. However, how the insects learn such behaviors and how we can develop “task capable” active matter in such regimes remains a challenge in part because interaction dynamics are dominated by local, potentially time-consuming collisions and no single agent can survey and guide the entire collective. Here, hypothesizing that effective flow and clog mitigation could be generated purely by collisional learning dynamics, we challenged small groups of robots to transport pellets through a narrow tunnel, and allowed them to modify their excavation probabilities over time. Robots began excavation with equal probabilities to excavate and without probability modification, clogs and clusters were common. Allowing the robots to perform a “reversal” and exit the tunnel when they encountered another robot which prevented forward progress improved performance. When robots were allowed to change their reversal probabilities via both a collision and a self-measured (and noisy) estimate of tunnel length, unequal workload distributions comparable to our previous work emerged and excavation performance improved. Our robophysical study of an excavating swarm shows that despite the seeming complexity and difficulty of the task, simple learning rules can mitigate or leverage unavoidable features in task capable dense active matter, leading to hypotheses for dense biological and robotic swarms.

Agitated ants: regulation and self-organization of incipient nest excavation via collisional cues

“…In this work, we introduced a resting mechanism employed homogeneously by all simulated ants, which results in a nearly equal workload distribution (data not shown). This is in contrast with previous findings of unequal participation in work and the potential rules leading up to it [51,57]. Our focus in this work was on emergent global regulation, rather than emergent workload distributions.…”

“…In corresponding CA simulations, individuals were initialized to have a preset equal or unequal tendency to enter tunnels; an unequal tendency to enter the tunnels was crucial for avoiding long-duration clogs and maintaining optimal traffic flow conditions. Recently, we reported on the implementation of reinforcement rules in a collective of excavating robots that results in unequal workload distribution and improved group performance [51]. In fire ants, it was reported that groups adapt to the removal of the most active workers by upregulating the activity of others [57], though we do not know what mechanism they use to accomplish that.…”

“…Our interest was specifically to determine if excavation regulation could emerge from independent individual decisions without invoking communication through chemical, visual or acoustic signals. Inspired by our recent studies on contact-responsive robotic collectives [50,51], we explored how contact sensing, as an information source, may contribute to global excavation regulation without other communication. To discover a minimal model, we make a simplifying assumption that ants make decisions based on their recent experiences, where encounters with others are registered but no information is actively passed between them.…”

Physics of smart active matter: integrating active matter and control to gain insights into living systems

“…We note that even without discussing if such systems are ''alive'' we can utilize smart active matter systems as models to discover principles by which living systems achieve robust function. 33 Indeed, physicists have a long history of being interested in deep questions but not letting these get in the way of making tangible progress; any history of quantum theory will clearly attest to this useful duality. So, there is much work to done in figuring out how to best couple active matter to smart controllers to enable the accomplishment of various tasks.…”