arXiv subjects

Michael A. D. Goodisman

Publications and source records attributed to Michael A. D. Goodisman.

4 recordsLinked to original sources

arXiv2026

Emergent Workload Inequality in Collective Excavation

Living collectives and artificial swarms frequently employ a division of labor, wherein individuals take on different tasks or perform different amounts of work. However, the mechanisms used by collectives to divide labor remain poorly understood. Here, we study how workload inequality arises in collectives by monitoring excavation in Solenopsis invicta fire ants, whose coordination in constrained environments makes them an attractive system for studying division of labor. We vary group size (between 2 and 25 ants) and track digging activity to create Lorenz curves and corresponding Gini coefficients, which represent relative workload inequality. We find that that workload becomes more unequal as group size increases: the number of "active" ants scales with the square root of the group size. We implement a cellular automata (CA) model in which agents regulate their activity based on local crowding in the tunnel. The CA reproduces experimental Gini coefficients over a wide range of parameters and group sizes, indicating that local decisions emergently account for the scaling of workload inequality. An analytic rate equation model recovers the square root scaling with the assumption that individuals exit the tunnel at a rate which scales quadratically with the group size. Power law scalings in workload distribution have been observed in other systems, including social and natural sciences; however, these laws are primarily observational. Here, we provide a mechanistic explanation for the emergent workload scaling patterns in constrained biological collectives, offering insight into organization in both natural and future task capable engineered collectives and swarms.

physics.bio-ph↗

arXiv2025

Robot Excavation and Manipulation of Geometrically Cohesive Granular Media

Construction throughout history typically assumes that its blueprints and building blocks are pre-determined. However, recent work suggests that alternative approaches can enable new paradigms for structure formation. Aleatory architectures, or those which rely on the properties of their granular building blocks rather than pre-planned design or computation, have thus far relied on human intervention for their creation. We imagine that robotic swarms could be valuable to create such aleatory structures by manipulating and forming structures from entangled granular materials. To discover principles by which robotic systems can effectively manipulate soft matter, we develop a robophysical model for interaction with geometrically cohesive granular media composed of u-shape particles. This robotic platform uses environmental signals to autonomously coordinate excavation, transport, and deposition of material. We test the effect of substrate initial conditions by characterizing robot performance in two different material compaction states and observe as much as a 75% change in transported mass depending on initial substrate compressive loading. These discrepancies suggest the functional role that material properties such as packing and cohesion/entanglement play in excavation and construction. To better understand these material properties, we develop an apparatus for tensile testing of the geometrically cohesive substrates, which reveals how entangled material strength responds strongly to initial compressive loading. These results explain the variation observed in robotic performance and point to future directions for better understanding robotic interaction mechanics with entangled materials.

cs.RO↗

arXiv2025

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

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, time-consuming collisions and no single agent can guide the entire collective. Here, we hypothesized that effective flow and clog mitigation could emerge purely through local learning. We tasked small groups of robots with pellet excavation in a narrow tunnel, allowing them to modify reversal probabilities over time. Initially, robots had equal probabilities and clogs were common. Reversals improved flow. When reversal probabilities adapted via collisions and noisy tunnel length estimates, workload inequality and 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.

cs.RO↗

arXiv2013

Climbing, falling and jamming during ant locomotion in confined environments

Locomotion emerges from effective interactions of an individual with its environment. Principles of biological terrestrial locomotion have been discovered on unconfined vertical and horizontal substrates. However a diversity of organisms construct, inhabit, and move within confined spaces. Such animals are faced with locomotor challenges including limited limb range of motion, crowding, and visual sensory deprivation. Little is known about how these organisms accomplish their locomotor tasks, and such environments challenge human-made devices. To gain greater insight into how animals move within confined spaces we study the confined locomotion of the fire ant {\em Solenopsis invicta}, which constructs subterranean tunnel networks (nests). Laboratory experiments reveal that ants construct tunnels with diameter, D, comparable to bodylength, L=3.5 $\pm$ 0.5 mm. Ants can move rapidly (> 9 bodylengths/sec) within these environments; their tunnels allow for effective limb, body, and antennae interaction with walls which facilitate rapid slip-recovery during ascending and descending climbs. To examine the limits of slip-recovery in artificial tunnels we perform perturbations consisting of rapid downward accelerations of the tunnels, which induce falls. Below a critical tunnel diameter, $D_s = 1.31 \pm 0.02$ L, falls are always arrested through rapid interaction of appendages and antennae with tunnel walls to jam the falls. $D_s$ is comparable to the size of incipient nest tunnels (D = 1.06 $\pm$ 0.23 L) supporting our hypothesis that fire ants construct environments which simplify their control task when moving through the nest, likely without need for rapid nervous system intervention.

physics.bio-ph↗