Informational Memory Shapes Collective Behavior in Intelligent Swarms

TL;DR

This study explores how internal memory and information exchange in drone swarms lead to complex collective behaviors, including phase transitions and chaotic state switching, driven by informational feedback.

Contribution

It introduces a combined experimental and theoretical framework showing how memory and information processing induce emergent collective phenomena in drone swarms.

Findings

Memory depth controls phase transitions in swarm behavior.

Information feedback can replace physical forces in organizing collective states.

Swarm dynamics exhibit chaotic switching and symmetry breaking.

Abstract

We present an experimental and theoretical study of 2-D swarms in which collective behavior emerges from both direct local mechanical coupling between agents and from the exchange and processing of information between agents. Each agent, an air-table drone endowed with internal memory and a binary decision variable, updates its state by integrating a time series of memories of local past collisions. This internal computation transforms the drone swarm into a dynamical information network in which history-dependent feedback drives spontaneous complete spin polarization, pitchfork bifurcated spin collectives, and chaotic switching between collective states. By tuning the depth of memory and the decision algorithm, we uncover a memory-induced phase transition that breaks spin symmetry at the population level. A minimal theoretical model maps these dynamics onto an effective potential landscape sculpted by informational feedback, revealing how temporally correlated computation can replace instantaneous forces as the driver of collective organization, informed by experiments. These results position physically interacting drone swarms as a model system for exploring the physics of informational drone ensembles whose emergent behavior arises from the interplay between physical interaction and information processing.