Abstract
We investigate emergent behaviors in large-scale space-exploration swarms by integrating density-modulated Boids (DMB) and task-field Ant Colony Optimization (TF-ACO) policies in Gossamer and evaluating at up to 5×10^5 agents in the Leviathan Engine. We characterize phase transitions in alignment, coverage, and collision rate as functions of agent density, sensing radius, and noise. DMB introduces adaptive rule weights based on local density and obstacle potential fields; TF-ACO lays stigmergic gradients on task fields to coordinate coverage and revisit schedules. Across asteroid-belt survey scenarios, DMB improved alignment order parameter by 19% and reduced collision rate by 46% relative to fixed-weight Boids, while TF-ACO increased unique coverage by 24% with 0.7× messages vs a greedy assignment baseline. We observe a supercritical density threshold beyond which naive policies collapse due to oscillations; DMB+TF-ACO delays this transition by 1.6× in density and sustains coverage under noise. Maneuver.Map orchestrations reveal stable pattern regimes and failure modes. These findings suggest emergent coordination can be tuned for robust exploration without centralized control, informing mission designs for belts, rings, and crater fields.