De Gennes's "Ant is A Labyrinth" problem confronted by real ants

We tracked longhorn crazy ant collective as they collectively haul large items through a semi-natural, randomized environment. To set a scale on the navigational efficiency of the ants ,we mapped their motion onto the 'Ant-in-a-Labyrinth' framework which studies physical transport through disordered media. We show that, in this environment, the ants use their numbers to collectively extend their sensing range. Although this extension is moderate, it nevertheless allows for extremely fast traversal times that overshadow known physical solutions to the 'Ant-in-a-Labyrinth' problem. To explain this large payoff, we use percolation theory and prove that whenever the labyrinth is solvable, a logarithmically small sensing range suffices for extreme speedup. Our results provide an algorithmic perspective to the ant-in-a-labyrinth problem while illustrating the potential advantages of group living and collective cognition for increasing transport efficiency.