Abstract
In this work, teams of small mobile robots are used to test hypotheses about cooperative transport by ants. This study attempts to explain a decrease in steady-state transport speed with increasing team size that was previously observed in the ant Novomessor cockerelli. Two models of one-dimensional collective towing are compared: one in which transporters with different maximum speeds pull the payload with continuous, variable forces and another in which transporters with identical speeds pull with intermittent, unsynchronized forces. A statistical analysis of ant data supports the hypothesis that ants behave according to the first model, in which the steady-state transport speed is the maximum speed of the slowest teammate. By contrast, the ant data are not consistent with the second model, which predicts constant speed regardless of team size. To verify these predictions, the ant behaviours in each model are translated into decentralized controllers and implemented on teams of two to four robots. The controller for the first model incorporates a real-time reinforcement learning algorithm that successfully reproduces the observed relationship between ant team size and transport speed. The controller for the second model yields the predicted invariance of transport speed with team size. These results show the value of robotic swarms for testing mechanistic hypotheses about biological collectives.
Highlights
Cooperative transport of large food items by ants is an impressive example of robust multi-agent coordination that is fully decentralized, scalable with the number of transporters, and effective in unknown environments with uneven terrain and obstacles
We have investigated the observation that the transport speed of a load towed by several Novomessor cockerelli ants decreases as a function of the team size, even with the same load mass per ant
The predicted load speed was independent of team size, as long as the mass per transporter remained constant, and the prediction was verified through experiments with teams of two to four small mobile robots
Summary
Cooperative transport of large food items by ants is an impressive example of robust multi-agent coordination that is fully decentralized, scalable with the number of transporters, and effective in unknown environments with uneven terrain and obstacles. A dynamical model of the ant data in [11] successfully reproduced transport dynamics by assuming that ants on the leading edge of the load pulled and lifted, while ants on the trailing edge only lifted [12] Effects such as these might be expected to reduce speed for larger team sizes, if additional transporters are required to occupy less advantageous positions. The controller is based on a reinforcement learning algorithm that uses only stigmergic feedback, similar to the type of information that would be available to the ants This controller was implemented on teams of two to four Pheeno robots [15] that cooperatively towed a rectangular payload, along with a second controller that produced intermittent, uncoordinated pulling forces.
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