A stochastic hybrid system model of collective transport in the desert ant aphaenogaster cockerelli

Abstract

Collective food transport in ant colonies is a striking, albeit poorly understood, example of coordinated group behavior in nature that can serve as a template for robust, decentralized multi-robot cooperative manipulation strategies. We investigate this behavior in Aphaenogaster cockerelli ants in order to derive a model of the ants' roles and behavioral transitions and the resulting dynamics of a transported load. In experimental trials, A. cockerelli are induced to transport a rigid artificial load to their nest. From video recordings of the trials, we obtain time series data on the load position and the population counts of ants in three roles. From our observations, we develop a stochastic hybrid system model that describes the time evolution of these variables and that can be used to derive the dynamics of their statistical moments. In our model, ants switch stochastically between roles at constant, unknown probability rates, and ants in one role pull on the load with a force that acts as a proportional controller on the load velocity with unknown gain and set point. We compute these unknown parameters by using standard numerical optimization techniques to fit the time evolution of the means of the load position and population counts to the averaged experimental time series. The close fit of our model to the averaged data and to data for individual trials demonstrates the accuracy of our proposed model in predicting the ant behavior.

Statistical analysis of stochastic multi-robot boundary coverage

“…Our future work will investigate scenarios that favor one approach over the other; for example, we would wish to determine the better coverage approach when collisions with obstacles or between robots need to be avoided. d) Applications to multi-robot transport problems: We are currently investigating the problem of developing strategies for multi-robot collective transport that are robust to payload type, environment layout, and transport team size, much like group retrieval strategies employed by certain species of ants [25]. We will consider scenarios in which robots stochastically allocate themselves around a payload and then proceed to transport the payload as a team.…”

Design of ant-inspired stochastic control policies for collective transport by robotic swarms

“…Robots bound to the Back (left) halves of the loads are shown in yellow, robots bound to the Front (right) halves are shown in purple, and the freely moving Detached robots are shown in green. This scenario is based on the observations of ants in (b) from Kumar et al (2013). There, the circular load is moving to the right, and ants are characterized as grasping the Back (left) or Front (right) of the load; otherwise, they are Detached ants.…”

“…Even with the possibility of rotation, theoretical models of fixed teams of robots show that rotation is only transient. Moreover, observational evidence of ants and simulated ant models (Berman et al 2011;Kumar et al 2013) show very little load rotation once smooth persistent load transport has begun. Thus, between attachment and detachment events, we model the time evolution of the load position x L and load velocity v L as linear translational motion: Fig.…”

“…We assume that the robots can differentiate between load types, but they have no prior information on the load quantities and locations. The robots execute random walks throughout the environment and rely on local sensor information to Kumar et al (2013) of a team of Aphaenogaster cockerelli ants carrying a weighted circular foam disk along a leftward direction. The ants on the left ("Front") side of the disk are assumed to be lifting and pulling the object, while the ants on the right ("Back") side of the disk are assumed to be only lifting the object.…”

Consensus or deadlock? Consequences of simple behavioral rules for coordination in group decisions

“…This previous research has also included detailed descriptions and models of cooperative transport, and in some cases models have been compared with empirical data (e.g. 4,22,24). But these studies have not focused on comparing alternative behavioral rules for overcoming the coordination challenge; thus, our understanding of behavioral rules in cooperative transport is limited.…”