@inproceedings{Kumar_Buffin__Pavlic_Pratt_Berman_13,
	author = {Kumar, Ganesh P. and Buffin, Aur{\'e}lie and Pavlic, Theodore P. and Pratt, Stephen C. and Berman, Spring M.},
	title = {A Stochastic Hybrid System Model of Collective Transport in the Desert Ant Aphaenogaster Cockerelli},
	booktitle = {Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control},
	series = {HSCC '13},
	year = {2013},
	isbn = {978-1-4503-1567-8},
	location = {Philadelphia, Pennsylvania, USA},
	pages = {119--124},
	numpages = {6},
	url = {http://doi.acm.org/10.1145/2461328.2461349},
	doi = {10.1145/2461328.2461349},
	acmid = {2461349},
	publisher = {ACM},
	address = {New York, NY, USA},
	keywords = {bio-inspired robotics, biomimicry, collective transport, distributed robot systems, social insect behavior modeling, stochastic hybrid system},
	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.},
}