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Markovian analysis of a heterogeneous system: application to a cooperation task for multiple consumer robots.

Rongier, P.; Liegeois, A.; Simonin, O.

In: Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC'2000), 8-11 October 2000, Nashville, TN, Vol. 4, pages 3033-3038. 2000.

Abstract: This paper shows how a probabilistic model is able to predict the time evolution of most multi-robot systems and thus to save a lot of simulation time. To demonstrate the performance of such an approach, a complex heterogeneous system is considered. It is composed of two populations of robots, having different but complimentary capabilities. They must survive by finding supply centers in the environment. It is shown how to model the process by a stochastic Petri net and its associated Markov chain. The latter allows to compute the time evolution of the system. The process includes several sink states, which correspond to a singular problem. However, comparisons of simulation and theoretical results show very close values of the state probabilities when the agents are initially located at random locations. The number of agents is varied in order to obtain the most favorable terminal state: the Markovian analysis is shown to help determine quickly the best parameters. Finally, the hardware used presently for real experiments is described.

Keywords: Markovian analysis, heterogeneous systems, multiple-robot systems, stochastic Petri nets.


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