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

This paper shows how a probabilistic model is able to predict the evolution of most multi-robot systems and thus to save a lot of simulation time. To demonstrate the performance of this approach, a complex heterogeneous system is considered. It is composed of two populations of robots, having different but complementary abilities. 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 one 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 positions. The number of agents is varied in order to obtain the most favorable terminal state: the Markovian analysis is shown to help one to determine the best parameters. Finally, the hardware used in experiments is described