A sizing method for a multi-robot system

Abstract

Addresses a fast method for optimizing and sizing a multi-robot system. 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 complementary abilities. It is shown that it is possible to find the optimum criteria to execute the mission and obtain the best probable solution It is based on the stochastic model of the Markov chains. The method is applied to a generic mission of multi-robot cooperation. The mission has also been simulated several thousand hundred times on a computer to compare the results and validate the model. It is also possible, with this model, to quickly predict the system evolution from different initial states and study the impact of the variation of some parameters on the global result. Some ongoing experimentations are also introduced.