Abstract
Distributed stochastic search is proposed for cooperative behavior in multi-robot systems. Distributed gradient is examined. This method consists of multiple stochastic search algorithms that start from different points in the solutions space and interact to each other while moving towards the goal position. Distributed gradient is shown to be efficient when the motion of the robots towards the goal position is described by a quadratic cost function. The algorithm’s performance is evaluated through simulation tests.
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