In this paper, multi-robot map building problem in a complex and unknown environment is investigated, and a map building approach is presented based on particle swarm optimization algorithm for global optimization, as well as Hilbert curve on the target region detection of multi-robot cooperative. Particle Swarm Optimization has characteristics of evolutionary computation and swarm intelligence, which can provide a good way to make different robots away from each other, near to their last destination and shortest time of arriving each region between robots during a map-building process. Hilbert curve can avoid duplication of the same detection area with the detection radius of the robot. Simulation experiment of comparing with S shape random exploring algorithm shows that this method will enable the robot to find the approximate optimal target area, reduce the probability of duplicate detection, and improve the efficiency of detection.
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