**Abstract**

Multi-robot systems have recently been in the spotlight in terms of efficiency in performing tasks. However, if there is no map in the working environment, each robot must perform SLAM which simultaneously performs localization and mapping the surrounding environments. To operate the multi-robot systems efficiently, the individual maps should be accurately merged into a collective map. If the initial correspondences among the robots are unknown or uncertain, the map merging task becomes challenging. This chapter presents a new approach to accurately conducting grid map merging with the Ant Colony Optimization (ACO) which is one of the well-known sampling-based optimization algorithms. The presented method was tested with one of the existing grid map merging algorithms and showed that the accuracy of grid map merging was improved by the ACO.

**Keywords:** Ant Colony Optimization, Intelligent Robot, Grid Map Merging, SLAM, Multi-Robot Systems
