**1. Introduction**

Multi-robot systems [1] have recently been in the spotlight because of the advantage that it can perform a given task more efficiently than a single robot system and can perform several tasks at the same time. For the design and construction of such a multi-robot system, various algorithms which are not required in a single robot system are required. If a multi-robot system is operated in unknown environments, it needs to conduct multi-robot simultaneous localization and mapping (SLAM) [2] to acquire the poses of multiple robots and a collective map for operating the give task cooperatively without collisions. An example of a multi-robot system for multi-robot SLAM in unknown environments is shown in **Figure 1**. The cooperation module which conducts global multiple path planning, relative robot pose estimation, and multiple map merging can be placed on the leader robot or a central control system. The wireless router can be located in the leader robot or another place to cover the operation area of multiple robots. The bandwidth for the wireless communication depends on the size of the operation area and the map representation method. To conduct SLAM, each robot needs sensors to acquire environmental data. Based on the SLAM result, each robot can plan a local path and move toward its own goal safely.

The most frequently used sensor for SLAM is a light detection and ranging (LiDAR) [3] which measures ranges by targeting an object with a laser and

### **Figure 1.**

*An example of a multi-robot system in unknown environments.*

measuring the time for the reflected light to return to the receiver. LiDAR can also be used to make digital three-dimensional representations of areas on the earth's surface and ocean bottom, due to differences in laser return times, and by varying laser wavelengths. Because a LiDAR can provide a lot of information about the surrounding environment, it has been used widely for SLAM. An example of using a LiDAR for a mobile robot is as shown in **Figure 2(a)**. If SLAM is conducted with a LiDAR, a map is generally represented by an occupancy grid map as shown in **Figure 2(b)**. The white, black and gray grids represent empty, occupied and unknown areas, respectively. The size of grids can be adjusted according to the resolution of the LiDAR and the memory size in the embedded system.

The key algorithm in multi-robot SLAM is the grid map merging algorithm in the cooperation module in **Figure 1** which accurately aligns and fuses the individual

### **Figure 2.**

*Occupancy grid map built by a mobile robot with a LiDAR sensor. (a) Mobile robot with a LiDAR sensor (b) Occupancy grid map.*

*Grid Map Merging with Ant Colony Optimization for Multi-Robot Systems DOI: http://dx.doi.org/10.5772/intechopen.98223*

grid maps of multiple robots. Many grid map merging algorithms have been developed, and they have their own advantages over others. However, for the more accurate grid map merging, all the algorithms need an optimization method to align the individual grid maps more precisely. In this work, we propose a new approach based on a sampling-based optimization method for grid map merging. The proposed approach was successfully conducted with other grid map matching algorithms and updated the map transformation matrix between robots more accurately.

The remainder of this paper is organized as follows. In Section 2, multi-robot SLAM is briefly described. In Section 3, the definition and classification of grid map merging are described. In Section 4, the proposed approach which is a grid map merging with ACO is presented. Section 5 shows and analyzes the experimental results of the proposed approach. Finally, conclusions are given.
