Currently swarm applications are gaining huge interest among almost all engineering areas. Especially exploration of environments where global knowledge is limited scenarios. For instance, search and rescue operations in the after-disaster locations, extra-terrestrial environments, and hazardous environment where autonomous operations, robustness, and self-organization ability are required the most. Due to the high demand in various applications and existing research gap in research area, I focused on the construction and improvement of swarm-based exploration framework. The given system in this thesis works by deploying frontier-based search mechanism in an environment which is represented by occupancy grid. To improve the efficiency of the whole system I implemented a non-conventional PSO (particle swarm optimization) algorithm-based path planning method which showed great improvement over other conventional methods. The proposed system explores and maps the environment fully autonomously, while sharing the local maps among the members of the swarm. In addition, the system was built in ROS2(robotic operation system) system with the combination of python programming language which gives great advantages for future deployment of the framework into real robot environment.