Swarm Robotics is garnering attention in the robotics field due to its substantial benefits. It has been proven to outperform most other robotic approaches in many applications such as military, space exploration and disaster search and rescue missions. It is inspired by the behavior of swarms of social insects such as ants and bees. It consists of a number of robots with limited capabilities and restricted local sensing. When deployed, individual robots behave according to local sensing until the emergence of a global behavior where they, as a swarm, can accomplish missions individuals cannot. In this research, we propose a novel exploration and navigation method based on a combination of Probabilistic Finite Sate Machine (PFSM), Robotic Darwinian Particle Swarm Optimization (RDPSO) and Depth First Search (DFS). We use V-REP Simulator to test our approach. We are also implementing our own cost effective swarm robot platform, AntBOT, as a proof of concept for future experimentation. We prove that our proposed method will yield excellent navigation solution in optimal time when compared to methods using either PFSM only or RDPSO only. In fact, our method is proved to produce 40% more success rate along with an exploration speed of 1.4x other methods. After exploration, robots can navigate the environment forming a Mobile Ad-hoc Network (MANET) and using the graph of robots as network nodes
