Swarm intelligence research deals with natural and artificial systems composed of many individuals that are coordinated using decentralized control and self-organization. Swarm intelligence is not an attempt to mimic nature but to explore the generative potential of swarm logic. This is a project to embrace complexity and to develop a nonlinear design that operates through multi-agent algorithms, generating an emergent architecture. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied in this field are for example colonies of ants and termites. In our research, we are precisely focusing on those two natural creatures. Looking closer at ants and their swarm intelligence. Their collective behavior of decentralized, self-organized system. Resulting from the local interactions of the individuals with each other and with their environment. This behaviour of the ants allows them to solve their problems by aggregation. This system allows the ants to have an hierarchy and roles for each ant within their community. When heading out for food collecting the Pathfinders find the shortest and best path to take. They find obstacles on the route. Another set of ants come to fix the problem, fill the holes or build a bridge over the gaps so the food collectors can reach the maximum efficiency.