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A modified ant colony optimization algorithm for implementation on multi-core robots

The Ant Colony Optimization (ACO) algorithm is an evolutionary algorithm that bio-mimics the behavior of ants in finding the shortest path between an origin and a destination within a set of pre-determined constraints. The goal of this work is to create a small-scale application of the ACO using a swarm of small autonomous robots. We investigate the practical applicability of the algorithm in real-life situations by addressing the issues and challenges encountered in the transition from the modeling/simulation level to the real-life application of the algorithm. We also suggest some modifications that will make feasible the implementation of the algorithm on the robots limited computing systems. The results show that the suggested modified algorithm, when implemented on the robotic swarm, enables them to successfully identify the shortest path between two points. These results open the door to a wide variety of applications like search & rescue, path planning, and mass transportation.