Robot path planning in a dynamic and unknown environment based on Colonial Competitive Algorithm (CCA) and fuzzy logic

Robot path planning has been one of the favorite areas for many Machine Learning researchers from the past up to date. The trajectory designed for a robot can be simple or complex. The robot must pass through obstacles which are either movable or fixed. One of the considerable ways for robot path planning in the dynamic and unknown environment is a combination of Evolutionary algorithm and Fuzzy logic. There are different kinds of evolutionary algorithms such as Genetic algorithm, Ant Colony algorithm, Colonial Competitive algorithm, etc. A new approach has been proposed in this paper for robot path planning in the dynamic and unknown environment based on both the Colonial Competitive algorithm and fuzzy rules. The implemented results of the proposed method present its superiority over previous methods which used only fuzzy logic method.