Path Planning of the Mobile Robot Using Fuzzified Advanced Ant Colony Optimization

Ant colony optimization (ACO) is a probabilistic optimization method. In this analysis, its application has been explored in mobile robotics for path planning. It provides multi-feedback information and robustness to the mobile robot during navigation. Due to the robustness of the advanced fuzzified ant colony optimization (FACO), the path planning task has been executed in the unstructured environment, and collision-free navigation has been achieved smoothly. For fuzzified advanced ant colony optimization (FAACO), path pheromone update scheme is divided into two categories like favorable and unfavorable path. Using these, path pheromone as the problems of conventional ACO like slow convergence has been sorted out. The advanced FACO improves the evaporation rate of pheromone to accelerate the convergence speed. Finally, the simulation results show the proposed method conquered the previous drawback.

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