Improved Ant Colony Optimization Algorithm and Its Application on Path Planning of Mobile Robot

This paper uses the grid method with coding tactic based on effective vertexes of barriers (EVB-CT-GM) as the method of environment modeling and ant colony optimization algorithm with two-way parallel searching strategy (TWPSS-ACOA) is adopted to accelerate searching speed. In view of that the TWPSS-ACOA has the defects of losing some feasible paths and even optimal paths because of its ants meeting judgment strategy (AMJS), so a new AMJS is proposed. Then a new method to rationally distribute initial pheromone is given to accelerate convergence speed of initial stages of ACO algorithm. Later, in order to avoid running into local optima and to speed up optimization process, a new path selecting method and a new global pheromone updating technique are put forward. Finally simulation researches of path planning of mobile robot based on improved TWPSS-ACOA are made under different two-dimension environments and simulation results show the improved algorithm can find safe paths at higher convergence speed even in complex environment.