Improved ant colony algorithm-based path planning for mobile robot

To solve the contradictory between the convergence speed and the local optimum in ant colony algorithm, an improved ant colony optimization algorithm(IACO) was proposed for path planning of mobile robot in the static environment. The locations of start and goal were utilized to build the environmental model based on the simplified visibility graph. In IACO, the local path information was integrated with the initialization of pheromone and the selected probabilities of the paths, resulting in improving the convergence speed and avoiding the premature phenomenon as far as possible. For overcoming the possible stagnation phenomenon, crossover operation is drawn into the proposed algorithm and the value of α, β and ρ were updated, which enhanced the capability of escaping stagnation phenomenon. The simulation results demonstrated that the search efficiency of optimum path and the overall performance of the proposed algorithm were improved to be better than that of standard ACO.