Improved ant colony algorithm for mobile robot path planning

Abstract

In order to remove peak points obtained by most path planning methods and to decrease the risks of trapping in premature convergence and occurrence of local optimization in the traditional ant colony algorithm(ACA),an improved ACA was proposed for a mobile robot path planning.At first,genetic operators were introduced into the traditional ACA using crossover and mutation operators to expand the search space and enhance the global property of solution.Then the optimization operators,such as simplification and smoothness operators,were applied to remove the redundant nodes and to increase the smoothness of the solved path.The simulation results concerning path planning for mobile robot in two grid environments illustrate that,compared with algorithm A* and traditional ACA,the proposed algorithm can obtain a much shorter and smoother path.