A novel heterogeneous feature ant colony optimization and its application on robot path planning

Abstract

Robot path planning is a complicated problem which needs to balance many factors. In pathfinding, the robot has to find the shortest path to the destination and avoid the obstacles. Ant colony optimization is a heuristic algorithm which has many excellent features in pathfinding. This paper proposed a heterogeneous feature ant colony optimization (HFACO) algorithm to solve the robot path planning problem. In the proposed method, two kinds of ants with different features are designed to influence the convergence rate of the algorithm by controlling the number of them. We also applied some other novel strategies that enhance the solving quality and the performance. The experiment results show that HFACO can find a better path in a shorter period of time compared to the classical ACO algorithms.