A self-learning algorithm with one computing parameter for path planning

Abstract

The existing robot path planning(RPP) algorithms have the problems that the parameters are complexity. To solve this problem, this paper proposes a self-learning ACO(SlACO) algorithm for robot path planning. In SlACO, an improved grid map(IGM) method is used for modeling the working space and the 8-geometry is used as the moving rule of ant individuals. The strategy of multi-objective search is used for the whole ant colony. The SlACO has the feature that the whole algorithm only need set one computing parameter. Simulation results indicate that the SlACO algorithm can rapid plan a smooth even in the complicated working space and its efficiency is better than existing RPP algorithms.

Keywords

• robot path planning
• ant colony optimization
• grid map
• self-learning