Robot Global Path Planning Based on an Improved Ant Colony Algorithm

Abstract

A global path planning approach of mobile robot based on improved ant colony algorithm is proposed to solve the problem of trapping into local minimum, in which, a grid map is used to model the environment. The pheromone threshold is limited to avoid the local optimum, and the parameters are adjusted dynamically in this improved ant colony algorithm. Based on the improved algorithm, the path generated by the elitist ant is checked to find the reason of local minima. And then, by embedding a sub-ant colony algorithm into the route choice model, some local routes can be optimized and updated. Simulation Results indicate that this approach can solve the U-shaped groove problem, and reduce the probability of trapping into the local minimum. Compared with several path planning approaches based on ant colony algorithm, the proposed one could generate a shorter path which is global optimal and have the capability of searching path in complicated environments.