Application of Ant Colony-Particle Swarm Optimization Algorithms in Path Planning of Security Robots

Abstract

In order to improve the efficiency in path planning of security robot, this paper addresses to construct three kinds of grid obstacle models from simple to complicated, and use the integrated ant colony-particle algorithm to make global path planning. For further explaining the effectiveness of the combined algorithm implementation, this paper compares the different effects in path planning of the traditional ant colony algorithm and the combined ant colony-particle swarm optimization from three aspects, which are the iteration number of searching the optimal solution, the shortest path length and the running time. It is found that the integrated ant colony-particle algorithm enhances the search efficiency of the optimal solution and reduces the number of iterations of the search, so as to complete the optimal path of the security robot from the start point to the end point.