Research on path planning for mobile robots based on improved ACO

Abstract

To solve the problems that the basic ant colony algorithm (ACO) is easy to fall into the local optimum, the path is too long and there are too many turns when searching the path of the mobile robot, an improved ACO is proposed in this paper. The improved algorithm randomly sets the ant walkable position points in the obstacle-free area of the map, so that the ants reach the next position at any angle according to the proximity point. Then we incorporate the idea of A* valuation function in the heuristic function to let the ants drive to the point on the line between the starting point and the ending point with a greater probability. The simulation results indicate that the improved ACO can reduce the number of turns and iterations in the path planning of mobile robots, so the robot can find a better path in a shorter time.