The Global Path Planning of Ant Colony System Mobile Robot Based onJump Point Search Strategy

For the problems of ant colony system (ACS) algorithm such as slow convergence speed, falling into local optimum easily and too many turning points, an ACS global path planning algorithm based on jump point search (JPS) strategy is proposed. The algorithm adds a special ant before iteration, uses the direction factor to guide the ant towards the target direction, and queries whether there is the simplest path. When ants query the next node, JPS algorithm is used to eliminate most of the nodes that don't need to be calculated. Finally, simulation experiments are carried out with different grid maps in order to verify the effectiveness of the proposed method. The simulation results show that compared with ACS algorithm, the improved ACS algorithm has faster convergence speed and shorter convergence time, and its path is better. Finally, the algorithm is applied to the actual navigation experiment of mobile robot based on robot operating system (ROS). The experimental results show that the improved ACS algorithm can effectively solve the global path planning problem of mobile robot and significantly improve the efficiency of global path planning of robot.