Abstract: Aiming at the problems of too many turning points, too fast convergence speed and easily falling into local optimum of potential field ant colony algorithm, an jump point ant colony system algorithm with potential field is proposed. The algorithm fuses the search strategy of ant colony algorithm and jump point search algorithm to make the planned path smoother. By introducing the coefficient of force decline in potential field, the local optimal problem of potential field ant colony algorithm is reduced. A simplified jump point search algorithm is introduced to update the initial pheromones and improve the search efficiency at the early stage. In order to verify the effectiveness of the algorithm, raster maps of different specifications are used for simulation experiments. The simulation results show that compared with the potential field ant colony algorithm, the algorithm can effectively reduce the number of convergence iterations, its convergence search time is shorter, and the final search path is better. Finally, the algorithm is applied to the actual mobile robot navigation based on ROS experiment, the experimental results show that the proposed algorithm can effectively solve the problem of mobile robot global path planning, and can significantly improve the efficiency of robot global path planning.

Key words: mobile robot, path planning, ant colony system algorithm, artificial potential field, jump point search algorithm