A new method for robot path planning based on double-starting point ant colony algorithm

Abstract

Due to the problems of insufficient search accuracy and easy to fall into local extreme values, too many iterations, and single solution goals in the global path planning of real environments, this paper proposes a double-starting ant colony algorithm. By simulating the grid map, the starting position is adjusted on the basis of the ant colony algorithm, and the fixed one-way mobile search method in the traditional ant colony algorithm is improved. Two starting points are set. On this basis, we also optimize the pheromone update strategy so that it can guide the process of the next iteration, which can shorten the path search time and reduce the blindness of the ant colony algorithm in the early search. As the number of iterations increases, the diversity of solutions that the algorithm can obtain in the early stage increases, which increases the probability of obtaining the optimal solution. It can also avoid the problem that the algorithm is prone to fall into the local optimum and accelerate the convergence speed of the algorithm in the later stage. Through the simulation test of the double-starting point ant colony algorithm, it is shown that the algorithm has good optimization performance and good iterative convergence.