Robot Path Planning Based on Fusion Improved Ant Colony Algorithm

Abstract

Robot path planning is a hot issue in the robotics field. Based on many algorithms, we consider the efficiency and accuracy of path planning. This paper proposes a path planning method that combines improved ant colony algorithm and $\mathrm{A}^{\ast}$ algorithm. First, by improving the pheromone update method, state transition probability and heuristic function in the ant colony algorithm, an ant colony algorithm with better performance is obtained. Afterwards, aiming at the defect that the same initial pheromone concentration in the ant colony algorithm leads to the purposeless search of the first-generation ant colony, the fusion improved ant colony algorithm and the $\mathrm{A}^{\ast}$ algorithm are proposed, and simulation experiments are carried out. The experimental results show that the path is planned through the fusion algorithm the search time is shortened, the number of iterations when reaching convergence is reduced, and a better path can be obtained, which shows that the algorithm can achieve good results when dealing with path planning problems.