Dynamic Path Planning of Mobile Robot Based on Improved Ant Colony Optimization Algorithm

Abstract

Aiming at the problem that the traditional ant colony algorithm (ACO) has poor solution quality in the dynamic path planning process, this paper proposes an improved ACO. Firstly, the genetic operator fused with the traditional ACO is proposed, and the genetic operation is used to expand the search space of the solution. Secondly, the fitness function is introduced in the traditional ACO and the safety distance is added. The pros and cons of the comprehensive evaluation algorithm planning path. Then, by introducing the optimization operator, the redundant nodes are eliminated and the smoothness is improved. Finally, the path planning simulation experiment is carried out in the grid map. The results show that the proposed algorithm can find a shorter and smoother in the dynamic environment path.