Improved ant colony algorithm based on artificial gravity field for adaptive dynamic path planning

Abstract

In view of the problems such as unclear target direction, low search efficiency, and slow convergence speed of the basic ant colony algorithm in AGV two-dimensional path planning, an improved ant colony algorithm based on artificial gravity field and triangle pruning method is proposed. The algorithm first uses the attractive strength provided by the gravity field to construct heuristic information, enhancing the guidance of the target point on the planning direction and improving the directionality and search efficiency. Then, based on the concentration enhancement mechanism of the elite ant model's pheromone, an adaptive reward update mechanism for increments is proposed to improve the convergence speed. Next, an adaptive adjustment mechanism of the pheromone heuristic factor value correlated with the iteration number is discussed to balance the randomness and search efficiency of the entire planning process. Finally, the triangle pruning method is applied to global path optimization based on global path planning, effectively reducing the number of turning nodes and improving the actual motion efficiency. Comparative experiments on path planning in two-dimensional static maps using matlab validate the effectiveness of the improved algorithm in AGV global dynamic path planning.