改进的蚁群算法机器人路径规划研究

The Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

针对蚁群算法收敛速度慢并且容易陷入局部最优解的不足,这里提出一种新的改进算法,首先将蚂蚁目前所在节点与下一时刻要选择的节点之间的关系引入到启发函数当中,然后改变信息素更新规则。利用两种不同的栅格地图在Matlab中对传统蚁群算法和改进的算法进行仿真实验。仿真结果表明,复杂地图环境下基本蚁群算法迭代次数为15次,最短路径为31.21,而这里改进的算法收敛速度更快,迭代次数为9次,最短路径为28.63。改进后的算法不仅在收敛速度上有所提升,并且避免了传统蚁群算法容易陷入局部最优解。

Aiming at the shortcomings of the ant colony algorithm,which is slow to converge and easy to fall into the local optimal solution,this paper proposes a new improved algorithm,first,the relationship between the current node of the ant and the node to be selected at the next moment is introduced into the heuristic function,and then changes the pheromone update rules.Using two different grid maps to simulate the traditional ant colony algorithm and improved algorithm in Matlab.In the complex map environment,the simulation results show that the basic ant colony algorithm has 15 iterations and the shortest path is 31.21,while the improved algorithm in this paper converges faster,with 9 iterations and 28.63.The improved algorithm not only improves the convergence speed,but also avoids the traditional ant colony algorithm easily falling into the local optimal solution.