基于异构双种群全局视野蚁群算法的移动机器人路径规划研究

针对蚁群算法中存在的算法收敛速度慢、逼近最优解能力不足等问题，提出一种基于异构双种群全局视野的蚁群算法，并将其应用于移动机器人路径规划领域。首先，研究基于异构蚁群的并行结构，通过差异化种群的相互协作提高蚁群算法的收敛速度和规划最优路径的能力；然后，研究具有全局视野的自适应步长，解决蚁群算法因局部视野导致无法搜索到最优步长的问题；最后，研究信息素初始化以及信息素更新方式，改进传统蚁群算法运行初期搜索无序性以及信息素更新不合理等问题。实验结果表明，该算法在逼近最优解能力和提高收敛速度等方面较对比方法有着显著提高，在测试的几种仿真地图中，平均路径长度优化了12%,平均迭代次数和平均运行时间分别减少了67%和82%。

Research on path planning of mobile robot based on heterogeneous dual population and global vision ant colony algorithm

Aiming at the problems of slow algorithm convergence and insufficient ability to approach the optimal solution in the ant colony algorithm, this paper proposed an ant colony algorithm based on heterogeneous dual-population and global vision, and applied to the field of mobile robot path planning. Firstly, the algorithm studied the parallel structure based on heterogeneous ant colonies, improved the convergence speed and the ability to plan the optimal path by collaboration through differentiated populations. Then, the algorithm studied the adaptive step size with global field of view, and solved the problem that the ant colony algorithm cannot search for the optimal step size due to the local field of view. Finally, the algorithm studied the pheromone initialization and update methods, improved the search disorder and unreasonable pheromone update in the initial stage of the traditional ant colony algorithm. The experimental results show that the algorithm has a significant improvement over the comparison methods in terms of approaching the optimal solution ability and improving the convergence speed. In the several simulation maps tested, the average path length is optimized by 12%, and the average number of iterations and average running time a reduce 67% and 82% respectively.