基于概率距离的电脑鼠走迷宫融合算法研究

Research on Micromouse Walking Maze Fusion Algorithm Based on Probability Distance

针对传统电脑鼠迷宫搜索算法无法适应随机迷宫图搜索的问题,提出一种新的电脑鼠走迷宫融合算法。运用概率距离将迷宫划分为八区域,标定各区域概率距离特征并进行算法填充,实现概率距离向心算法和洪水算法的高效融合,提高迷宫搜索效率并降低对高性能CPU的依赖性。通过对6张迷宫的测试结果表明,与传统向心和洪水算法相比,该算法迷宫搜索时间可减少50%,搜索成功率达到100%,是一种高效的迷宫融合搜索算法。

Aiming at the problem that the traditional micromouse maze search algorithm can not adapt to the random mazemap search,a new micromouse walking maze fusion algorithm is proposed.The algorithm uses the probability distance to divide the maze into eight regions,calibrates the probability distance features of each region and fills the algorithm to achieve efficient fusion of the probabilistic distance centripetal algorithm and the flood algorithm,improving the maze search efficiency and reducing the dependence on the high-performance CPU.Test results of 6 mazes show that,compared with the traditional centripetal and flooding algorithms,the algorithm can reduce the maze search time by 50%and the search success rate by 100%,it is an efficient maze fusion search algorithm.