一种改进蚁群算法的移动机器人路径规划研究

Research on path planning of mobile robot based on improved ant colony algorithm

针对蚁群算法在大型、复杂环境下全局搜索效率差且收敛速度慢的问题,提出一种改进的蚁群路径规划算法。该算法通过引入目标方向函数作为启发因子,构建距离与目标方向的融合启发函数,以提高算法的收敛速度;采用一种基于有效拐点的路径优化策略,以减少规划路径的转弯次数;在输出路径的基础上采用三次B样条曲线进行路径平滑处理,以提高路径平滑性。通过梯度实验和2种不同规模对比实验表明,该算法与传统蚁群算法相比,2种规模下转弯次数分别减少41.67%和70.18%,最短路径迭代次数分别减少72.73%和63.24%,验证了该算法的优越性与可行性。

An improved ant colony path planning algorithm was proposed to solve the problem of poor global search efficiency and slow convergence speed of ant colony algorithm in large and complex environments.In this algorithm,the target direction function was introduced as the heuristic factor,and the fusion heuristic function of distance and target direction was constructed to improve the convergence speed of the algorithm.A path optimization strategy based on effective inflection points was adopted to reduce the turning times of the planned path.Based on the output path,cubic B-spline curve was used to smooth the path and improve the smoothness of the path.Gradient experiments and two different scale experiments show that compared with the traditional ant colony algorithm,the turning times of this algorithm under the two scales are reduced by 41.67%and 70.18%,and the iteration times of the shortest path are reduced by 72.73%and 63.24%,which verifies the superiority of this algorithm.