一种面向室内AGV路径规划的改进蚁群算法

An improved ant colony algorithm for indoor AGV path planning

针对传统蚁群算法在大规模和复杂环境中,全局搜索效率差,收敛速度慢,路径转弯次数过多且不够平滑等问题,本文提出一种改进蚁群算法。该方法通过动态更新不同等级蚂蚁路径上的信息素,加快算法的收敛速度;通过引入距离函数和方向函数作为启发因子,改善路径搜索质量;采用一种改进自适应伪随机转移策略,减小陷入局部最优解的概率;在最优路径的基础上引入三次均匀B样条曲线进行优化,提高路径的平滑性。通过在2种不同规模环境下的路径规划实验表明,本文算法相比传统算法在分别减少55.6%和59.4%转弯次数的基础上,提升87.5%和100%的收敛速度,验证了本文算法的优越性。最后,以QBot2e为平台,将本算法应用到室内自动导引车(AGV)路径规划中,进一步验证了算法的实用性。

The traditional ant colony algorithm in large-scale and complex environment has problems of slow global search convergence,too many turns in the path and not smooth enough.To address these issues,an improved ant colony algorithm is proposed in this article.This method speeds up the convergence of the algorithm by dynamically updating the pheromones on different levels of ant paths.By introducing the distance function and the direction function as heuristic factors,the quality of path search is improved.An improved adaptive pseudo-random transition strategy is utilized to avoid the probability of falling into the local optimal solution.Based on the optimal path,the cubic uniform B-spline curve is introduced to improve the smoothness of the path.Compared with the traditional algorithm,the path planning experiments in two different scale environments show that the proposed algorithm reduces the number of turns by 55.6%and 87.5%,respectively.The convergence speed is improved by 87.5%and 100%,which verifies the superiority of the proposed algorithm.Finally,taking QBot2 e as the platform,the algorithm is applied to indoor automated guided vehicle(AGV)path planning to further evaluate the practicability of the algorithm.