基于改进蚁群算法的机器人全局路径规划

Research on Robot Global Path Planning Based on Improved Ant Colony Algorithm

针对传统蚁群算法存在初始信息素缺乏、收敛速度慢以及无法有效躲避障碍物等问题,文中提出了一种基于改进蚁群算法的全局路径规划。引入正态分布函数改进传统启发函数,提高了算法效率,缩短了算法收敛所需时间。自适应调整信息素挥发系数,限定信息素范围,避免过早收敛。对算法路径平滑处理,缩短路径长度,从而实现机器人的全局路径规划。仿真结果表明,在20×20环境下,文中算法平均迭代次数比传统蚁群算法减少了28代,收敛速度更快。平均拐点减少了33.3%,使路径更为平滑,克服了初始信息素缺乏,加快了收敛速度,减少了拐点数量,能够有效躲避环境中的障碍物,证明了该算法的可行性。

In response to the problems of traditional ant colony algorithm such as lack of initial pheromone,slow convergence speed and inability to effectively avoid obstacles,this study proposes a global path planning based on improved ant colony algorithm.The introduction of a normal distribution function improves the traditional heuristic function,greatly improving the efficiency of the algorithm and shortening the time required for convergence;adaptively adjusting the pheromone volatility coefficient to limit the pheromone range and avoid premature convergence;and smoothing the algorithm path to shorten the path length,thus realising global path planning for the robot.Simulation results show that under a 20×20 environment,the average number of iterations of the proposed algorithm is 28 generations less than that of the traditional ant colony algorithm,resulting in faster convergence,the average number of inflection points is reduced by 33.3%,making the path smoother,overcoming the lack of initial pheromone,speeding up convergence,reducing the number of inflection points,and enabling effective avoidance of obstacles in the environment,demonstrating the feasibility of the algorithm.