摘要
为了提升移动机器人在全局静态环境下搜寻到达指定目标点的最优路径的能力,提出一种粒子群算法和蚁群算法改进求解路径规划问题的融合算法。改进算法针对粒子群算法易陷入局部极值,利用蚁群算法获得全局最优路径;设置标识栅格,提升路径安全度;同时引入活跃因子增加粒子速度的多样性,根据粒子群算法最优解调整路径上信息素分布,解决蚁群算法中初始信息素缺乏的问题。融入简化算子,对路径做进一步处理,优化路径长度。仿真结果表明,融合算法具有较强的安全性以及改善了算法寻找最优解的能力。
In order to improve global optimal path planning to preset destination for mobile robots,a fusion algorithm of ant colony and particle swam optimization is proposed to modify problems on solving for optimal paths.Ant colony algorithm is applied to obtain the global optimal path,since particle swarm optimization is prone to local optimization;sets the identity grid and improves path reliability;brings in active factor to promote particle velocity diversity.Simplified operator is incorporated to further optimize the path length.The simulation results show that the fusion algorithm has a satisfactory reliability and upgrades the optimization performance.
作者
韩颜
许燕
周建平
HAN Yan;XU Yan;ZHOU Jian-ping(College of Mechanical Engineering,Xinjiang University,Urumqi 830047,China)
出处
《组合机床与自动化加工技术》
北大核心
2020年第2期47-50,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
自治区高校科研计划自然科学重点项目(XJEDU2018I006)
关键词
移动机器人
路径规划
粒子群算法
蚁群算法
融合算法
mobile robot
path planning
particle swarm optimization
ant colony algorithm
fusion algorithm
