摘要
针对当前机器人路径规划算法存在计算参数多的问题,提出一种单计算参的自学习蚁群算法。该算法使用一种改进的栅格法完成环境建模,种群中个体使用8-geometry行进规则,整个种群的寻优过程使用了自学习和多目标搜索策略。其特点在于整个算法只需进行一个计算参数设置。蚂蚁个体可使用1、■、2、■、■步长行进,一次搜索可以发现多条可行路径,提高了算法计算效率。仿真实验表明,在复杂的工作空间,该算法可以迅速规划出一条安全避碰的最优路径,效率优于已存在算法。
The existing robot path planning(RPP)algorithms have the problems that the parameters are complexity.To solve this problem,this paper proposes a self-learning ACO(SlACO)algorithm for robot path planning.In SlACO,an improved grid map(IGM)method is used for modeling the working space and the 8-geometry is used as the moving rule of ant individuals.The strategy of multi-objective search is used for the whole ant colony.The SlACO has the feature that the whole algorithm only need set one computing parameter.Moreover,the ant individuals can move with step 1,■,2,■and■.By the strategy of machine learning and multi-objective search,the SlACO algorithm can find more than one feasible paths with a move from starting position to the ending position.Simulation results indicate that the SlACO algorithm can rapid plan a smooth even in the complicated work-ing space and its efficiency is better than existing RPP algorithms.
作者
程乐
徐义晗
卞曰瑭
Cheng Le;Xu Yihan;Bian Yuetang(Department of Computer Science and Communication Engineering,Huaian Vocational College of Information Technology,Huaian 223003,China;College of Computer and Information,Hohai University,Nanjing 210098,China;School of Business,Nanjing Normal University,Nanjing 210023,China)
出处
《电子技术应用》
2019年第4期100-103,108,共5页
Application of Electronic Technique
基金
国家自然科学基金项目(71301078)
江苏省高校自然科学基金面上项目(16KJB520049)
淮安市自然科学研究计划(HAB201709)
关键词
机器人路径规划
蚁群算法
栅格法
自学习
robot path planning
ant colony optimization
grid map
self-learning
