摘要
为了降低移动机器人工作路径长度、减少算法迭代次数、提高路径平滑性,提出了多种群博弈蚁群算法的规划方法。建立了机器人工作环境的栅格模型;提出了由1个主种群和2个从种群组成的多种群蚁群算法;将博弈论应用于种群的协同与竞争中,设计了合作博弈机制、奖惩机制、针锋相对机制和协调博弈机制;针锋相对机制和协调博弈机制应用于从种群间的交流与竞争,以帕累托最优为目的提高整个从种群的搜索多样性;合作博弈机制和奖惩机制应用与主从种群之间的交流与合作,使从种群将搜索经验和较优路径片段传递给主种群,从而提高主种群搜索效率和质量。经仿真验证,多种群博弈蚁群算法的路径多样性在迭代过程中保持较高水平;多种群博弈算法规划的路径长度比最大最小蚂蚁系统减小了5.98%,搜索迭代次数和路径平滑性也优于最大最小蚂蚁系统,证明了多种群博弈蚁群算法在路径规划中的有效性。
In order to reduce mobile robot working path length,lessen algorithm iteration time,and improve path smoothness,Planning method based on multi-population game ant colony algorithm is put forward.Grid model of robot working environment is built.Multi-population ant colony algorithm consisting of 1 main-population and 2 vassal-populations is proposed.Applying game theory to synergy and competition among population,cooperation game mechanism,reward and punishment mechanism,tit-for-tat mechanism and coordinating game mechanism are designed.cooperation game mechanism and tit-for-tat mechanism are applied to communication and competition among vassal-populations to improve diversity of vassal-population.reward and punishment mechanism and cooperation game mechanism are used to communication and competition among main-population and vassal-population,which makes vassal-population delivery searching experiment and optimal path segment to main-population to improve searching efficiency and quality of main-population.It is clarified by simulation that diversity of path planned by multi-population game ant colony algorithm maintains a high level.Path length planned by multi-population game ant colony algorithm decrease by 5.98%compared with maximum-minimum ant system.Besides,iteration time and path smoothness are also primary to maximum-minimum ant system.Which can proves validity of multi-population game ant colony algorithm on path planning.
作者
陈银燕
高安邦
CHEN Yin-yan;GAO An-bang(Jiangsu Engineering Technical R&D Center for Equipment Manufacturing of Electronic Products,Jiangsu Huaian223003,China;Huaian Vocation College of Information and Technology,Jiangsu Huaian223003,China;Harbin University of Science and Technology,Heilongjiang Harbin150080,China)
出处
《机械设计与制造》
北大核心
2021年第1期272-276,281,共6页
Machinery Design & Manufacture
基金
2019年淮安市自然科学研究项目课题(HAB201910)。
关键词
机器人导航
多种群博弈蚁群算法
博弈论
帕累托最优
Robot Navigation
Multi-population Game Ant Colony Algorithm
Game Theory
Pareto Optimality
