摘要
针对基本蚁群算法在移动机器人全局路径规划中收敛速度慢,易陷入局部最优解的问题,提出一种改进的蚁群算法。将A*算法的根据目标点自适应调整启发函数的思想应用于蚁群算法中,增加目标点对启发函数的影响;改进状态选择策略,增加解的多样性;混合使用多种信息素分配机制,提高算法的收敛速度。通过布置相同的路径搜索条件,在MATLAB语言环境下进行仿真分析,验证了改进的算法是可行有效的。
An improved ant colony algorithm is proposed in view of the shortcomings of slow rate of convergence of the traditional colony algorithm in the mobile robot global path planning and that it is easy to fall into the local optimal solution.The idea adjusting the self-adaptive heuristic function according to the target point in A*algorithm is applied to the ant colony algorithm which is used to construct the heuristic function.The state selection policy is improved to increase the diversity of the solution.The mixed pheromones distribution mechanism is used to improve the convergence speed of the algorithm.The simulation experiments of the mobile robot global path planning based on the traditional ant algorithms and improved ant algorithms are implemented under the same experimental environment.Experimental results demonstrate the effectiveness and superiority of the improved algorithm.
作者
杨萍
赵珍
郑海霞
YANG Ping;ZHAO Zhen;ZEHNG Haixia(School of Mechanical and Electrical Engineering, Lanzhou University Of Technology Lanzhou 730050, China)
出处
《机械制造与自动化》
2017年第6期155-157,192,共4页
Machine Building & Automation
关键词
移动机器人
路径规划
蚁群算法
自适应
改进算法
mobile robot
path planning
ant colony algorithm
self-adaptive
improved method
