摘要
针对传统遗传算法求解机器人路径规划问题存在的收敛速度较慢的缺陷,设计一种知识引导遗传算法,在染色体的编码、初始种群的产生、各种遗传算子和优化算子中加入相关的领域知识.综合考虑机器人路径的长度、安全度和平滑度等性能指标,在对机器人进行路径规划的同时,利用删除、简化、修正和平滑4种优化算子进行路径优化操作.仿真结果表明,所提方法能够有效提高遗传算法求解实际路径规划问题的能力和效率.
In order to improve the convergence speed of traditional genetic algorithm for path planning of robot, a knowledge-guided genetic algorithm is designed by introducing domain knowledge of a path planning problem into the coding of chromosome, initialization of population, genetic operators and optimization operators. The length, safety and smoothness of paths are considered simultaneously during the process of path planning. Four optimization operators, deletion, simplification, modification and smoothness operators, are used to optimize paths searched by the genetic operators. Simulation results show that the proposed method can improve the ability and efficiency of genetic algorithm in solving the practical path planning problem of robot.
出处
《控制与决策》
EI
CSCD
北大核心
2009年第7期1043-1049,共7页
Control and Decision
基金
国家自然科学基金项目(60804022)
教育部新世纪优秀人才支持计划项目(NCET-08-0836)
高等学校博士学科点专项科研基金项目(20070290537
200802901506)
国家博士后科学基金项目(20070411064)
江苏省青蓝工程项目(苏教师[2007]2号)
关键词
遗传算法
领域知识
机器人
路径规划
Genetic algorithm
Domain knowledge
Robot
Path planning
