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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.
Aiming at the shortcomings of traditional genetic algorithm, such as the slow convergence speed of robot path planning problem, a knowledge-guided genetic algorithm is designed to add relevant fields to chromosome coding, initial population generation, various genetic operators and optimization operators Knowledge.According to the performance indexes such as the length, safety and smoothness of the robot path, the path optimization operation is carried out by using four kinds of optimization operators: deletion, simplification, correction and smoothing, while the robot path planning is performed. The simulation results show that The proposed method can effectively improve the ability and efficiency of genetic algorithm to solve practical path planning problems.