Addressing the challenges of path unevenness and prematurity in mobile robot path planning using traditional genetic algorithms,an enhanced genetic algorithm was introduced.Initially,a terminal distance and included angle-based population initialization method was implemented to elevate the quality of the initial population.Subsequently,a novel fitness function tailored to path length and turning angle was developed to enhance path smoothness.An improved tournament selection strategy retaining inferior solutions was employed to bolster population diversity.Furthermore,a node quantity screening-based crossover operator was designed to expedite the convergence speed and minimize the invalid crossovers.Additionally,multiple mutation strategies were implemented to enhance the search capability in the algorithm's later stages.Extensive comparative simulation experiments reveal that this improved genetic algorithm avoids prematurity,generates smoother paths,and converges faster,and the comprehensive performance is relatively better.
关键词
移动机器人/路径规划/改进遗传算法/平滑路径/种群初始化
Key words
mobile robot/path planning/improved genetic algorithm/smooth path/population initialization
维普
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