改进遗传算法在机器人路径规划中的应用

Application of improved genetic algorithm in robot path planning

为解决基本遗传算法在移动机器人路径规划中存在的路径不够平滑、易陷入局部最优等问题,提出了一种改进遗传算法。首先采用中间值插入法提高种群的初始质量;其次设计了新的适应度函数来平滑路径;然后采用混合选择策略以改善算法早熟的缺陷,同时对交叉算子和变异算子进行了改进以便增加种群的多样性;设计了自适应策略,对交叉和变异概率进行调整,更有效地避免了算法陷入局部最优的情况;最后提出简化算子,对生成路径进行二次优化。仿真结果表明,该算法在性能上更优。

In order to solve the problems of the basic genetic algorithm in the path planning of mobile robots that the path is not smooth enough and easily falls into the local optimum,an improved genetic algorithm was proposed.First,the intermediate value insertion method was used to improve the initial quality of the population;secondly,a new fitness function was designed to smooth the path;then the hybrid selection strategy was used to improve the premature of the algorithm,at the same time,the crossover operator and mutation operator were improved to increase the diversity of the population;the crossover and mutation probability were adjusted by the designed adaptive strategy to avoid falling into local optimum;finally,the simplification operator was used to optimize the generated path.The simulation results show that the algorithm is better in performance.