基于改进遗传算法的移动机器人路径规划

2020 3rd International Conference on Electron Device and Mechanical Engineering (ICEDME) Pub Date : 2020-05-01 DOI:10.1109/ICEDME50972.2020.00163

Yongdong Wei, Jihe Feng, Yuming Huang, Kaiwei Liu, Bin Ren

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引用次数: 1

摘要

针对移动机器人路径规划中收敛速度慢、避免局部最优的问题，对基本遗传算法进行了改进，提出了一种静态环境下移动机器人路径规划方法。本文将最短规划路径和自适应平滑度作为路径个体适应度函数值的影响因素，并对这两个因素赋予一定的权重。改进了基本遗传算法的局部最优解，克服了早熟的缺点，提高了算法的全局搜索能力。仿真结果表明，改进的遗传算法在移动机器人路径规划中是可行和有效的。

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Path planning of mobile robot based on improved genetic algorithm

In order to solve the problems of slow convergence speed and avoid local optimum in the path planning of mobile robot, the basic genetic algorithm was improved and a method for path planning of mobile robot in static environment was proposed. In this paper, the shortest planning path and the adaptive smoothness are combined as the influencing factors of the individual fitness function value of the path, and a certain weight is assigned to these two factors. It improves the local optimal solution of the basic genetic algorithm, overcomes the shortcoming of precocity, and improves the global search ability of the algorithm. The simulation results show that the improved genetic algorithm is feasible and effective in the path planning of mobile robots.

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来源期刊

2020 3rd International Conference on Electron Device and Mechanical Engineering (ICEDME)

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