地图加权遗传算法的油井自动巡检机器人路径规划

针对油井巡检机器人与障碍物的接触率高，造成设备故障率高增加石油生产成本问题，提出基于地图加权的遗传算法。首先将地图进行栅格化，建立栅格地图模型，并进行加权设置。其次引入遗传算法模型进行路径规划，将每次路径规划结果存入染色体中并计算路径长度，最后筛选最大权值中的路径最短染色体，并绘制路线。在参数设定相同的条件下，采用基于地图加权的遗传算法、经典遗传算法进行比对实验，仿真结果表明，基于地图加权的遗传算法优先选择了不靠近障碍物的栅格的情况下完成了路径规划任务，机器人与障碍物的接触率下降了74.91%,时间和路程仅增加0.3179 s与32%。

Path planning optimization of oil well automatic inspection Robot based on genetic algorithm

A Map weighted genetic algorithm was proposed to solve the problem of high equipment failure rate and increased oil production cost due to the high contact rate between oil well inspection robot and obstacles. Firstly, the map is rasterized, the raster map model is established, and the weighted setting is carried out. Secondly, genetic algorithm model was introduced to carry out path planning, and the result of each path planning was saved into cells and the path length was calculated. Finally, cells with the shortest path in the maximum weight value were selected and the path was drawn. Set in the parameter under the same conditions, using the genetic algorithm based on weighted map, compare experiment, the classical genetic algorithm simulation results show that the genetic algorithm based on weighted map priorities is not close to the obstacles of grids completed the path planning tasks, robot and obstacles of exposure rate dropped by 74.91%, Time and distance increased by only 0.3179s and 32%.