It is a classical problem to divide the workspace of the robot by grids and optimize the path of the robot by using optimization algorithm. At present, the ant colony algorithm is used to optimize the path on the grid map, and the genetic algorithm is used to optimize the path on the grid map, which is more difficult to achieve. The difficulties of using genetic algorithm to deal with the path planning of robot on raster map mainly include: 1. guaranteeing that the path is uninterrupted, 2. guaranteeing that the path does not cross obstacles. The steps of genetic algorithm in solving optimization problems are fixed, that is, population initialization, selection, crossover, mutation, fitness calculation. Then I will talk about the problems, difficulties and solutions of genetic algorithm in each step of robot path planning in raster map.

Filename	Size	Update
GAforPathPlaning\cal_path_smooth.m	1361	2019-01-10
GAforPathPlaning\cal_path_value.m	749	2018-12-17
GAforPathPlaning\crossover.m	1014	2019-01-10
GAforPathPlaning\DrawMap.m	335	2019-01-10
GAforPathPlaning\generate_continuous_path.m	3632	2018-12-17
GAforPathPlaning\main.m	4401	2019-01-10
GAforPathPlaning\mutation.m	1257	2018-12-17
GAforPathPlaning\selection.m	566	2018-12-17
GAforPathPlaning	0	2019-01-12

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