机器人全覆盖最优路径规划的改进遗传算法

全区域覆盖是一种特殊的路径规划，要求遍历环境中所有的可达区域。目前已经提的许多算法，如模板算法、分块算法等，都只能保证覆盖所有的区域，对于寻找全局最优解却无能为力。提出了一种基于遗传算法的全区域覆盖算法，结合分块算法和模板算法的优点。先采用矩形分解法将环境划分成若干个相邻的子模块，并为每一个子模块选用相应的模板，从而生成覆盖路径，然后采用遗传算法找出最优的路径。算法在虚拟环境中进行了实验，实验结果证明了其可行性和有效性。

Improved Genetic Algorithms optimal area covering path planning for family robot

A special kind of path planning is complete coverage path planning.There are a lot of algorithms on this problem have been developed,e.g.template based,cellular decomposition.But these algorithms just cover the complete area;they are not designed to optimize the process.This paper presents a method of complete coverage path planning based on genetic algorithms, which combine the advantages of cellular decomposition and template algorithm.The environment is divided in sub-regions as in rectangular decomposition method,and then Genetic Algorithms (GA) is used to compute and find the order of the sub-regions and the appropriate template for each region. The algorithm is tested in the virtual environment;the simulation results confirm the feasibility of this method.