本文主要解决无人机在复杂环境下最短路径下规划问题。此本文主要采用遗传算法和最短路径算法等机器学习算法,研究无人机的最佳航线,解决基于遗传算法下无人机路径规划问题。最后本文对模型进行了误差分析,采用距离判别法对上述分类问...本文主要解决无人机在复杂环境下最短路径下规划问题。此本文主要采用遗传算法和最短路径算法等机器学习算法,研究无人机的最佳航线,解决基于遗传算法下无人机路径规划问题。最后本文对模型进行了误差分析,采用距离判别法对上述分类问题进行随机检验,检验结果表明,本文所采用的模型算法是合理且有效的,据此将模型进行评价和推广。This paper primarily addresses the problem of path planning for drones in complex environments, focusing on the optimization of the shortest route. Various machine learning algorithms, including genetic algorithms and shortest path algorithms, are employed to investigate the optimal flight path for drones, offering a solution to the drone path planning problem based on genetic algorithms. Finally, the paper conducts an error analysis of the model, utilizing the distance discrimination method for random verification of the classification problem. The verification results indicate that the model algorithms employed in this paper are both reasonable and effective. Based on this, the model is evaluated and recommended for further application and extension.展开更多
文摘本文主要解决无人机在复杂环境下最短路径下规划问题。此本文主要采用遗传算法和最短路径算法等机器学习算法,研究无人机的最佳航线,解决基于遗传算法下无人机路径规划问题。最后本文对模型进行了误差分析,采用距离判别法对上述分类问题进行随机检验,检验结果表明,本文所采用的模型算法是合理且有效的,据此将模型进行评价和推广。This paper primarily addresses the problem of path planning for drones in complex environments, focusing on the optimization of the shortest route. Various machine learning algorithms, including genetic algorithms and shortest path algorithms, are employed to investigate the optimal flight path for drones, offering a solution to the drone path planning problem based on genetic algorithms. Finally, the paper conducts an error analysis of the model, utilizing the distance discrimination method for random verification of the classification problem. The verification results indicate that the model algorithms employed in this paper are both reasonable and effective. Based on this, the model is evaluated and recommended for further application and extension.
