基于改进遗传算法的无人机搜索路径规划的研究

Research on UAV Search Path Planning Based on Improved Genetic Algorithm

摘要: 本文针对无人机搜索目标时移动速度的大小和方向不确定的问题，设计了基于改进遗传算法的搜索路径规划算法。传统的基于解析的办法适应性不强，不便于计算机自动化计算的实现。本文提出了基于改进遗传算法的搜索路径规划算法，将目标移动方向的概率时间等参数作为动态的人工势场综合考虑，提高了搜索路径规划问题求解算法的适应性，对于复杂的搜索区域和目标移动概率，均具有较高的适应性和自动化程度。根据遗传算法中适者生存的思想，择优选出更加优化的搜索路径。在这个过程中，将无人机的路径点当作生物的基因，把搜索路径看作是生物的染色体，然后通过杂交、选择、变异等操作得到更加优化的搜索路径。设置算例进行仿真计算，由仿真计算结果可知，本文提出的基于改进遗传算法的搜索路径规划算法是可行的，并且具有灵活性强、效率高和自动化的优点。

Abstract: Aiming at the problem of UAV searching for the target with uncertain moving speed and direction, this paper designs a search path planning algorithm based on improved genetic algorithm. The traditional method based on analysis has not strong adaptability and is not convenient for the realization of computer automatic calculation. In this paper, a search path planning algorithm based on improved genetic algorithm is proposed, which considers the probability time and other parameters of the target moving direction as a dynamic artificial potential field, improves the adaptability of the algorithm for solving the search path planning problem, and has a high degree of adaptability and automation for the complex search area and the target moving probability. According to the idea of survival of the fittest in genetic algorithm, a more optimal search path is selected. In this process, the path point of UAV is regarded as the gene of organism; the search path is regarded as the chromosome of organism; and then the search path is optimized through hybridization, selection, mutation and other operations. Then, an example is set up for simulation calculation. From the simulation results, we can see that the search path planning algorithm based on the improved genetic algorithm is feasible, and has the advantages of flexibility, efficiency and automation.