一种面向未知环境的无人机群区域覆盖搜索算法研究

Research on Area Coverage Search Algorithm for UAVs in Unknown Environment

近年来,随着无人机技术的飞速发展,旋翼无人机由于具有灵活机动、轻量化、成本低等优点在搜救领域得到了广泛应用。本文面向未知环境研究无人机群执行区域覆盖搜索任务,以任务耗时最短为算法评价指标,提出了回字形扩展搜索算法。首先对传统区域覆盖搜索算法和本文所提出的算法进行了介绍,之后针对算法建立了仿真环境模型和算法模型,并基于NetLogo仿真环境通过蒙特卡罗方法进行了试验及结果分析。与传统随机游走覆盖搜索算法进行对比,结果显示本文提出的基于搜索图的协同模式下的回字形扩展覆盖搜索算法区域覆盖耗时短、重叠率低,具有分布式、自主性、在线实时规划、抗毁性等特点。

In recent years,with the rapid development of UAV technology,rotor UAV has been widely used in the field of search and rescue due to its advantages of flexibility,light weight and low cost.In this paper,the area coverage search task of UAV swarm is studied for unknown environment.Taking the shortest task time as the evaluation index of the algorithm,a backtracking extended search algorithm is proposed.Firstly,the traditional area coverage search algorithm and the algorithm proposed in this paper are introduced.Then the simulation environment model and algorithm model are established for the algorithm,and the experiment and result analysis are carried out by Monte Carlo method based on Net Logo simulation environment.Compared with the traditional random walk coverage search algorithm,the results show that the Hui extended coverage search algorithm based on the search graph cooperative mode proposed in this paper has short coverage time and low overlap rate.It has the characteristics of distribution,autonomy,online real-time planning and invulnerability.