基于RRT的无人机特征点覆盖搜索算法优化

UAV Feature Points Coverage Searching Algorithm Optimization Based on RRT

为提高无人机对特定目标点的覆盖搜索效率,设计一种无人机特征点覆盖搜索算法。首先采用一般的“Z”字型搜索方式确认大致搜索范围,并且以此设置转弯起点、终点及搜索障碍物,然后使用经引入引力分量优化后的快速拓展随机树(RRT)算法产生搜索路径,最后对路径进行圆弧化处理产生最终路径,完成针对特征点的区域覆盖。算法实现与理论分析结果表明,该无人机特征点覆盖搜索算法将“Z”字型搜索与RRT快速随机搜索树方法进行集成优化,能较为高效地完成对给定区域特征点的搜索覆盖。

In order to improve the efficiency of unmanned aerial vehicle(UAV)coverage searching for specific target points,a UAV feature point coverage searching algorithm is designed.The proposed algorithm first uses the general“Z”search method to confirm the approximate search range,and then sets the turning beginning points,ending points and searching barriers,then uses the rapidly-exploring random tree(RRT)algorithm which has introduced the gravity component optimization to generate the search path,and finally generates the final path through the path arc processing to complete the area coverage for the feature points.The results of the algorithm implementation and theoretical analysis show that the proposed UAV feature point coverage search algorithm integrates the“Z”search and RRT fast random search tree,and can effectively complete the search coverage of the given area feature points.