摘要
针对无人机在复杂任务空间执行作战任务过程中的航路规划问题,提出了一种对于快速扩展随机树算法(Rapidly-exploring Random Trees,RRT)的综合改进航路规划算法。为了提高算法的收敛速度,加快生成航路的时间并减少扩展树分支,在传统RRT算法中加入了引力场引导随机树节点向目标点的扩展。同时针对该算法中固定生长步长下生成随机树冗余节点较多、生成航路曲折的问题,使用了动态生长步长策略对算法进行改进。仿真实验结果表明,与传统RRT算法相比较,综合改进的RRT算法在搜索时间上节省了80%,搜索路径节点个数减少了50%,且在航路平滑性等性能中有明显提升,算法整体性能更优。
Aiming at the path planning problem of unmanned aircraft in the process of executing combat missions in complex mission space,a comprehensive algorithm based on the traditional Rapidly-Exploring Random Trees(RRT) algorithm was proposed. In order to improve the convergence speed of the algorithm,speed up the time of generating the path and reduce the branch of the expanded tree,a gravitational field was added to the traditional RRT algorithm to guide the expansion of random tree nodes to the target point. At the same time,in view of the problem of many redundant nodes and tortuous paths in the random tree generated under the fixed growth step size,the algorithm was improved by using the dynamic growth step size strategy. The simulation results show that compared with the traditional RRT algorithm,the comprehensively improved RRT algorithm saves 80% in search time,reduces the number of search path nodes by 50%,and has significant improvement in the performance of path smoothness.The overall performance of the algorithm is better.
作者
郭莹婷
林川
GUO Yingting;LIN Chuan(The 15th Research Institute of China Electronics Technology Group Corporation,Beijing 100083,China)
出处
《电子设计工程》
2022年第20期56-60,共5页
Electronic Design Engineering
作者简介
郭莹婷(1997-),女,安徽淮北人,硕士研究生。研究方向:指挥信息系统。
