Abstract
Aiming at the rapid planning of the optimal flight path of the intelligent aircraft, considering the error constraints and correction probability constraints, a model for intelligent aircraft path planning under multiple constraints is constructed, and a global search algorithm based on Dijkstra algorithm is proposed to solve the model. By calculating the residual error and restricts flight distance, the basic Dijkstra algorithm is improved to make it more adaptable to solve the path planning under multiple constraints. At the same time, simulation experiment is conducted with the optimal goal of the shortest track length and satisfying the error constraints. The experimental results show that the aircraft passed a total of 18 correction points when it reached the destination. The total track length was 144 287.932 m, the vertical position error was 17.254 units, and the horizontal position error was 6.420 units. The results meet the error requirements. The results show that the intelligent aircraft path planning model and Dijkstra-based global search algorithm with multiple constraints are reasonable in solving such problems.
Highlights
The results show that the intelligent aircraft path planning model and Dijk⁃ stra⁃based global search algorithm with multiple constraints are reasonable in solving such problems
Beijing: Beijing Institute of Technology, 2016 ( in Chinese)
Summary
∑ ìïïEk,v ei,v i=1 (∑ ) í îïïEk,h = min k ei,h ,5 (4) 根据误差随距离的积累规律,将误差约束转化 为距离约束。 结合点集与权值思想,并对比 O⁃D 距 离矩阵,通过循环迭代得到距离约束矩阵,最终以距 离约束矩阵为基础,设计基于 Dijkstra 的全局搜寻 算法。 2.1 剩余误差计算 选择集合 Q 中首个元素 pk, 提取其对应权值 d(pk), 修正最短路径权值:d(pj) = min{d(pj), d( pk) + ykj} ; Step4 更新遍历点集 单次迭 代 结 束, 还原距离约束矩阵Y, 令 yij = wij; Step6 循环迭代 搜寻算法,设置相关参数( 参数取值可进行相关调 整) ,如表 2 所示;同时由 MATLAB 随机函数生成起 点、终点以及 325 个误差校正点坐标,部分坐标值如 表 3 所示。
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