一种新的改进A^(*)算法无人机三维路径规划

A New Improved A^(*) Algorithm for UAV 3D Path Planning

传统无人机三维路径规划算法具有搜索空间复杂、威胁种类多、自身约束复杂等特点,为解决搜索路径绕路,转向点多的问题,提出了一种改进的A^(*)算法。首先,对搜索方向进行自适应处理,缩减搜索空间,提高搜索效率;其次,将路径危险程度以概率的形式加入估价函数,使规划后的路径避开危险区;再次,采用贪婪搜索法优化路径,删除冗余节点;最后,设计了包含雷达、防空武器、干扰设备以及障碍物分布区4种威胁的对比仿真实验。仿真结果表明,改进后的A^(*)算法与传统A^(*)算法相比,规划路径长度减少15.93%,转向点数减少81.82%;与遗传算法相比,路径规划长度减少9.08%,转向点数减少66.67%,验证了改进算法的有效性。

The traditional UAV 3Dpath planning algorithm has the characteristics of complex search space,many kinds of threats and complex self-constraints,etc.To deal with these problems and reduce the redundant turning points of the planned path,we proposed an improved A^(*) algorithm.Firstly,the search direction is adaptively processed to reduce the search space and improve the search efficiency;secondly,the path danger degree is added to the valuation function in the form of probability,so that the planned path avoids the danger zone;again,the greedy search method is used to optimize the path and delete the redundant nodes.Finally,a comparative simulation experiment containing four threats:radar,antiaircraft weapons,jamming devices and obstacle distribution zones was designed.The simulation results show that compared with the traditional A^(*) algorithm,the planned path length of the improved A^(*) algorithm is reduced by 15.93%and the number of steering points is reduced by 81.82%;Compared with genetic algorithm,the planned path length is reduced by 9.08% and the number of steering points is reduced by 66.67%,which verifies the effectiveness of the improved algorithm.