基于Markov模型及A~*算法的平台航路规划

Platform Route Planning Based on Markov Model and A~* Algorithm

通过实验一种改进的A*算法,将平台执行任务时约束条件转化为生存代价问题。飞行时,对路径中的雷达、武器区域,采用未发现、发现、跟踪、对战、击中5种状态进行描述,根据飞行过程中每个状态之间的转移概率计算生存代价,与算法本身的适应度函数结合,设置新的适应度函数,从而在符合路径长度等约束条件下寻最优路径。实验结果最终表明,相对于传统算法,改进后算法搜索路径更优,且能实时获取平台状态。

Through experiments,an improved A＊ algorithm is used to transform the constraints of the platform into the cost of survival problem. The flight path of the radar and weapon areas,using Undetected,Detected,Tracking,Engaged and Hit five states to be described. The survival cost is Calculated according to the transition probability between each state during the flight,and the algorithm itself with the fitness function,setting up a new fitness function,so as to find the optimal path in line with the path length constraint conditions. Experimental results show that,compared with the traditional algorithm,the improved algorithm has better search path and real-time access to the platform state.