基于改进型A*-Markov联合模型的无人机航路规划

UAV route planning based on United markov model and Improved A* algorithm

摘要:

实验一种基于改进型A*算法来对无人机进行任务路径规划.引入了无人机的机动步长、转角限制和飞行高度,有效地缩减了无人机搜索空间,除此之外还引入了地形、任务等约束条件,使航路规划更接近于真实环境.考虑到规划过程中敌方能力的不确定性,以及无人机在航路中的生存概率的不可知性,采用八状态Markov模型评估无人机的飞行状态.该模型与A*算法结合时在威胁区域内完成任务后,由于时间的积累向外扩展时生存代价值越高,导致迭代次数过多,而同心圆模型越向外扩展时生存代价值越低的优点可避免该问题,因此引入同心圆模型与Markov模型的联合模型.仿真实验结果表明,该方法可以有效地检测航路中飞机的各种状态的概率,以此对整个航路进行评估,并且有效降低了仅采用Markov模型时在威胁区域内完成任务点后规划新航路迭代次数过多的问题.

Abstract:

Experimenting an improved A* algorithm is used to plan the mission path of uav. The uav’s step length,the restriction of turning angle and flight height are introduced to effectively reduce the uav’s search space,and constraints such as terrain and mission are introduced to make the route planning closer to the real environment. Considering the uncertainty of the enemy’s ability in the planning process and unpredictability of survival probability of the uav in the route, a markov model was used, which uses eight states markov chain to describe the state of uav. A* algorithm is combined the model in threat area after completed the task,due to the accumulation of time, the outer point’s cost of survival is higger than the inner point, it results too many iterations, but this problem can be avoided by virtue of the lower survival value of concentric circle model, therefore, introducing the united model which combined by the concentric circle model and markov model. Simulation experiment results show that the method can effectively detect route in the probability of various states of the uav and evaluate the whole route, and effectively reduce many iterations only by using the Markov model when planning new route after completing the task.