基于PSO-Markov联合模型的无人机路径规划

UAV Path Planning Based on PSO-Markov Joint Model

利用PSO算法对无人机在侦查任务中的路径进行实时规划,将算法与地形、飞行角度、油量等约束条件结合起来,有效地缩减了搜索空间;同时为了在任务中能够更好地掌握无人机完成任务与生存的概率以及整个路径的代价,引入了一种可用于计算风险和评估任务威胁的生存性联合模型,该模型使用了八状态马尔科夫链来描述无人机的状态。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,并以此对整个航路进行评估。

The PSO algorithm is used to realize the real time path planning of the unmanned aerial vehicle in the reconnaissance task.Terrain,flight angle and oil mass and other constraints are combined with the algorithm to reduce the search space effectively.At the same time,in order to grasp the probability of completing a task,and survival rate,as well as the path cost,a kind of survivability joint model is introduced to calculate the risks and to make task threat assessment.The model uses the eight-state markov chain to describe the state of the UAV.The simulation results show that this method can effectively detect the probabilities of various states of the UAV,and then can evaluate the whole path.