基于DBN威胁评估的MPC无人机三维动态路径规划

模型预测控制(model predictive control， MPC)路径规划算法适用于三维动态环境下的无人机(unmanned aerial vehicle, UAV)路径规划；动态贝叶斯网络(dynamic Bayesian network， DBN)能够有效推理战场态势，对无人机进行威胁评估。针对威胁尾随无人机时的路径规划问题，构建DBN威胁评估模型，将UAV在战场环境中的威胁态势用威胁等级概率表示，与MPC路径规划算法相结合，得到基于DBN威胁评估的MPC UAV路径规划算法。通过多组仿真分析表明，在三维动态环境下，特别是威胁尾随无人机时，基于DBN威胁评估的MPC无人机路径规划算法可以得到有效的无人机路径。

MPC three-dimensional dynamic path planning for UAV based on DBN threat assessment

The model predictive control （MPC） path planning algorithm can solve the problem of dynamic unmanned aerial vehicle (UAV) path planning. Dynamic Bayesian network （DBN） is an effective tool for reasoning and threat assessment. Considering the problem of path planning when the dynamic threat tags the UAV, the MPC path planning algorithm combined with DBN threat assessment is presented, which used the threat lever probability to describe the threat situation. A group of simulations demonstrate the efficiency of MPC three dimensional dynamic path planning algorithm for UAV based on DBN threat assessment especially when the dynamic threat tags the UAV.