针对复杂环境下无人机的动态航迹规划问题, 在量子粒子群优化算法基础上, 提出一种RHC-QPSO航迹规划算法。该算法采用四叉树建立实时环境模型, 以QPSO算法为基础进行无人机航迹寻优, 利用卡尔曼滤波对空间中动态威胁进行轨迹预估, 结合RHC方法, 对动态威胁采取主动规避策略, 选取最小化Φ, ψ, θ及a为过程性能指标, 并将其作为每一个滚动优化窗口的优化指标。仿真实验结果表明, 该算法不仅能够实时、有效地完成具有一定先验地图知识下无人机的动态航迹规划, 而且防止无人机在规划过程中为规避动态威胁进行大机动动作, 在一定程度上改善了航迹平滑度, 提高无人机安全性。

An RHC-QPSO path planning algorithm is proposed for the dynamic path planning of UAVs in complex environments based on the quantum particle swarm optimization algorithm.The algorithm uses quadtree to establish the real-time environment model.The UAV path optimization is made based on QPSO algorithm, and Kalman filter is used to estimate the trajectory of dynamic threats in space.Combined with the RHC method, an active avoidance strategy is adopted against the dynamic threats.The minimized Φ, ψ, θ and a are selected as process performance indicators, and used as the optimization indicators for each rolling optimization window.The simulation experiments show that, the algorithm can not only effectively realize the dynamic path planning of UAVs with certain prior knowledge of the map in real time, but also prevent the UAV from making large maneuvering to avoid dynamic threats during the planning process, and the smoothness of the path is improved to some extent, which can improve the safety of the UAV.