改进人工蜂群算法的无人机航迹规划研究

Research on Path Planning of UAV Based on Improved ABC Algorithm

如何快速地规划出满足约束条件的飞行航迹,是实现无人机自主飞行的关键。将改进的人工蜂群算法应用于求解无人机航迹规划问题,同时在人工蜂群算法的侦察阶段引入差分进化算法的思想。通过仿真实验并与标准人工蜂群算法比较,结果表明此算法能够有效加快收敛速度,提高最优航迹精度,是解决航迹规划和其他高维复杂函数优化的有效方法。

It is critical for autonomous planning of Unmanned Aerial Vehicles （UAV） that how to plan the flight path quickly which fulfills some constraints. This paper applies improved Artificial Bee Colony （ABC） algorithm to solve the problem of UAV path planning and introduce the idea of Differential Evolution （DE） algorithm in the scout bee phase of ABC algorithm. Compared with the standard ABC algorithm,it shows that this algorithm can effectively accelerate the convergence speed and improve the accuracy of the optimal path through the simulation experiments ,which proves the feasibility,effectiveness and robustness of this method on the path planning and other high-dimensional complex function optimization.