基于改进蛇优化算法的无人机路径规划研究

Research on UAV path planning based on improved snake optimization algorithm

为提高无人机在复杂山区环境中飞行的可靠性和安全性,提出了一种改进蛇优化算法的无人机路径规划方法。首先,结合数字高程信息和复杂地形威胁构建了无人机环境模型和山峰威胁模型;其次,提出改进蛇优化算法,将传统蛇优化算法与元胞自动机进行融合用于无人机路径规划,并引入小生境技术和最优局部抖动,避免算法陷入局部最优,提高全局搜索能力;最后,在3种场景下进行仿真实验验证所提方法的有效性。实验结果表明,所提方法在3种复杂场景下平均路径长度分别为2.201、1.801和2.187 km,平均收敛时间为14.8、13.9和14.9 s,与其他路径规划算法相比具有良好的优越性,且所生成的路径对真实无人机运行具有良好的实际效果。

To improve the reliability and safety of unmanned aerial vehicles(UAV)flying in complex mountainous environments,an improved snake optimization algorithm for UAV path planning is proposed.Firstly,a drone environment model and a mountain peak threat model were constructed by combining digital elevation information and complex terrain threats.Secondly,an improved snake optimization algorithm is proposed,which integrates traditional snake optimization algorithms with cellular automata for unmanned aerial vehicle path planning.The niche technology and optimal local jitter are introduced to avoid the algorithm falling into local optima and improve global search ability.Finally,simulation experiments are carried out in three scenarios to verify the effectiveness of the proposed method.The experimental results show that the average path length of the proposed method is 2.201,1.801 and 2.187 km respectively in three complex scenarios,and the average convergence time is 14.8,13.9 and 14.9 s.Compared with other path planning algorithms,the generated path has good practical effect on real UAV operation.