针对无人机在三维路径规划时存在搜素范围和寻优性能不足等问题,以及现有黏菌算法(SMA)寻优精度不足, 易陷入局部最优的缺陷,提出了一种基于自适应黏菌算法(GSMA)优化的无人机三维路径规划方法.首先,根据无人机经过的实际环境,建立三维地形、威胁源和无人机自身约束条件;其次,针对搜素范围不足的问题,设计改进的Logistic混沌映射增加种群的多样性并扩大搜索范围,提升了SMA的全局搜索能力;然后,设计一种非线性自适应惯性权重因子,将线性收敛方式改进为非线性收敛,利用权重值更新黏菌位置,提高了收敛速度;最后,在算法后期中设计自适应柯西变异,增大了黏菌的搜索空间,寻优精度也得到了提高.实验结果表明,GSMA相比于灰狼优化(GWO)算法、SMA和海鸥算法(SOA)3种算法,路径更短且更平滑,收敛速度更快,寻优精度更高,同时能耗更低,进一步提升了无人机的路径规划能力.

Aimed at the problems of insufficient search range and optimization performance in 3D path planning of unmanned aerial vehicles (UAVs), and the lack of optimization accuracy of the existing slime mould algorithm (SMA), which is easy to fall into local optimization, a 3D path planning method for UAV based on adaptive slime mould algorithm optimization is proposed. First, according to the actual environment that the UAV passes through, the 3D terrain, the threat source and the constraints of the AUV were established. Next, for the problem of insufficient search range, an improved Logistic chaotic map is designed to increase the diversity of the population and expand the search range, which improves the global search ability of SMA. Then, a nonlinear adaptive inertia weight factor is designed to change the linear convergence method into nonlinear convergence, and the weight value is used to update the position of the slime mould, which improves the convergence speed. Finally, in the later stage of the algorithm, the adaptive cauchy mutation is designed, which increases the search space of the slime mould and improves the optimization accuracy. The experimental results show that GSMA has a shorter and smoother path, a faster convergence, a higher optimization accuracy, and a lower energy consumption compared with the gray wolf optimizer (GWO) algorithm, the SMA, and the seagull algorithm (SOA), which further improves the path planning capability of the UAV.