摘要
为更有效解决无人机三维路径规划的优化问题,提出一种改进的蜜獾算法,在挖掘阶段和采蜜阶段之后加入透镜成像反向学习策略,增强了算法的探索能力,使得算法能够更快地收敛。通过指数函数生成不同数量的障碍物并模拟不同的飞行场景,增强了地形仿真的多样性;同时通过行程代价、环境的地形代价和边界代价作为目的函数,提高了无人机航行的稳定性。最后通过实验仿真很好地验证了改进的蜜獾算法在该问题中的有效性和实用性。
In order to solve the optimization problem of UAV three-dimensional path planning more effectively,an improved honey badger algorithm is proposed.After the mining stage and the honey collecting stage,the lens imaging reverse learning strategy is added to enhance the exploration ability of the algorithm and make the algorithm converge faster.Different numbers of obstacles are generated by exponential function and different flight scenarios are simulated,which enhances the diversity of terrain simulation.At the same time,the stability of UAV navigation is improved by taking the travel cost,the terrain cost of the environment and the boundary cost as the objective function.Finally,the effectiveness and practicability of the improved honey badger algorithm in this problem are verified by experimental simulation.
作者
贾鹤鸣
饶洪华
王琢
文昌盛
苏媛媛
JIA Heming;RAO Honghua;WANG Zhuo;WEN Changsheng;SU Yuanyuan(Sanming University,Sanming,Fujian 365004,China)
出处
《龙岩学院学报》
2022年第5期18-25,共8页
Journal of Longyan University
基金
福建省自然科学基金项目(2021J011128)
福建省大学生创新创业训练计划项目。
