改进沙猫群优化算法的无人机三维路径规划  

UAV 3D path planning based on improved sand cat swarm optimization algorithm

作　　者：贾鹤鸣 王琢 文昌盛 饶洪华 苏媛媛 JIA He-ming;WANG Zhuo;WEN Chang-sheng;RAO Hong-hua;SU Yuan-yuan(Department of Information Engineering,Sanming University,Sanming,Fujian 365004,China)

机构地区：[1]三明学院信息工程学院,福建三明365004

出　　处：《宁德师范学院学报（自然科学版）》2023年第2期171-179,共9页Journal of Ningde Normal University(Natural Science)

基　　金：国家级大学生创新创业训练计划项目(202311311019)。

摘　　要：为提高无人机在复杂三维障碍环境中的路径规划效率及准确性,提出一种融合准反向学习策略的沙猫群优化算法.根据实际情况构建适合本场景的数学模型,再通过成本函数将三维路径规划问题转化为目标函数求解问题,同时考虑无人机的安全性、偏航角和俯仰角等相关约束,从而找出最优飞行路径.仿真实验结果表明,在相同障碍环境下进行多次重复实验,沙猫群优化算法只有约40%概率能够寻找到满足相关约束的最优飞行路径,而融合准反向学习策略的沙猫群优化算法能够准确找出最优飞行路径且在不同的障碍环境下也能够高效寻找最优路径.In order to improve the efficiency and accuracy of path planning of unmanned aerial vehicles in complex three-dimensional obstacle environment,this paper proposes a quasi-reflection learning Sand Cat Swarm Optimization(QRLSCSO)optimization algorithm that integrates quasi-reverse learning strategies.At the same time,we consider the relevant constraints such as the safety,yaw angle and pitch angle of the UAV to find the optimal flight path.The simulation results show that the SCSO algorithm can find the optimal flight path satisfying the relevant constraints only with a probability of about 40%after repeated experiments in the same obstacle environment,while the QRLSCSO algorithm can find the optimal flight path accurately and efficiently in different obstacle environments.

关 键 词：三维路径规划 沙猫群优化算法 准反向学习策略 障碍环境 飞行安全 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]