一种基于改进人工蜂群算法的无人机航迹规划方法
An UAV Path Planning Method Based on Improved Artificial Bee Colony Algorithm
周枫 1王卫东1
作者信息
- 1. 江苏科技大学计算机学院 镇江 212100
- 折叠
摘要
针对无人机在多障碍的环境下,如何高效准确的寻找到最优或较优航迹,提出了一种改进人工蜂群算法(GWOABC).首先根据无人机航迹规划环境构建数学模型用于仿真模拟.其次,融合灰狼算法思想、并且引入一种新的动态评价机制、引入柯西变异策略对传统人工蜂群算法的搜索规则、蜜源选择方式进行改进.最后为验证该方法的有效性,进行20次对比实验.实验结果表明,该方法在处理航迹规划任务时,其收敛速度、收敛精度和鲁棒性均优于对比算法;并且该方法规划出的航迹更优、安全性更好.
Abstract
To find the optimal path of UAV in multi obstacle environment efficiently and accurately,an improved artificial bee colony algorithm(GWOABC)is proposed.Firstly,a mathematical model is constructed according to the UAV path planning environ-ment for simulation.Secondly,in order to improve traditional artificial bee colony algorithm's search rules and honey source selec-tion method,GWOABC algorithm introduces the idea of Gray Wolf algorithm,a new dynamic evaluation rule and cauchy mutation strategy.Finally,in order to verify the effectiveness of GWOABC algorithm,20 comparative experiments are carried out.The experi-mental results show GWOABC algorithm's convergence speed,convergence accuracy and robustness are better than those of the comparison algorithm,and the path planned by GWOABC algorithm is better and safer.
关键词
无人机/灰狼算法/人工蜂群算法/柯西变异/航迹规划Key words
UAV/gray wolf algorithm/artificial bee colony algorithm/Cauchy mutation/path planning引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据