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一个增强的人工阻碍算法及其在无人机路径规划中的应用

Xuemei Zhu¹, Chaochuan Jia², Jiangdong Zhao¹

¹Experimental Training Teaching Management Department, West Anhui University, Yu'an District, Lu'an 237012, China.

Biomimetics (Basel, Switzerland)

|June 25, 2025

概括

一个增强的人工 lemming 算法 (EALA) 改善了无人机系统 (UAS) 的路径规划. 这种先进的方法确保在复杂的3D环境中更安全,更快,更有效地生成飞行路径.

科学领域:

人工智能的人工智能
机器人技术机器人技术机器人技术
航空航天工程航空航天工程

背景情况:

在3D环境中无人机系统 (UAS) 路径规划带来了重大的计算挑战.
现有的算法往往难以避免复杂的障碍,同时优化多个路径目标.

研究的目的:

为高级UAS路径规划引入一个增强的人工阻碍算法 (EALA).
为了改善勘探-开采平衡和优化算法的本地改进.

主要方法:

将混乱初始化,自适应性扰动和混合突变纳入人工阻碍算法 (ALA).
使用IEEE CEC2017和CEC2022基准函数进行验证.
适用于具有现实的限制的大型和中型3D无人机系统路径规划场景.

主要成果:

与标准ALA和其他10个基准函数算法相比,EALA表现出更快的融合和更高的性能.
EALA生成了帕雷托最佳路径,最大限度地减少了长度,曲率和计算时间.
即使在复杂的障碍物配置中,也可以保证无碰撞路径.

结论:

在UAS路径规划能力方面,EALA提供了显著的进步.
该算法对于要求严格的安全性和时间遵守的关键任务应用程序非常有效.
EALA为复杂的现实世界无人机操作环境提供了强大的解决方案.

关键词:

3D轨道优化优化 3D轨道优化无人机路径规划无人机路径规划适应性突变是一种适应性突变.人工虫算法的人工虫算法混乱的初始化初始化进行元启发式优化优化.

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