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适应性群优化与子群和模糊逻辑为3D激光扫描路径规划的3D激光扫描路径规划

Junfang Song¹, Yuanyuan Pu¹, Xiaoyu Xu¹

¹College of Information Engineering, Xizang Minzu University, No. 6, East Section of Wenhui Road, Weicheng District, Xianyang 712082, China.

Sensors (Basel, Switzerland)

|February 24, 2024

概括

本研究介绍了一种新的3D表面激光扫描路径规划技术,使用自适应性群优化与子群和模糊逻辑 (SFACO) 来提高测量精度和效率.

科学领域:

计量学和测量科学计量学和测量科学
计算机视觉和机器人技术
人工智能和优化的优化

背景情况:

精确测量复杂的表面需要精确的定位和路径规划的激光传感器探针.
目前用于激光扫描路径规划的方法往往难以同时优化测量速度和精度.

研究的目的:

提出一种新的3D表面激光扫描路径规划技术,称为SFACO (适应性群体优化与子群体和模糊逻辑).
通过优化激光传感器探针态度和扫描路径来提高复杂表面测量的精度和效率.

主要方法:

使用一个四坐标测量机和一个点激光移位传感器探头.
建立一个坐标系统并转换测量以确定最佳的测量态度.
开发一个模糊的群优化 (ACO) 算法,具有适应性子群和动态域结构 (SFACO).
创建一个名义距离矩阵,基于最佳的测量态度,用于路径规划.

主要成果:

SFACO算法在解决3D表面激光扫描路径规划问题的有效性得到了证明.
使用13个流行的旅行销售员问题 (TSP) 基准数据集进行实验验证,证实了算法的复杂性和有效性.
拟议的方法成功地整合了测量点布局,探头态度和路径规划,以获得更好的结果.

结论:

SFACO算法为优化3D表面激光扫描路径规划提供了一个强大的解决方案.
模糊逻辑和自适应子群体的集成显著提高了殖民地优化对此应用程序的性能.
这种技术为更准确,更高效的复杂表面计量学提供了基础.

关键词:

殖民地优化殖民地优化模糊的逻辑模糊的逻辑复杂的表面测量方法激光扫描扫描激光扫描的使用方法扫描路径规划扫描路径规划

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