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一种基于 AGV 的轻量级组件细分网络的新托盘定位方法 迈向智能仓储

Bin Wu1, Shijie Wang1, Yi Lu1

  • 1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
概括

本研究引入了一种新的轻量级网络,用于在仓库中精确地对托盘进行细分和定位,从而改善自动引导车辆 (AGV) 的运行. 该方法将准确度提高了10%以上,处理速度提高了32%,提高了仓库效率.

关键词:
注意力机制注意力机制组件细分 组件细分 组件细分深度学习是一种深度学习.概括能力 概括能力的能力.托盘定位器的位置

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能

背景情况:

  • 自动引导车辆 (AGV) 由于托盘尺寸不同,在仓库中面临挑战,这阻碍了运营效率.
  • 现有的语义细分模型难以平衡空间细节和高级语义信息,导致冗余计算.

研究的目的:

  • 为精确的托盘细分和本地化提出一个轻量级组件细分网络.
  • 为了解决现有模型在处理各种各样的托盘形状和尺寸的自动挑选方面的局限性.

主要方法:

  • 一个新的轻量级组件细分网络,具有双重注意力机制和编码器-解码器架构.
  • 整合剩余结构以减少网络参数和减轻梯度问题.
  • 使用双分支输入图像来提取多尺度特征以增强细分.

主要成果:

  • 使用有限的注释图像,实现了各种托盘类型的精确细分.
  • 在不同的照明和背景噪音下,在托盘定位方面表现出强性.
  • 与传统模型相比,精度提高了10.41%,图像处理速度提高了32.8%.

结论:

  • 拟议的网络有效地在复杂的仓储环境中对多类别的托盘进行细分和定位.
  • 该方法通过精确的托盘识别和定位来提高AGV的运行效率.
  • 在各种条件下的真实仓储场景中验证了可靠性和性能.