户外仓库管理:无人机驱动的精密跟踪堆叠的钢
Assia Belbachir1, Antonio M Ortiz1, Erik T Hauge1
1NORCE Research AS, Grimstad, Norway.
概括
本研究介绍了一种无人机系统,用于使用QR码进行产品本地化,为仓库和工业场所提供无基础设施的解决方案. 它实现了高精度,即使在户外,增强自动化库存管理.
科学领域:
- 机器人和自动化机器人与自动化
- 计算机视觉 计算机视觉
- 物流和供应链管理的物流和供应链管理.
背景情况:
- 户外产品识别面临着诸如可变照明,天气和缺乏固定基础设施等挑战.
- 像GPS和RFID这样的现有方法通常需要外部基础设施,限制在非结构化环境中的可扩展性.
研究的目的:
- 开发基于视觉的无人驾驶飞行器 (UAV) 系统,用于在户外工业环境中自主定位产品.
- 通过QR码检测和相对空间关系,创建一个无基础设施的方法来准确定位产品.
主要方法:
- 无人机自主扫描区域,捕获视频以检测和提取QR码.
- 产品之间的空间关系是使用可信度图表来计算的,以提高可靠性.
- 该系统依赖相对定位,消除了对GPS或RFID等外部基础设施的需求.
主要成果:
- 拟议的算法在室内设置中实现了超过94%的定位精度,在室外环境中达到80%.
- 即使在遮蔽和不同的照明条件下,也保持了高精度.
- 该系统在现实世界工业场景中展示了可扩展性和适应性.
结论:
- 开发的无人机系统为在具有挑战性的环境中实现产品本地化提供了一种新的,无基础设施的方法.
- 整合可信度评分可以提高产品位置检测的可靠性.
- 这项技术在增强物流和仓库库存管理中的自动化方面具有重大潜力.
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