面向循环医疗保健的热力学深度学习基于视觉的灵活机器人细胞
Federico Zocco1, Denis Sleath1, Shahin Rahimifard1
1Centre for Sustainable Manufacturing and Recycling Technologies, Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Ashby Road, Loughborough, LE11 3TU England UK.
概括
这项研究引入了深度学习驱动的机器人细胞,用于循环医疗,自动化医疗设备再处理. 一个新的热力学框架增强了材料流分析,以改善循环经济的建模和评估.
科学领域:
- 机器人和自动化
- 循环经济
- 热力学
背景情况:
- 线性经济面临资源枯竭和浪费,尤其是在医疗保健领域.
- 医疗废物再加工可能会造成污染.
- 循环经济模型缺乏基于物理的材料流分析方法.
研究的目的:
- 开发一个灵活的机器人单元来自动化循环医疗保健任务:资源映射,拆卸和医疗设备废物分类.
- 将机器人与系统层面的视角结合起来, 使用一种新的热力学框架.
- 为评估材料流效率提出新的循环性指标.
主要方法:
- 开发一种深度学习视觉支持的机器人细胞,用于自动化医疗设备再处理.
- 将区间动态热力学与机器人力学集成为系统级建模.
- 应用图形理论和热力学框架来开发循环指标.
主要成果:
- 一个灵活的机器人细胞,能够绘制,拆卸和分类小型医疗设备 (例如葡萄糖计,吸入器).
- 一个热力学框架,通过动态能量平衡和系统动态来增强材料流分析.
- 两个拟议的循环性指标来评估处理时间和输出分离效率.
结论:
- 开发的机器人细胞和热力学框架提供了基于物理的方法来推进医疗保健的循环经济.
- 该系统在模拟循环材料流中提供了更高的准确性和可重复性.
- 循环性指标有助于医疗管理人员评估机器人再处理系统的效率.
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