针对UVC辐射的覆盖计划:基于群体智能算法的机器人表面消毒
Peiyao Guo1, Dekun Luo1, Yizhen Wu1
1Research Center for Optoelectronic Materials and Devices, Guangxi Key Laboratory for the Relativistic Astrophysics, School of Physical Science & Technology, Guangxi University, Nanning 530004, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究引入了一种针对自主机器人的新型紫外线剂量覆盖计划框架,优化能源使用并减少消毒任务的冗余性. 该方法提高了机器人消毒的效率,以消除病原体.
科学领域:
- 机器人和自动化 机器人和自动化
- 传染病控制和控制传染病
- 摄影化学的使用.
背景情况:
- 紫外线 (UV) 辐射是病原体消毒的关键策略.
- 自主移动机器人提高了消毒覆盖率,但面临着路径规划的复杂性.
- 高紫外线剂量用于无活化导致能源效率低下和剂量冗余.
研究的目的:
- 为机器人消毒开发一个优化的紫外线剂量覆盖范围规划框架.
- 解决大规模消毒中的能源消耗和剂量冗余挑战.
- 提高自主移动机器人在病原体灭活方面的效率.
主要方法:
- 使用了一个多目标优化模型,用MOPSO (多目标粒子集群优化) 来解决.
- 在规划框架中整合了UV-C光效率系数.
- 专注于剂量积累特征,以优化辐射规划.
主要成果:
- 拟议的框架有效地规划了紫外线剂量覆盖范围,减轻了冗余.
- 对机器人消毒的能源开支显著减少.
- 经验试验证实了该模型在使用自主机器人禁用病毒方面的有效性.
结论:
- 新的剂量覆盖计划框架提高了机器人消毒的效率,并降低了能源消耗.
- 这种方法通过优先考虑剂量积累和光效率来优化紫外线消毒.
- 该研究为大规模的节能机器人消毒提供了可行的解决方案.
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