防灾工厂巡检智能机器人视觉系统

Saifuddin Mahmud, Justin Dannemiller, R. Sourave, Xiangxu Lin, Jong-Hoon Kim
{"title":"防灾工厂巡检智能机器人视觉系统","authors":"Saifuddin Mahmud, Justin Dannemiller, R. Sourave, Xiangxu Lin, Jong-Hoon Kim","doi":"10.1109/IRC55401.2022.00079","DOIUrl":null,"url":null,"abstract":"Simulation of emergency response scenarios and routine inspections are imperative means in ensuring the proper functioning and safety of power plants, oil refineries, iron works, and industrial units. By utilizing autonomous robots, moreover, the reliability and frequency of such inspections can be improved. With the exception of facilities located in hazardous areas, such as off-shore factories, where dispatching response teams might be impossible, accidents caused by human mistakes can be prevented by autonomous inspections and diagnosis of facilities (pumps, tanks, boilers, and so on). One of the primary obstacles in robot-assisted inspection operations is detecting various types of gauges, reading them, and taking appropriate action. This study describes a unique robot vision-based plant inspection system that may be used to enhance the frequency of routine checks and, in turn, minimize equipment faults and accidents (explosions or fires caused by gas leaks) caused by human mistakes or natural degradation. This suggested system can conduct facility inspections by detecting and reading a variety of gauges and issuing reports upon the detection of any anomalies. Furthermore, this system is capable of responding to unforeseen anomalous events that pose potential harm to human response teams, such as the direct manipulation of valves in the presence of a gas leak.","PeriodicalId":282759,"journal":{"name":"2022 Sixth IEEE International Conference on Robotic Computing (IRC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Smart Robot Vision System for Plant Inspection for Disaster Prevention\",\"authors\":\"Saifuddin Mahmud, Justin Dannemiller, R. Sourave, Xiangxu Lin, Jong-Hoon Kim\",\"doi\":\"10.1109/IRC55401.2022.00079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Simulation of emergency response scenarios and routine inspections are imperative means in ensuring the proper functioning and safety of power plants, oil refineries, iron works, and industrial units. By utilizing autonomous robots, moreover, the reliability and frequency of such inspections can be improved. With the exception of facilities located in hazardous areas, such as off-shore factories, where dispatching response teams might be impossible, accidents caused by human mistakes can be prevented by autonomous inspections and diagnosis of facilities (pumps, tanks, boilers, and so on). One of the primary obstacles in robot-assisted inspection operations is detecting various types of gauges, reading them, and taking appropriate action. This study describes a unique robot vision-based plant inspection system that may be used to enhance the frequency of routine checks and, in turn, minimize equipment faults and accidents (explosions or fires caused by gas leaks) caused by human mistakes or natural degradation. This suggested system can conduct facility inspections by detecting and reading a variety of gauges and issuing reports upon the detection of any anomalies. Furthermore, this system is capable of responding to unforeseen anomalous events that pose potential harm to human response teams, such as the direct manipulation of valves in the presence of a gas leak.\",\"PeriodicalId\":282759,\"journal\":{\"name\":\"2022 Sixth IEEE International Conference on Robotic Computing (IRC)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Sixth IEEE International Conference on Robotic Computing (IRC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRC55401.2022.00079\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Sixth IEEE International Conference on Robotic Computing (IRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRC55401.2022.00079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

应急情景模拟和例行检查是确保电厂、炼油厂、铁厂和工业单位正常运行和安全的必要手段。此外,通过使用自主机器人,可以提高此类检查的可靠性和频率。除了位于危险区域的设施(如海上工厂)可能无法派遣响应小组之外,可以通过对设施(泵、储罐、锅炉等)的自动检查和诊断来防止人为错误造成的事故。机器人辅助检测操作的主要障碍之一是检测各种类型的仪表,读取它们,并采取适当的行动。本研究描述了一种独特的基于机器人视觉的工厂检查系统,可用于提高例行检查的频率,从而最大限度地减少由人为错误或自然退化引起的设备故障和事故(由气体泄漏引起的爆炸或火灾)。该建议系统可以通过检测和读取各种仪表来进行设施检查,并在检测到任何异常时发出报告。此外,该系统还能够应对不可预见的异常事件,这些事件可能对人类响应团队造成潜在危害,例如在气体泄漏时直接操纵阀门。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Smart Robot Vision System for Plant Inspection for Disaster Prevention
Simulation of emergency response scenarios and routine inspections are imperative means in ensuring the proper functioning and safety of power plants, oil refineries, iron works, and industrial units. By utilizing autonomous robots, moreover, the reliability and frequency of such inspections can be improved. With the exception of facilities located in hazardous areas, such as off-shore factories, where dispatching response teams might be impossible, accidents caused by human mistakes can be prevented by autonomous inspections and diagnosis of facilities (pumps, tanks, boilers, and so on). One of the primary obstacles in robot-assisted inspection operations is detecting various types of gauges, reading them, and taking appropriate action. This study describes a unique robot vision-based plant inspection system that may be used to enhance the frequency of routine checks and, in turn, minimize equipment faults and accidents (explosions or fires caused by gas leaks) caused by human mistakes or natural degradation. This suggested system can conduct facility inspections by detecting and reading a variety of gauges and issuing reports upon the detection of any anomalies. Furthermore, this system is capable of responding to unforeseen anomalous events that pose potential harm to human response teams, such as the direct manipulation of valves in the presence of a gas leak.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Improved Approach to 6D Object Pose Tracking in Fast Motion Scenarios Mechanical Exploration of the Design of Tactile Fingertips via Finite Element Analysis Generating Robot-Dependent Cost Maps for Off-Road Environments Using Locomotion Experiments and Earth Observation Data* Tracking Visual Landmarks of Opportunity as Rally Points for Unmanned Ground Vehicles Experimental Assessment of Feature-based Lidar Odometry and Mapping
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1