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}
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.