A Smart Autonomous Pipeline Inspection Robot

智能自主管道检测机器人

• 批准号: RGPIN-2014-06512
• 负责人: Gueaieb, Wail
• 金额: $ 1.82万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=589058
• 关键词: Smart; Autonomous; Pipeline; Inspection; Robot

Currently, very little internal inspection is conducted on gas pipe segments that are known to leak. The maintenance of commercial gas distribution networks is often based on statistical information, such as previous maintenance record, leak surveys, and corrosion data, more than scientific evidence. To date, the state-of-the-art of internal gas pipe inspection systems is based on tethered robot-like vehicles that are inserted in the pipes and operated manually through joysticks. They collect sensory data and store it locally so that it can be processed offline later by trained technicians to identify any signs of defects in the pipe. This process is very costly and time consuming. Typically, it takes a crew of technicians a full day to collect data for a pipe segment of 1 mile, and a week to process it. At this pace, it would be impossible to cover gas and oil distribution networks, which already passed 115,000 in Canada. In 2012, it costed Canadian gas and oil distribution companies $1.1 billion to inspect and maintain their mains. Finding more cost- and time-efficient ways to monitor and inspect distribution and delivery systems has become an urgent necessity, especially with the aging of the mains and the rapid growth of the network driven by high demand on energy sources. The goal of this research is to design and develop a fully autonomous untethered mobile robot capable of long-range navigation inside gas pipes of various sizes and structural configurations to perform in-situ pipe inspection. The robot will automatically diagnose pipes for different types of defects, including cracks, dents, corrosion, accumulated debris, and water infiltration. It will process and analyze its sensory measurements online and wirelessly communicate detailed reports back to a maintenance unit. This would facilitate identifying the exact locations of the defects, generate early warnings for potential anomalous segments, help deciding on the proper repair method (spot, relining, replacement, etc.), and decrease the response time in case of an emergency. The research will help insuring the reliability of Canadian gas distribution networks, which transport about 23% of Canadian exports (including oil exports). It will minimizes the chances of gas leaks, especially in remote areas where technicians cannot reach easily (eg., offshore), and so limit their side effects on the environment. Advancing the state of knowledge in this area will have a positive impact on other economic sectors by paving the road for the development of similar robot inspection systems for oil, water, and sewage distribution networks.

目前，很少对已知泄漏的煤气管段进行内部检查。商业气体分配网络的维护通常基于统计信息，例如先前的维护记录、泄漏调查和腐蚀数据，而不是科学证据。迄今为止，最先进的内部气体管道检测系统是基于插入管道中并通过操纵杆手动操作的系留机器人式车辆。它们收集传感数据并将其存储在本地，以便经过培训的技术人员稍后可以离线处理这些数据，以识别管道中的任何缺陷迹象。这个过程非常昂贵和耗时。通常情况下，一组技术人员需要一整天的时间来收集1英里管道段的数据，一周的时间来处理这些数据。按照这种速度，不可能覆盖加拿大已经超过115，000个的天然气和石油分销网络。2012年，加拿大天然气和石油分销公司花费了11亿美元来检查和维护其管道。寻找更具成本效益和时间效益的方法来监控和检查配电和输送系统已成为迫切需要，特别是随着干线的老化和对能源的高需求推动的网络的快速增长。 本研究的目标是设计和开发一种完全自主的无系绳移动的机器人，能够在各种尺寸和结构配置的气体管道内进行远程导航，以进行现场管道检测。机器人将自动诊断管道的不同类型的缺陷，包括裂缝，凹痕，腐蚀，堆积的碎片和水渗透。它将在线处理和分析其感官测量结果，并将详细的报告无线传回维护单位。这将有助于识别缺陷的确切位置，为潜在的异常段生成早期警告，帮助决定适当的修复方法（点、重衬、更换等），并减少紧急情况下的响应时间。该研究将有助于确保加拿大天然气分销网络的可靠性，该网络运输约23%的加拿大出口（包括石油出口）。它将最大限度地减少气体泄漏的机会，特别是在技术人员不易到达的偏远地区（例如，离岸），从而限制其对环境的副作用。推进这一领域的知识水平将对其他经济部门产生积极影响，为开发用于石油，水和污水分配网络的类似机器人检测系统铺平道路。