A Smart Autonomous Pipeline Inspection Robot

目前，很少对已知有泄漏的燃气管段进行内部检查。商业配气网络的维护通常基于统计信息，如以前的维护记录，泄漏调查和腐蚀数据，而不是科学证据。到目前为止，最先进的内部气体管道检测系统是基于拴在管道上的类似机器人的车辆，通过操纵杆手动操作。他们收集传感数据并将其存储在本地，以便稍后由训练有素的技术人员离线处理，以识别管道中任何缺陷的迹象。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英里长的管道段的数据，并需要一周的时间来处理数据。按照这个速度，将不可能覆盖天然气和石油分销网络，而加拿大的天然气和石油分销网络已经超过11.5万立方米。2012年，加拿大天然气和石油分销公司为检查和维护管道花费了11亿美元。寻找更具成本效益和时间效率的方法来监控和检查配电系统已经成为一个迫切的需要，特别是随着主电源的老化和能源高需求驱动的网络的快速增长。

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.