Abstract
This paper presents the development of a mapping and nondestructive technique (NDT) for the assessment of concrete pipeline networks. It focuses on the inspection of open-end culverts and storm drain systems using sensor networks. The network consists of 3D Lidar/3D camera/radar with NDT sensors. These sensors are mounted on autonomous vehicles that are set to reduce the extent of human operator involvement through intelligent control algorithms. More accurate remote monitoring is achieved by using smart robots and sensor networks equipped with advanced AI-based techniques. In the systems, ground robots are used with onboard sensing to enable environmental observation. The ground robots have the capability of remote communication, teleoperation, and the transmission of collected data. Effective automated mapping requires the flexibility to allow real-time changes in planning to accommodate unmodeled/unexpected uncertainties and disturbances. The autonomous system can be used repeatedly with reduced cost, primarily for maintenance. In addition, making the process autonomous can reduce the risk to human inspectors and save time during inspections. Also, it can be operated by personnel who require less training to obtain the data than standard inspection processes. The developed technology is demonstrated by actual field and lab testing. It provides accurate assessment tools for underground pipeline networks.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
