原文传递 Unmanned Surface Vessel (USV) Systems for Bridge Inspection
题名: Unmanned Surface Vessel (USV) Systems for Bridge Inspection
作者: Von Ellennrieder, K.; Wampler, J.
关键词: Bridge inspection##Unmanned surface vehicles##Autonomous control systems##Real-time imaging##Underwater inspection##Acoustic sensing##
摘要: The use of unmanned surface vehicles (USVs) for bridge inspection has been explored. The following issues were considered: (1) the requirements of and current techniques utilized in on-water bridge inspection; (2) USV design and configuration considerations for USV-based bridge inspection; (3) use of acoustic sensing techniques for imaging underwater bridge structures and channel bottom features; (4) the control and dynamic positioning of USVs for bridge inspection; (5) the use of advanced robotics techniques for improving vehicle navigation under bridges and the mapping of bridge features; and lastly, (6) recommendations for the addition of standard operating procedures to accommodate the use of USVs for bridge inspection. A proof of concept system was developed and tested using an existing USV at Florida Atlantic University (FAU) outfitted with a real-time imagining sonar. Field experiments were conducted with the system at several sites near the city of Carrabelle, Florida and in Dania Beach, Florida. Live demonstrations of the system were also conducted at the 2015 Florida Automated Vehicles Summit. The system was able to autonomously collect images of bridge structures, both underwater and at the waterline, by traversing a series of preprogrammed waypoints along a bridge and station-keeping at locations of interest. The results of the field tests and background literature survey are presented, and a set of recommendations for use of USV-based bridge inspection systems is given. It is suggested that the application of advanced robotics techniques for Human-Robot-Interaction and autonomous mapping/imaging can improve the preliminary inspection approach implemented during this study.
总页数: 168
报告类型: 科技报告
检索历史
应用推荐