Proof of Concept for Using Unmanned Aerial Vehicles for High Mast Pole and Bridge Inspections

Bridges and high mast luminaires (HMLs) are key components of transportation infrastructures. Effective inspection processes are crucial to maintain the structural integrity of these components. The most common approach for inspections is visual examination by trained and experienced inspectors. A proposed approach to assist inspectors during the visual inspection process is to use small unmanned aerial systems (sUAS) equipped with high-definition cameras to transmit video data of structural components in near real time. The use of sUAS as tools for structural inspections can significantly reduce costs and safety risks associated with inspectors and motorists, and improve the effectiveness and accuracy of structural health evaluations. Following a systems engineering approach, a proof-of-concept initial study was conducted to identify system limitations and gain insights into the expected usefulness of sUAS as tools for structural inspections. Extensive indoor controlled experiments using industrial fans were conducted to evaluate sUAS flight response in controlled wind conditions, to measure image quality in different flight scenarios, and to determine image quality in low-light conditions. Altitude, payload, and maneuverability tests were conducted to understand sUAS performance and limitation parameters related to their use for transportation infrastructure inspections. In full coordination with Florida Department of Transportation (FDOT), limited field tests were conducted to collect image data of underside bridge sections and HMLs. The collected images were of similar quality than those collected by inspectors during previous inspections. In addition, a basic sUAS flight training program was developed, and a preliminary cost analysis was conducted to estimate the cost for using sUAS as tools during inspections. Preliminary results showed potential cost savings in man-hours by using an sUAS approach instead of conventional methods. Overall, results provided evidence that significant benefits can be obtained from using sUAS during bridge and HML inspections. However, there still exist gaps that need to be addressed in order to use these aerial systems safely and effectively in practice. Various future research areas are identified to close these gaps and increase the general understanding of sUAS for structural inspections.

[1]  Nasir G. Gharaibeh,et al.  Use of micro unmanned aerial vehicles for roadside condition assessment , 2010 .

[2]  Zhaozheng Yin,et al.  Develop a UAV Platform for Automated Bridge Inspection , 2015 .

[3]  Asce,et al.  2013 report card for America’s infrastructure , 2017 .

[4]  Janice Harper,et al.  ENDANGERED SPECIES , 2000, Ecological Restoration.

[5]  R L McCrum,et al.  Inspection and Repair of High-Mast Luminaires (HML) , 1996 .

[6]  K.P. Valavanis,et al.  Statistical profile generation for traffic monitoring using real-time UAV based video data , 2007, 2007 Mediterranean Conference on Control & Automation.

[7]  Thomas Oommen,et al.  Evaluating the Use of Unmanned Aerial Vehicles for Transportation Purposes , 2015 .

[8]  Scot Becker,et al.  Best Practices In Bridge Management Decision-Making , 2009 .

[9]  Barbara Kitchenham,et al.  Procedures for Performing Systematic Reviews , 2004 .

[10]  Patrick Doherty,et al.  From images to traffic behavior - A UAV tracking and monitoring application , 2007, 2007 10th International Conference on Information Fusion.

[11]  Edward McCormack,et al.  The use of small unmanned aircraft by the Washington State Department of Transportation , 2008 .

[12]  Konstantinos Kanistras,et al.  A survey of unmanned aerial vehicles (UAVs) for traffic monitoring , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[13]  E. Steele,et al.  FAA Modernization and Reform Act of 2012 , 2014 .

[14]  Paul S. Moller,et al.  Caltrans bridge inspection aerial robot. , 2008 .

[15]  B. Coifman,et al.  Surface Transportation Surveillance from Unmanned Aerial Vehicles , 2003 .

[16]  Tarek Hamel,et al.  A UAV for bridge inspection: Visual servoing control law with orientation limits , 2007 .

[17]  Helen Liu,et al.  Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology , 2010 .

[18]  Guoqing Zhou,et al.  DETECTING AND COUNTING VEHICLES FROM SMALL LOW-COST UAV IMAGES , 2009 .

[19]  Jun-Seok Oh,et al.  Lessons learned: Application of small UAV for urban highway traffic monitoring , 2007 .

[20]  Zhaozheng Yin A Quadcopter with Heterogeneous Sensors for Autonomous Bridge Inspection , 2014 .

[21]  Joseph E. Krajewski,et al.  Bridge Inspection and Interferometry , 2006 .

[22]  Rama Rao Nidamanuri,et al.  Use of field reflectance data for crop mapping using airborne hyperspectral image , 2011 .

[23]  John C. Knight,et al.  Safety critical systems: challenges and directions , 2002, Proceedings of the 24th International Conference on Software Engineering. ICSE 2002.

[24]  Sang-Hyeok Gang,et al.  Report Card for America's Infrastructure , 2012 .

[25]  Josué Jr. Guimarães Ramos,et al.  Internet-based solutions in the development and operation of an unmanned robotic airship , 2003 .

[26]  W. Grossman,et al.  Autonomous Searching and Tracking of a River using an UAV , 2007, 2007 American Control Conference.

[27]  Steven Chase,et al.  Developing a Tele-Robotic Platform for Bridge Inspection , 2011 .

[28]  Aaron M. Dollar,et al.  Stability of small-scale UAV helicopters and quadrotors with added payload mass under PID control , 2012, Autonomous Robots.