View Item 

Show simple item record

Autonomous wireless sensor deployment with unmanned aerial vehicles for structural health monitoring applications
dc.contributor.authorZhou, Hao
dc.contributor.authorLynch, Jerome
dc.contributor.authorZekkos, Dimitrios
dc.date.accessioned2022-05-06T17:26:52Z
dc.date.available2023-07-06 13:26:46en
dc.date.available2022-05-06T17:26:52Z
dc.date.issued2022-06
dc.identifier.citationZhou, Hao; Lynch, Jerome; Zekkos, Dimitrios (2022). "Autonomous wireless sensor deployment with unmanned aerial vehicles for structural health monitoring applications." Structural Control and Health Monitoring 29(6): n/a-n/a.
dc.identifier.issn1545-2255
dc.identifier.issn1545-2263
dc.identifier.urihttps://hdl.handle.net/2027.42/172278
dc.publisherSpringer
dc.publisherWiley Periodicals, Inc.
dc.subject.othermobility
dc.subject.otherUAV
dc.subject.otherwireless sensor
dc.subject.otherKalman filter
dc.subject.othercomputer vision
dc.subject.otherautonomy
dc.titleAutonomous wireless sensor deployment with unmanned aerial vehicles for structural health monitoring applications
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172278/1/stc2942.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172278/2/stc2942_am.pdf
dc.identifier.doi10.1002/stc.2942
dc.identifier.sourceStructural Control and Health Monitoring
dc.identifier.citedreferenceLu CP, Hager GD, Mjolsness E. Fast and globally convergent pose estimation from video images. IEEE Trans Pattern Anal Mach Intell. 2000; 22 ( 6 ): 610 - 622. doi: 10.1109/34.862199
dc.identifier.citedreferenceGreenwood WW, Lynch JP, Zekkos D. Applications of UAVs in civil infrastructure. J Infrastruct Syst. 2019; 25 ( 2 ): 04019002. doi: 10.1061/(asce)is.1943-555x.0000464
dc.identifier.citedreferenceSony S, Laventure S, Sadhu A. A literature review of next-generation smart sensing technology in structural health monitoring. Struct Control Heal Monit. 2019; 26 ( 3 ): e2321. doi: 10.1002/STC.2321
dc.identifier.citedreferenceKane M, Zhu D, Hirose M, Dong X, Winter B, Häckell M, Lynch JP, Wang Y, Swartz A. Development of an extensible dual-core wireless sensing node for cyber-physical systems. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014. Vol 9061, International Society for Optics and Photonics; 2014. doi: 10.1117/12.2045325
dc.identifier.citedreferenceArduPilot. ArduPilot project. GitHub Repos. Published online 2011. https://github.com/ArduPilot/ardupilot
dc.identifier.citedreferenceArduPilot. ArduPilot EKF. Published online 2011. https://ardupilot.org/copter/docs/common-apm-navigation-extended-kalman-filter-overview.html
dc.identifier.citedreferencePittelkau ME. Rotation vector in attitude estimation. J Guid Control Dynam. 2003; 26 ( 6 ): 855 - 860. doi: 10.2514/2.6929
dc.identifier.citedreferenceDroneKit. DroneKit-Python: library for communicating with drones via MAVLink. Published online 2014. https://github.com/dronekit/dronekit-python
dc.identifier.citedreferenceSwatbotics. Apriltag. GitHub Repos. Published online 2016. https://github.com/swatbotics/apriltag
dc.identifier.citedreferenceZhou H, Lynch JP, Zekkos D. Vision-based precision localization of UAVs for sensor payload placement and pickup for field monitoring applications. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019. Vol. 10970. SPIE; 2019: 1097007.
dc.identifier.citedreferenceLepetit V, Moreno-Noguer F, Fua P. EPnP: an accurate O ( n ) solution to the PnP problem. Int J Comput Vis. 2009; 81 ( 2 ): 155 - 166. doi: 10.1007/s11263-008-0152-6
dc.identifier.citedreferenceBradski G. The OpenCV library. Dr Dobb’s J Softw Tools. 2000; 25: 120 - 125.
dc.identifier.citedreferenceOpenCV. Camera calibration and 3D reconstruction, OpenCV 2.4.13.7 documentation. Published online 2019.
dc.identifier.citedreferenceLevenberg K. A method for the solution of certain non-linear problems in least squares. Q Appl Math. 1944; 2 ( 2 ): 164 - 168. doi: 10.1090/qam/10666
dc.identifier.citedreferenceMarquardt DW. An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math. 1963; 11 ( 2 ): 431 - 441. doi: 10.1137/0111030
dc.identifier.citedreferenceLeutenegger S, Lynen S, Bosse M, Siegwart R, Furgale P. Keyframe-based visual–inertial odometry using nonlinear optimization. Int J Rob Res. 2015; 34 ( 3 ): 314 - 334. doi: 10.1177/0278364914554813
dc.identifier.citedreferenceForster C, Carlone L, Dellaert F, Scaramuzza D. On-manifold preintegration for real-time visual-inertial odometry. IEEE Trans Robot. 2016; 33 ( 1 ): 1 - 21. doi: 10.1109/TRO.2016.2597321
dc.identifier.citedreferenceQin T, Li P, Shen S. VINS-mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans Robot. 2018; 34 ( 4 ): 1004 - 1020. doi: 10.1109/TRO.2018.2853729
dc.identifier.citedreferenceMourikis AI, Roumeliotis SI. A multi-state constraint Kalman filter for vision-aided inertial navigation. In: Proceedings 2007 IEEE International Conference on Robotics and Automation. IEEE; 2007: 3565 - 3572.
dc.identifier.citedreferenceBloesch M, Omari S, Hutter M, Siegwart R. Robust visual inertial odometry using a direct EKF-based approach. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2015: 298 – 304, IEEE. doi: 10.1109/IROS.2015.7353389
dc.identifier.citedreferenceBar-Shalom Y, Li XR, Kirubarajan T. Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. New York, NY: John Wiley & Sons; 2004.
dc.identifier.citedreferenceBrincker R, Zhang L, Andersen P. Modal identification from ambient responses using frequency domain decomposition. In: IMAC 18: Proceedings of the International Modal Analysis Conference (IMAC); 2000: 625 - 630.
dc.identifier.citedreferenceKrogius M, Haggenmiller A, Olson E. Flexible layouts for fiducial tags. In: IEEE International Conference on Intelligent Robots and Systems. IEEE; 2019: 1898 - 1903.
dc.identifier.citedreferenceDurrant-Whyte H, Bailey T. Simultaneous localization and mapping: part I. IEEE Robot Autom Mag. 2006; 13 ( 2 ): 99 - 110. doi: 10.1109/MRA.2006.1638022
dc.identifier.citedreferenceBailey T, Durrant-Whyte H. Simultaneous localization and mapping (SLAM): part II. IEEE Robot Autom Mag. 2006; 13 ( 3 ): 108 - 117. doi: 10.1109/MRA.2006.1678144
dc.identifier.citedreferenceLaw KH, Lynch JP. Smart city: technologies and challenges. IT Prof. 2019; 21 ( 6 ): 46 - 51. doi: 10.1109/MITP.2019.2935405
dc.identifier.citedreferenceLynch JP. Design of a wireless active sensing unit for localized structural health monitoring. Struct Control Heal Monit. 2005; 12 ( 3–4 ): 405 - 423. doi: 10.1002/STC.77
dc.identifier.citedreferenceKoo KY, Brownjohn JMW, List DI, Cole R. Structural health monitoring of the Tamar suspension bridge. Struct Control Heal Monit. 2013; 20 ( 4 ): 609 - 625. doi: 10.1002/STC.1481
dc.identifier.citedreferenceTang Z, Chen Z, Bao Y, Li H. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Struct Control Heal Monit. 2019; 26 ( 1 ): e2296. doi: 10.1002/STC.2296
dc.identifier.citedreferenceTsiapoki S, Bahrami O, Häckell MW, Lynch JP, Rolfes R. Combination of damage feature decisions with adaptive boosting for improving the detection performance of a structural health monitoring framework: validation on an operating wind turbine. Struct Heal Monit. 2020; 20 ( 2 ): 637 - 660. doi: 10.1177/1475921720909379
dc.identifier.citedreferenceJang S, Jo H, Cho S, et al. Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation. Smart Struct Syst. 2010; 6 ( 5_6 ): 461 - 480.
dc.identifier.citedreferenceFloreano D, Wood RJ. Science, technology and the future of small autonomous drones. Nature. 2015; 521 ( 7553 ): 460 - 466. doi: 10.1038/nature14542
dc.identifier.citedreferenceEscobar-Wolf R, Oommen T, Brooks CN, Dobson RJ, Ahlborn TM. Unmanned aerial vehicle (UAV)-based assessment of concrete bridge deck delamination using thermal and visible camera sensors: a preliminary analysis. Res Nondestruct Eval. 2018; 29 ( 4 ): 183 - 198. doi: 10.1080/09349847.2017.1304597
dc.identifier.citedreferenceYan Y, Mao Z, Wu J, Padir T, Hajjar JF. Towards automated detection and quantification of concrete cracks using integrated images and lidar data from unmanned aerial vehicles. Struct Control Heal Monit. 2021; 28 ( 8 ): e2757. doi: 10.1002/STC.2757
dc.identifier.citedreferenceHoskere V, Park JW, Yoon H, Spencer BF. Vision-based modal survey of civil infrastructure using unmanned aerial vehicles. J Struct Eng. 2019; 145 ( 7 ): 04019062. doi: 10.1061/(ASCE)ST.1943-541X.0002321
dc.identifier.citedreferenceEllenberg A, Kontsos A, Moon F, Bartoli I. Bridge related damage quantification using unmanned aerial vehicle imagery. Struct Control Heal Monit. 2016; 23 ( 9 ): 1168 - 1179. doi: 10.1002/STC.1831
dc.identifier.citedreferenceFujino Y, Siringoringo DM. Recent research and development programs for infrastructures maintenance, renovation and management in Japan. Struct Infrastruct Eng. 2020; 16 ( 1 ): 3 - 25. doi: 10.1080/15732479.2019.1650077
dc.identifier.citedreferenceHuston DR, Miller J, Esser B. Adaptive, robotic, and mobile sensor systems for structural assessment. In: Liu S-C, ed. Smart Structures and Materials 2004: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems. Vol. 5391; 2004: 189 - 196.
dc.identifier.citedreferenceZhu D, Guo J, Cho C, Wang Y, Lee KM. Wireless mobile sensor network for the system identification of a space frame bridge. IEEE/ASME Trans Mechatronics. 2012; 17 ( 3 ): 499 - 507. doi: 10.1109/TMECH.2012.2187915
dc.identifier.citedreferenceSaripalli S, Montgomery JF, Sukhatme GS. Visually guided landing of an unmanned aerial vehicle. IEEE Trans Robot Autom. 2003; 19 ( 3 ): 371 - 380. doi: 10.1109/TRA.2003.810239
dc.identifier.citedreferenceMerz T, Duranti S, Conte G. Autonomous landing of an unmanned helicopter based on vision and inertial sensing. In: Experimental Robotics IX. Springer, Berlin/Heidelberg, Germany; 2006: 343 – 352. doi: 10.1007/11552246_33
dc.identifier.citedreferenceLange S, Sünderhauf N, Protzel P. A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments. In: 2009 International Conference on Advanced Robotics. IEEE; 2009: 1 - 6.
dc.identifier.citedreferenceKato H, Billinghurst M. Marker tracking and hmd calibration for a video-based augmented reality conferencing system. In: Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR’99). 1999: 85 – 94, IEEE. doi: 10.1109/IWAR.1999.803809
dc.identifier.citedreferenceGarrido-Jurado S, Muñoz-Salinas R, Madrid-Cuevas FJ, Marín-Jiménez MJ. Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognit. 2014; 47 ( 6 ): 2280 - 2292. doi: 10.1016/j.patcog.2014.01.005
dc.identifier.citedreferenceOlson E. AprilTag: a robust and flexible visual fiducial system. In: 2011 IEEE International Conference on Robotics and Automation. 2011: 3400 – 3407, IEEE. doi: 10.1109/ICRA.2011.5979561
dc.identifier.citedreferenceBorowczyk A, Nguyen D-T, Phu-Van Nguyen A, Nguyen DQ, Saussié D, Le Ny J. Autonomous landing of a multirotor micro air vehicle on a high velocity ground vehicle. Ifac-Papersonline. 2017; 50 ( 1 ): 10488 - 10494. doi: 10.1016/J.IFACOL.2017.08.1980
dc.identifier.citedreferenceChaves SM, Wolcott RW, Eustice RM. NEEC research: toward GPS-denied landing of unmanned aerial vehicles on ships at sea. Nav Eng J. 2015; 127 ( 1 ): 23 - 35.
dc.identifier.citedreferenceAraar O, Aouf N, Vitanov I. Vision based autonomous landing of multirotor UAV on moving platform. J Intell Robot Syst. 2017; 85 ( 2 ): 369 - 384. doi: 10.1007/s10846-016-0399-z
dc.identifier.citedreferenceRees C. PX4 and 3D Robotics launch new Pixhawk autopilot for UAVs. Published online 2013. https://www.unmannedsystemstechnology.com/2013/08/px4-and-3d-robotics-launch-new-pixhawk-autopilot-for-uavs/
dc.identifier.citedreferenceMAVLink. MAVLink: micro air vehicle message marshalling library. GitHub Repos. Published online 2010. https://github.com/mavlink/mavlink
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
stc2942.pdf
Size:
48.45MB
Format:
PDF
View/Open
Name:
stc2942_am.pdf
Size:
3.177MB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.