0
Research Papers

A Two-Stage Local Positioning Method With Misalignment Calibration for Robotic Structural Monitoring of Buildings

[+] Author and Article Information
Rong Wang

Navigation Research Center,
College of Automation Engineering,
Nanjing University of Aeronautics
and Astronautics (NUAA),
29 Jianjun Avenue,
Jiangning District,
Nanjing 211106, China
e-mails: rongwang@nuaa.edu.cn; dr.rongwang@gmail.com

Zhi Xiong

Navigation Research Center,
College of Automation Engineering,
Nanjing University of Aeronautics
and Astronautics (NUAA),
29 Jianjun Avenue,
Jiangning District,
Nanjing 211106, China
e-mail: xiongzhi@nuaa.edu.cn

Yulu Luke Chen

Sonny Astani Department of Civil
and Environmental Engineering,
University of Southern California (USC),
3620 South Vermont Avenue,
KAP 210, MC 2531,
Los Angeles, CA 90089-2531
e-mail: yuluchen@usc.edu

Preetham Manjunatha

Sonny Astani Department of Civil
and Environmental Engineering,
University of Southern California (USC),
3620 South Vermont Avenue,
KAP 210, MC 2531,
Los Angeles, CA 90089-2531
e-mail: aghalaya@usc.edu

Sami F. Masri

Fellow ASME
Sonny Astani Department of Civil
and Environmental Engineering,
University of Southern California (USC),
3620 South Vermont Avenue,
KAP 210, MC 2531,
Los Angeles, CA 90089-2531
e-mail: masri@usc.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received July 24, 2017; final manuscript received December 27, 2018; published online March 27, 2019. Editor: Joseph Beaman.

J. Dyn. Sys., Meas., Control 141(6), 061014 (Mar 27, 2019) (11 pages) Paper No: DS-17-1379; doi: 10.1115/1.4042882 History: Received July 24, 2017; Revised December 27, 2018

In structural health monitoring (SHM) applications carried out by mobile robots, the precise locating of the SHM robot is essential for accurate detection and quantification of defects. The traditional dead reckoning (DR) approach can only provide local position in the horizon, which is not enough for SHM applications in three dimensions in large buildings. In this paper, a new robot positioning algorithm for active building structural defect detection and localization is proposed. The two-stage robot positioning scheme is designed through the self-misalignment calibration and the positioning during SHM task stages, fusing the absolute and relative measurements. In order to overcome the drawback of the DR algorithm, in the full analysis of existing localization mode that can be applied to mobile robots, this paper adopted the inertial navigation system (INS) approach to measure the absolute motion information of a moving robot. On this basis, through the transformation between the absolute positioning coordinates and the local positioning coordinates of buildings, the mobile robot's optimal trajectory on building surface was designed for self-calibration of coordinate misalignments. The proposed method could effectively achieve the robot local positioning in building coordinate frame by fusing the external relative assistant measurements with absolute measurement. By using the designed strategies, the coordinate misalignment can also be self-calibrated effectively, improving local positioning accuracy.

FIGURES IN THIS ARTICLE
<>
Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.

References

Myung, H. , Lee, S. , and Lee, B. , 2011, “ Paired Structured Light for Structural Health Monitoring Robot System,” Struct. Health Monit., 10(1), pp. 49–64. [CrossRef]
Shin, J.-U. , Kim, D. , Kim, J.-H. , and Myung, H. , “ Micro-Aerial Vehicle Type Wall-Climbing Robot Mechanism for Structural Health Monitoring,” Proc. SPIE, 8692, p. 86921B.
Lins, R. G. , and Givigi, S. N. , 2016, “ Autonomous Robot System Architecture for Automation of Structural Health Monitoring,” Annual IEEE Systems Conference (SysCon), Orlando, FL, Apr. 18–21, pp. 1–7.
Myung, H. , Jung, J. , and Jeon, H. , 2012, “ Robotic SHM and Model-Based Positioning System for Monitoring and Construction Automation,” Adv. Struct. Eng., 15(6), pp. 943–954. [CrossRef]
Cho, B.-S. , Moon, W. , Seo, W.-J. , and Baek, K.-R. , 2011, “ A Study on Localization of the Mobile Robot Using Inertial Sensors and Wheel Revolutions,” Fourth International Conference Intelligent Robotics and Applications (ICIRA), Aachen, Germany, Dec. 6–8, pp. 575–583.
HoŁA, J. , and Schabowicz, K. , 2010, “ State-of-the-Art Nondestructive Methods for Diagnostic Testing of Building Structures—Anticipated Development Trends,” Arch. Civ. Mech. Eng., 10(3), pp. 5–18. [CrossRef]
Hobbs, B. , and Tchoketch Kebir, M. , 2007, “ Nondestructive Testing Techniques for the Forensic Engineering Investigation of Reinforced Concrete Buildings,” Forensic Sci. Int., 167(2–3), pp. 167–172. [CrossRef] [PubMed]
Revel, G. M. , Pandarese, G. , and Cavuto, A. , 2013, “ Advanced Ultrasonic Nondestructive Testing for Damage Detection on Thick and Curved Composite Elements for Constructions,” J. Sandwich Struct. Mater., 15(1), pp. 5–24. [CrossRef]
Bogue, R. , 2010, “ The Role of Robotics in Non‐Destructive Testing,” Ind. Robot: An Int. J., 37(5), pp. 421–426. [CrossRef]
Eich, M. , and Vögele, T. , 2011, “ Design and Control of a Lightweight Magnetic Climbing Robot for Vessel Inspection,” 19th Mediterranean Conference on Control & Automation (MED), Corfu, Greece, June 20–23, pp. 1200–1205.
Jung, S. , Shin, J. U. , Myeong, W. , and Myung, H. , 2015, “ Mechanism and System Design of MAV(Micro Aerial Vehicle)-Type Wall-Climbing Robot for Inspection of Wind Blades and Non-Flat Surfaces,” 15th International Conference on Control, Automation and Systems (ICCAS), Busan, South Korea, Oct. 13–16, pp. 1757–1761.
Amakawa, T. , Yamaguchi, T. , Yamada, Y. , and Nakamura, T. , 2017, “ Proposing an Adhesion Unit for a Traveling-Wave-Type, Omnidirectional Wall-Climbing Robot in Airplane Body Inspection Applications,” IEEE International Conference on Mechatronics (ICM), Churchill, VIC, Australia, Feb. 13–15, pp. 178–183.
Cho, B.-S. , Moon, W.-S. , Seo, W.-J. , and Baek, K.-R. , 2011, “ A Dead Reckoning Localization System for Mobile Robots Using Inertial Sensors and Wheel Revolution Encoding,” J. Mech. Sci. Technol., 25(11), pp. 2907–2917. [CrossRef]
Maklouf, O. , Ghila, A. , and Abdulla, A. , 2012, “ Cascade Kalman Filter Configuration for Low Cost IMU/GPS Integration in Car Navigation Like Robot,” Int. Scholarly Sci. Res. Innovation, 6(6), pp. 571–578. https://waset.org/publications/14260/cascade-kalman-filter-configuration-for-low-cost-imu-gps-integration-in-car-navigation-like-robot
Eling, C. , Klingbeil, L. , and Kuhlmann, H. , 2015, “ Real-Time Single-Frequency GPS/MEMS-IMU Attitude Determination of Lightweight UAVs,” Sensors, 15(10), p. 26212. [CrossRef] [PubMed]
Lim, C. H. , Lim, T. S. , and Koo, V. C. , 2014, “ A MEMS Based, Low Cost GPS-Aided INS for UAV Motion Sensing,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Besacon, France, July 8–11, pp. 576–581.
Biswas, J. , and Veloso, M. , 2010, “ WiFi Localization and Navigation for Autonomous Indoor Mobile Robots,” IEEE International Conference on Robotics and Automation, Anchorage, AK, May 3–7, pp. 4379–4384.
Palipana, S. , Kapukotuwe, C. , Malasinghe, U. , Wijenayaka, P. , and Munasinghe, S. R. , 2012, “ Localization of a Mobile Robot Using ZigBee Based Optimization Techniques,” IEEE 6th International Conference on Information and Automation for Sustainability, Beijing, China, Sept. 27–29, pp. 215–220.
Trinh, L. A. , Thang, N. D. , Kim, D. , Lee, S. , and Chang, S. , 2012, “ Application of Matrix Pencil Algorithm to Mobile Robot Localization Using Hybrid DOA/TOA Estimation,” Int. J. Adv. Rob. Syst., 9(6), p. 254. [CrossRef]
Qian, D. , and Dargie, W. , 2012, “ Evaluation of the Reliability of RSSI for Indoor Localization,” International Conference on Wireless Communications in Underground and Confined Areas (CWCUCA), Ferrand, France, Aug. 28–30, pp. 1–6.
Newman, P. , Cole, D. , and Ho, K. , 2006, “ Outdoor SLAM Using Visual Appearance and Laser Ranging,” IEEE International Conference on Robotics and Automation (ICRA), Orlando, FL, May 15–19, pp. 1180–1187.
Nakamura, T. , and Suzuki, S. , 2014, “ Simplified EKF-SLAM by Combining Laser Range Sensor With Retro Reflective Markers for Use in Kindergarten,” Int. J. Rob. Mechatronics, 1(1), pp. 1–7. [CrossRef]
An, S.-Y. , Lee, L.-K. , and Oh, S.-Y. , 2016, “ Ceiling Vision-Based Active SLAM Framework for Dynamic and Wide-Open Environments,” Auton. Rob., 40(2), pp. 291–324. [CrossRef]
Ethier, S. N. , and Kurtz, T. G. , 2009, Markov Processes: Characterization and Convergence, Vol. 282, Wiley, Hoboken, NJ.
Noureldin, A. , Karamat, T. B. , Eberts, M. D. , and El-Shafie, A. , 2009, “ Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications,” IEEE Trans. Veh. Technol., 58(3), pp. 1077–1096. [CrossRef]
Mei, C. , and Patrick, R. , 2006, “ Calibration Between a Central Catadioptric Camera and a Laser Range Finder for Robotic Applications,” IEEE International Conference on Robotics and Automation (ICRA), Orlando, FL, May 15–19, pp. 532–537.
Goshen-Meskin, D. , and Bar-Itzhack, I. Y. , 1990, “ Observability Analysis of Piece-wise Constant Systems With Application to Inertial Navigation,” 29th IEEE Conference on Decision and Control, Honolulu, HI, Dec. 5–7, pp. 821–826.
Kong, X. , Guo, M. , and Dong, J. , 2009, “ An Improved PWCS Approach on Observability Analysis of Linear Time-Varying System,” Chinese Control and Decision Conference (CCDC), Guilin, China, June 17–19, pp. 761–765.
Mirzaei, F. M. , and Roumeliotis, S. I. , 2008, “ A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation,” IEEE Trans. Rob., 24(5), pp. 1143–1156. [CrossRef]
VectorNav Technologies, 2012, “ VN-200 Product Brief,” VectorNav Technologies, Dallas, TX, accessed Apr. 27, 2016, https://www.vectornav.com/docs/default-source/documentation/vn-200-documentation/PB-12-0003.pdf?sfvrsn =749ee6b9_15
Xsens Technologies, 2018, “ Mti-7 Leaflet,” Xsens Technologies B.V., An Enschede, The Netherlands, accessed Aug. 6, 2018, https://www.xsens.com/download/pdf/documentation/mti-7/MTi-7_Leaflet.pdf

Figures

Grahic Jump Location
Fig. 1

Trace of the mobile robot motion in the building coordinate frame

Grahic Jump Location
Fig. 2

Horizontal velocity transformation relationship

Grahic Jump Location
Fig. 3

Framework of the enhanced local positioning system

Grahic Jump Location
Fig. 4

Calibration results of the coordinate misalignment (group 1): (a) uniform rectilinear motion, (b) accelerating rectilinear motion, and (c) uniform circular motion

Grahic Jump Location
Fig. 5

Calibration results of the coordinate misalignment (group 2): (a) uniform rectilinear motion, (b) accelerating rectilinear motion, and (c) uniform circular motion

Grahic Jump Location
Fig. 6

SHM robot trajectory (group 1): (a) in 3D building space, (b) in geographic coordinate frame, and (c) in building coordinate frame

Grahic Jump Location
Fig. 7

SHM robot trajectory (group 2), (a) in 3D building space, (b) in geographic coordinate frame, and (c) in building coordinate frame

Grahic Jump Location
Fig. 8

Error comparison of local positioning in building coordinate frame (group 1): (a) in the X-axis, (b) in the Y-axis, and (c) in the Z-axis

Grahic Jump Location
Fig. 9

Error comparison of local positioning in building coordinate frame (group 2): (a) in 3D building space, (b) in geographic coordinate frame, and (c) in building coordinate frame

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In