Research Papers

Industrial Robot Accuracy Degradation Monitoring and Quick Health Assessment

[+] Author and Article Information
Guixiu Qiao

National Institute of Standards and Technology,
100 Bureau Drive, Gaithersburg, MD 20899
e-mail: guixiu.qiao@nist.gov

Brian A. Weiss

National Institute of Standards and Technology,
100 Bureau Drive, Gaithersburg, MD 20899
e-mail: brian.weiss@nist.gov

1Corresponding author.

Manuscript received March 18, 2019; final manuscript received April 26, 2019; published online May 14, 2019. Assoc. Editor: Y. Lawrence Yao.

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Manuf. Sci. Eng 141(7), 071006 (May 14, 2019) (7 pages) Paper No: MANU-19-1160; doi: 10.1115/1.4043649 History: Received March 18, 2019; Accepted April 28, 2019

Robot accuracy degradation sensing, monitoring, and assessment are critical activities in many industrial robot applications, especially when it comes to the high accuracy operations which may include welding, material removal, robotic drilling, and robot riveting. The degradation of robot tool center accuracy can increase the likelihood of unexpected shutdowns and decrease manufacturing quality and production efficiency. The development of monitoring, diagnostic and prognostic (collectively known as prognostics and health management (PHM)) technologies can aid manufacturers in maintaining the performance of robot systems. PHM can provide the techniques and tools to support the specification of a robot’s present and future health state and optimization of maintenance strategies. This paper presents the robotic PHM research and the development of a quick health assessment at the U.S. National Institute of Standards and Technology (NIST). The research effort includes the advanced sensing development to measure the robot tool center position and orientation; a test method to generate a robot motion plan; an advanced robot error model that handles the geometric/nongeometric errors and the uncertainties of the measurement system, and algorithms to process measured data to assess the robot’s accuracy degradation. The algorithm has no concept of the traditional derivative or gradient for algorithm converging. A use case is presented to demonstrate the feasibility of the methodology.

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Anandan, T. M., 2016, Aerospace Manufacturing on Board With Robots, RIA, Ann Arbor, MI.
Rossmann, J., 2015, “eRobotics Meets the Internet of Things Modern Tools for Today’s Challenges in Robotics and Automation,” Proceedings 2015 International Conference on Developments in Esystems Engineering Dese 2015, Burj Khalifa, Dubai, UAE, Dec. 12–15, pp. 318–323.
Culla, D., Gorrotxategi, J., Rodriguez, M., Izard, J. B., Herve, P. E., and Canada, J., 2018, “Full Production Plant Automation in Industry Using Cable Robotics With High Load Capacities and Position Accuracy,” Robot 2017: Third Iberian Robotics Conference, Vol. 2, Vol. 694, A. Ollero, A. Sanfeliu, L. Montano, N. Lau, and C. Cardeira, eds., Seville, Spain, Nov. 22–24, Springer International Publishing Ag, Cham, pp. 3–14.
DeVlieg, R., 2010, “Expanding the Use of Robotics in Airframe Assembly Via Accurate Robot Technology,” SAE Int. J. Aerospace, 3(1), pp. 198–203. [CrossRef]
Kim, H. J., Kawamura, A., Nishioka, Y., and Kawamura, S., 2018, “Mechanical Design and Control of Inflatable Robotic Arms for High Positioning Accuracy,” Adv. Rob. 32(2), pp. 89–104. [CrossRef]
Klimchik, A., and Pashkevich, A., 2018, “Robotic Manipulators With Double Encoders: Accuracy Improvement Based on Advanced Stiffness Modeling and Intelligent Control,” IFAC Papersonline, 51(11), pp. 740–745. [CrossRef]
Mitsi, S., Bouzakis, K. D., Mansour, G., Sagris, D., and Maliaris, G., 2004, “Off-line Programming of an Industrial Robot for Manufacturing,” Int. J. Adv. Manuf. Technol. 26(3), pp. 262–267. [CrossRef]
Yuan, P. J., Chen, D. D., Wang, T. M., Cao, S. Q., Cai, Y., and Xue, L., 2018, “A Compensation Method Based on Extreme Learning Machine to Enhance Absolute Position Accuracy for Aviation Drilling Robot,” Adv. Mech. Eng., 10(3), p. 11. [CrossRef]
Shen, N. Y., Guo, Z. M., Li, J., Tong, L., and Zhu, K., 2018, “A Practical Method of Improving Hole Position Accuracy in the Robotic Drilling Process,” Int. J. Adv. Manuf. Technol. 96(5–8), pp. 2973–2987. [CrossRef]
Schares, R., Schmitt, S., Emonts, M., Fischer, K., Moser, R., and Fruhauf, B., 2018, “Improving Accuracy of Robot-Guided 3d Laser Surface Processing by Workpiece Measurement in a Blink,” High-Power Laser Materials Processing: Applications, Diagnostics, and Systems VII, Vol. 10525, S. Kaierle, and S. W. Heinemann, eds., Spie-Int Soc Optical Engineering, Bellingham, 1052508-2.
Drouot, A., Zhao, R., Irving, L., Sanderson, D., and Ratchev, S., 2018, “Measurement Assisted Assembly for High Accuracy Aerospace Manufacturing,” IFAC Papersonline, 51(11), pp. 393–398. [CrossRef]
Massi, F., Bouscharain, N., Milana, S., Le Jeune, G., Maheo, Y., and Berthier, Y., 2014, “Degradation of High Loaded Oscillating Bearings: Numerical Analysis and Comparison With Experimental Observations,” Wear, 317(1–2), pp. 141–152. [CrossRef]
Abdi, H., Nahavandi, S., Frayman, Y., and Maciejewski, A. A., 2012, “Optimal Mapping of Joint Faults Into Healthy Joint Velocity Space for Fault-Tolerant Redundant Manipulators,” Robotica, 30(4), pp. 635–648. [CrossRef]
Visinsky, M. L., Cavallaro, J. R., and Walker, I. D., 1994, “Robotic Fault Detection and Fault Tolerance: A Survey,” Reliab. Eng. Syst. Saf. 46(2), pp. 139–158. [CrossRef]
Bittencourt, A. C., 2012, “On Modeling and Diagnosis of Friction and Wear in Industrial Robots,” Postgraduate thesis, Department of Electronic Engineering, Linköping University, Linköping.
Caccavale, F., Marino, A., Pierri, F., and IEEE, 2010, “Sensor Fault Diagnosis for Manipulators Performing Interaction Tasks,” IEEE International Symposium on Industrial Electronics (ISIE 2010), Bari, Italy, July 4–7, pp. 2121–2126.
Caccavale, F., Cilibrizzi, P., Pierri, F., and Villani, L., 2009, “Actuators Fault Diagnosis for Robot Manipulators With Uncertain Model,” Control Eng. Pract. 17(1), pp. 146–157. [CrossRef]
Weiss, B. A., Vogl, G. W., Helu, M., Qiao, G., Pellegrino, J., Justiniano, M., and Raghunathan, A., 2015, “Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings From a Roadmapping Workshop,” Presented at the Annual Conference of the Prognostics and Health Management Society 2015, Coronado, CA, Oct. 18–24, pp. 46–52.
Rodrigues, L. R., Gomes, J. P. P., Ferri, F. A. S., Medeiros, I. P., Galvao, R. K. H., and Nascimento, C. L., 2015, “Use of PHM Information and System Architecture for Optimized Aircraft Maintenance Planning,” IEEE Syst. J. 9(4), pp. 1197–1207. [CrossRef]
Qiao, G. X., and Weiss, B. A., 2018, “Quick Health Assessment for Industrial Robot Health Degradation and the Supporting Advanced Sensing Development,” J. Manuf. Syst. 48(Part C), pp. 51–59. [CrossRef]
Jang, J. H., Kim, S. H., and Kwak, Y. K., 2001, “Calibration of Geometric and Non-Geometric Errors of an Industrial Robot,” Robotica, 19(3), pp. 311–321. [CrossRef]
Qiao, G., and Weiss, B. A., 2017, “Accuracy Degradation Analysis for Industrial Robot Systems” Proceedings of 2017 ASME International Manufacturing Science and Engineering Conference, Los Angeles, CA, June 4–8, University of Southern California, Los Angeles, CA, MSEC2017-2782.
Zhang, X. P., Yan, W. C., Zhu, W., and Wen, T., 2012, Applied Mechanics and Civil Engineering, Vol. 137, R. Zhu, ed., Trans Tech Publications Ltd, Zurich, Switzerland, pp. 382–386.
Phillips, F., 2006, “A Novel Means of Software Compensation for Robots and Machine Tools,” SAE Technical Paper 2006-01-3167.
Sammons, P. M., Ma, L., Embry, K., Armstrong, L. H., Bristow, D. A., and Landers, R. G., 2014, Modeling and Compensation of Backlash and Harmonic Drive-Induced Errors in Robotic Manipulators, Amer Soc Mechanical Engineers, New York.
Ma, L., Bazzoli, P., Sammons, P. M., Landers, R. G., and Bristow, D. A., 2018, “Modeling and Calibration of High-Order Joint-Dependent Kinematic Errors for Industrial Robots,” Rob. Comput. Integr. Manuf., 50, pp. 153–167. [CrossRef]
Kalvodova, P., and Zalud, L., 2018, “Accuracy Evaluation Method of Multispectral Data Fusion for Robotic Systems,” Modelling and Simulation for Autonomous Systems, Vol. 10756, J. Mazal, ed., Springer International Publishing Ag, Cham, pp. 237–250.
Wampler, C. W., Hollerbach, J. M., and Arai, T., 1995, “An Implicit Loop Method for Kinematic Calibration and Its Application to Closed-Chain Mechanisms,” IEEE Trans. Rob. Autom. 11(5), pp. 710–724. [CrossRef]


Grahic Jump Location
Fig. 1

Workflow of the robot accuracy degradation quick assessment methodology

Grahic Jump Location
Fig. 2

Fixed-loop measurement plan for UR5

Grahic Jump Location
Fig. 3

Robot fixed-loop motion generation flowchart

Grahic Jump Location
Fig. 4

Six degree-of-freedom errors of the rotation axis

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Fig. 6

Even distribution of measurements in the joint space

Grahic Jump Location
Fig. 7

Robot's error histogram

Grahic Jump Location
Fig. 8

J1's error distribution



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