Research Papers

Statistical Learning Algorithms to Compensate Slow Visual Feedback for Industrial Robots

[+] Author and Article Information
Cong Wang

Department of Mechanical Engineering,
University of California,
Berkeley, CA 94720
e-mail: wangcong@berkeley.edu

Chung-Yen Lin

Department of Mechanical Engineering,
University of California,
Berkeley, CA 94720
e-mail: chung_yen@berkeley.edu

Masayoshi Tomizuka

Department of Mechanical Engineering,
University of California,
Berkeley, CA 94720
e-mail: tomizuka@me.berkeley.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 16, 2013; final manuscript received June 12, 2014; published online October 21, 2014. Assoc. Editor: Jongeun Choi.

J. Dyn. Sys., Meas., Control 137(3), 031011 (Oct 21, 2014) (8 pages) Paper No: DS-13-1512; doi: 10.1115/1.4027853 History: Received December 16, 2013; Revised June 12, 2014

Vision guided robots have become an important element in the manufacturing industry. In most current industrial applications, vision guided robots are controlled by a look-then-move method. This method cannot support many new emerging demands which require real-time vision guidance. Challenge comes from the speed of visual feedback. Due to cost limit, industrial robot vision systems are subject to considerable latency and limited sampling rate. This paper proposes new algorithms to address this challenge by compensating the latency and slow sampling of visual feedback so that real-time vision guided robot control can be realized with satisfactory performance. Statistical learning methods are developed to model the pattern of target's motion adaptively. The learned model is used to recover visual measurement from latency and slow sampling. The imaging geometry of the camera and all-dimensional motion of the target are fully considered. Tests are conducted to provide evaluation from different aspects.

Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.


Corke, P. I., 1995, “Dynamic Issues in Robot Visual-Servo Systems,” International Symposium on Robotics Research, Herrsching, Germany, October 21–24, pp. 488–498.
Corke, P., and Good, M., 1996, “Dynamic Effects in Visual Closed-Loop Systems,” IEEE Trans. Rob. Autom., 12(5), pp. 671–683. [CrossRef]
Wang, C., Chen, W., and Tomizuka, M., 2012, “Robot End-Effector Sensing With Position Sensitive Detector and Inertial Sensors,” IEEE International Conference on Robotics and Automation, St. Paul, MN, May 14–18, pp. 5252–5257.
Wang, C., Lin, C.-Y., and Tomizuka, M., 2013, “Visual Servoing Considering Sensing Dynamics and Robot Dynamics,” 6th IFAC Symposium on Mechatronic Systems, Hangzhou, China, April 10–12, pp. 45–52.
Lin, C.-Y., Wang, C., and Tomizuka, M., 2013, “Visual Tracking With Sensing Dynamics Compensation Using the Expectation-Maximization Algorithm,” American Control Conference, Washington, DC, June 17–19, pp. 6281–6286.
Namiki, A., and Ishikawa, M., 2003, “Robotic Catching Using a Direct Mapping From Visual Information to Motor Command,” IEEE International Conference on Robotics and Automation, Taipei, Taiwan, September 14–19, Vol. 2, pp. 2400–2405.
Furukawa, N., Namiki, A., Taku, S., and Ishikawa, M., 2006, “Dynamic Regrasping Using a High-Speed Multifingered Hand and a High-Speed Vision System,” IEEE International Conference on Robotics and Automation, Orlando, FL, May 15–19, pp. 181–187.
Li, X. R., and Jilkov, V., 2003, “Survey of Maneuvering Target Tracking, Part I - Dynamic Models,” IEEE Trans. Aerosp. Electron. Syst., 39(4), pp. 1333–1364. [CrossRef]
Singer, R., and Behnke, K., 1971, “Real-Time Tracking Filter Evaluation and Selection for Tactical Applications,” IEEE Trans. Aerosp. Electron. Syst., 7(1), pp. 100–110. [CrossRef]
Shim, H. C., Kochem, M., and Tomizuka, M., 1998, “Use of Accelerometer for Precision Motion Control of Linear Motor Driven Positioning System,” 24th Annual Conference of the IEEE Industrial Electronics Society, Aachen, Germany, August 31–September 4, Vol. 4, pp. 2409–2414.
Lee, D. J., and Tomizuka, M., 2001, “State/Parameter/Disturbance Estimation With an Accelerometer in Precision Motion Control of a Linear Motor,” ASME International Mechanical Engineering Congress and Exposition, New York, November 11–16, pp. 11–16.
Jeon, S., 2010, “State Estimation Based on Kinematic Models Considering Characteristics of Sensors,” American Control Conference, Baltimore, MD, June 30–July 2, pp. 640–645.
Dempster, A. P., Laird, N. M., and Rubinr, D. B., 1977, “Maximum Likelihood From Incomplete Data via the EM Algorithm,” J. R. Stat. Soc. Ser. B (Stat. Methodol.), 39(1), pp. 1–38.
Lin, C.-Y., Wang, C., and Tomizuka, M., 2014, “Pose Estimation in Industrial Machine Vision Systems Under Sensing Dynamics: A Statistical Learning Approach,” IEEE International Conference on Robotics and Automation, Hong Kong, China, May 31–June 7.
Wang, C., Lin, C.-Y., and Tomizuka, M., 2013, “Visual Servoing for Robot Manipulators Considering Sensing and Dynamics Limitations,” ASME Dynamic Systems and Control Conference, Palo Alto, CA, October 21–23, Paper No. DSCC2013-3833. [CrossRef]


Grahic Jump Location
Fig. 2

Visual sensing dynamics

Grahic Jump Location
Fig. 1

Motivation of real-time vision guidance

Grahic Jump Location
Fig. 3

Dual-rate Kalman filtering to compensate latency and low sampling rate

Grahic Jump Location
Fig. 7

Bandwidth evaluation

Grahic Jump Location
Fig. 8

Setup of visual servoing tests

Grahic Jump Location
Fig. 9

Visual servoing w/o and with VSDC in the loop

Grahic Jump Location
Fig. 4

Perspective projection of the camera

Grahic Jump Location
Fig. 5

Test setup for accuracy evaluation

Grahic Jump Location
Fig. 6

State estimation errors in accuracy evaluation tests



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