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.

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Fig. 3

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

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Fig. 2

Visual sensing dynamics

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Fig. 1

Motivation of real-time vision guidance

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Fig. 4

Perspective projection of the camera

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Fig. 6

State estimation errors in accuracy evaluation tests

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Fig. 5

Test setup for accuracy evaluation

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Fig. 8

Setup of visual servoing tests

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Fig. 9

Visual servoing w/o and with VSDC in the loop

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Fig. 7

Bandwidth evaluation




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