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Research Papers

A Control-Theoretic Model for Human Time-Motion Evaluation in Pick-and-Place Operations

[+] Author and Article Information
Chao Wang

Department of Mechanical and
Aerospace Engineering,
University of California—Davis,
Davis, CA 95616
e-mail: cwwang@ucdavis.edu

Bahram Ravani

Professor
Fellow ASME
Department of Mechanical and
Aerospace Engineering,
University of California—Davis,
Davis, CA 95616
e-mail: bravani@ucdavis.edu

Ronald A. Hess

Professor
Department of Mechanical and
Aerospace Engineering,
University of California—Davis,
Davis, CA 95616
e-mail: rahess@ucdavis.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 19, 2016; final manuscript received October 21, 2016; published online February 9, 2017. Assoc. Editor: Tesheng Hsiao.

J. Dyn. Sys., Meas., Control 139(4), 041009 (Feb 09, 2017) (13 pages) Paper No: DS-16-1111; doi: 10.1115/1.4035095 History: Received February 19, 2016; Revised October 21, 2016

This paper deals with physical modeling of human hand–eye coordinated movement for applications in time-motion study of pick-and-place operations. Time-motion studies typically use experimentations to closely examine each segment of a worker's pick-and-place movements in order to design a more optimized operation. This paper presents two different methods that can replace the need for experimentation or estimation in the time motion process with control-theoretic models. The first method is a control-theoretic physical model of the human hand–eye coordinated movement in performing a pick-and-place operation. It is based on an extension of control theoretic models of airplane pilots. The second method combines two existing techniques developed in the literature for different purposes. It is shown in this paper that the combination of these two existing methods provides for an alternative approach that can be used for time-motion studies related to the human pick-and-place operation. Using simple experimentation, it is shown that both methods provide reasonable model-based representation of time motion studies for pick-and-place tasks. In developing the physical model, a method based on the use of the quantitative feedback theory (QFT) is also developed for tuning the physical model that can be utilized in making the model specific to different applications involving human hand–eye coordinated movements. Furthermore, the physical model is applied in a predictive fashion and it is shown that it can successfully estimate the movement time for manual pick-and-place tasks found in some industrial applications.

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References

Figures

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Fig. 1

A block diagram representation of the structural model for a human pilot (Adapted from Hess [14]. Copyright 1985 by JAI Press)

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Fig. 2

Block diagram of the modified crossover model

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Fig. 3

The physical model is simplified structural model with a single inner loop. Transfer functions of blocks are given in Table 2.

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Fig. 4

Nichol's chart plot of the plant trajectory and loop shaping boundaries before (left) and after (right) implementing the controller C1 in Fig. 3

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Fig. 5

Bode plot of the closed-loop system before (left) and after (right) implementing the prefilter F in Fig. 3

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Fig. 6

Beginning (left) and end (right) of one movement in the planar movement experiment as captured by the camera. The initial position of the part and the target are marked by the circle and the rectangle, respectively.

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Fig. 7

A frame from captured movement video after processing. The fitted circle is drawn on the top of the hand-held part and its center is the estimated position of the moving part. The initial position (circle on right) and the target area (rectangle on left) are also shown.

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Fig. 8

Hand movement testing apparatus

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Fig. 9

Image showing the transmitter module and the switch fitted to the part

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Fig. 10

A subject performing the simulated pick-and-place task with the transmitter module attached to the wrist

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Fig. 11

Comparing model predicted movement time against planar movement experimental data and MTM-1 data card values

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Fig. 12

Comparing model predicted movement time against 3D pick-and-place experiment data and MTM-1 data card values

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Fig. 13

Identifying Fitts' law parameters from experimental data

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Fig. 14

Plot of experimentally gathered movement trajectory against trajectory generated using the physical model and Flash–Hogan–Fitts model for movement configuration 1 in. the planar movement experiment. The RMS error of trajectories generated using each method is also noted in this figure.

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Fig. 15

Comparing RMS error of trajectories generated using Flash–Hogan–Fitts model and physical model

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Fig. 16

Comparing predicted movement time using physical model and Fitts' law against data collected from the planar movement experiment

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Fig. 17

Comparing predicted movement time using physical model and Fitts' law against data collected from the 3D pick-and-place experiment

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Fig. 18

A schematic of the pin to bushing indicating target distance and size of the opening on the bushing. (Adapted from Fig. 6 in Barnes and Mundel [37]. Copyright 1938 by State University of Iowa).

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Fig. 19

Dimensions of the bushing used in the experiments. The hole diameter (D) was 0.250 in. for group A and 0.258 in. for group B. (Adapted from Fig. 4 in Barnes and Mundel [37]. Copyright 1938 by State University of Iowa).

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Fig. 20

Comparison between movement time generated using Fitts' law, the physical model, and MTM-1 table against experimental data gathered reported by Barnes and Mundel in Ref. [37]

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Fig. 21

Comparison between movement time generated using Fitts' law, the physical model, and MTM-1 table for individual movement segments in the bolt and washer assembly task

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Fig. 22

Comparison between total movement times generated using Fitts' law, the physical model, and MTM-1 table for the bolt and washer assembly task

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Fig. 23

A typical loop structure of a system in QFT design (external disturbance is ignored for simplicity)

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