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SCIENCE CHINA Technological Sciences, Volume 60 , Issue 3 : 374-384(2017) https://doi.org/10.1007/s11431-016-0434-x

Human stochastic closed-loop behavior for master-slave teleoperation using multi-leap-motion sensor

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  • ReceivedJun 11, 2016
  • AcceptedOct 18, 2016
  • PublishedJan 17, 2017

Abstract

Teleoperation has a wide range of applications that have been under development over the past two decades. Previous researches have focused on the control design of teleoperation machine systems to deal with obstacles such as time-delayed stability and transparency. Recent researches have shown that the inclusion of human closed-loop dynamics in control design can improve the performance of robot telemanipulation. The complexity of human behavior arises from the uncertainty of both human physiology and psychology; hence, the investigation can benefit from empirical studies. This study develops a type of statistical learning method to model and evaluate human stochastic closed-loop behavior, which is considered as a hand motion during the direct incremental control process of master-slave teleoperation. The hand trajectory is empirically considered as having a binary linear regression relationship with the error and error rate between the demanded and simulated teleoperator trajectories, while random movements with zero error and error rate are discovered. Hand movement tracking is achieved using a multi-leap-motion sensor (MLM), which is a markerless and natural infrared vision-based manner for motion capture. The established behavior model and statistical learning results reveal certain human properties of operational activities including visual perception, decision making, and robot telemanipulation. The properties indicate some probable system enhancements for future work.


Funded by

National Natural Science Foundation of China(11402004)


Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant No. 11402004).


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  • Figure 1

    (Color online) Basic representation of a teleoperation system, which consists of a human operator, robotic motion visual feedback, human-teleoperator interface utilizing MLM at local operator site, and teleoperator at a remote robot site.

  • Figure 2

    (Color online) Multi-Leap-Motion sensor and right-handed coordinate definitions including measure coordinate Smi (i=0, 1, 2, 3, 4) and hand coordinate Sh.

  • Figure 3

    The process of the statistical learning of the behavior model.

  • Figure 4

    (Color online) Teleoperation system for the experiment. The haptic interface in the figure is replaced by the movable MLM sensor shown as Figure 2, and the right robot is chosen as the teleoperator.

  • Figure 5

    (Color online) Expected trajectory of remote robot’s end effector plotted as the solid line and simulated trajectory of end effector plotted as the dashed line.

  • Figure 6

    (Color online) Human hand movement consists of 15 continuous trajectories during the tracking task. The breakpoint exists due to the limited workspace of MLM sensor and relatively large range of robotic motion.

  • Figure 7

    (Color online) Samples and BLR model of behavior along (a) yW axis and (b) zW axis.

  • Figure 8

    (Color online) Relearning samples and BLR model of behavior along (a) yW axis and (b) zW axis.

  • Figure 9

    (Color online) Complementary relationship of error rate pair (a) (ey(t), ez(t)) and (b) (e˙y(t), e˙z(t)).

  • Figure 10

    (Color online) Scatters of hand trajectories along (a) yW axis and (b) zW axis during the practical operation and simulated operation.

  • Table 1   Statistical learning results of behavior along yW and zW axes

    Estimations

    Values

    Confidence interval

    Estimations

    Values

    Confidence interval

    Behavior along yW axis

    Behavior along zW axis

    β0y

    6.55

    [2.91, 10.19]

    β0z

    –3.74

    [–5.15, –2.33]

    β1y

    –0.54

    [–0.75, –0.32]

    β1z

    –0.17

    [–0.28, –0.059]

    β2y

    –0.26

    [–0.75, 0.23]

    β2z

    –0.17

    [–0.41, 0.080]

    R2=0.193, Fs=12.20, p<2×10–5, RSE2=252.18

    R2=0.087, Fs=4.86, p<0.01, RSE2=42.00

  • Table 2   Relearning results of behavior along yW and zW axes

    Estimations

    Values

    Confidence interval

    Estimations

    Values

    Confidence interval

    Behavior along yW axis

    Behavior along zW axis

    β0y

    17.89

    [12.11, 23.68]

    β0z

    –3.93

    [–6.42, –1.44]

    β1y

    –0.89

    [–1.15, –0.63]

    β1z

    –0.18

    [–0.31, –0.045]

    β2y

    –0.40

    [–0.89, 0.087]

    β2z

    –0.17

    [–0.41, 0.075]

    R2=0.472, F=24.56, p<2×10–8, RSE2=236.29

    R2=0.148, F=3.82, p<0.03, RSE2=37.79

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