2020
DOI: 10.3390/robotics9020024
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Experimental Testing of Bandstop Wave Filter to Mitigate Wave Reflections in Bilateral Teleoperation

Abstract: A bilateral teleoperation system can become unstable in the presence of a modest time delay. However, the wave variable algorithm provides stable operation for any fixed time delay using passivity arguments. Unfortunately, the wave variable method produces wave reflection that can degrade teleoperation performance when a mismatched impedance exists between the master and slave robot. In this work, we develop a novel bandstop wave filter and experimentally verify that the technique can mitigate the effects of w… Show more

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Cited by 2 publications
(4 citation statements)
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“…It is impossible to change orientation when stationary, no matter how hard one steers. However, the variable time base also has some disadvantages for the controller [49][50][51][52][53].…”
Section: Orientation Controller Designmentioning
confidence: 99%
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“…It is impossible to change orientation when stationary, no matter how hard one steers. However, the variable time base also has some disadvantages for the controller [49][50][51][52][53].…”
Section: Orientation Controller Designmentioning
confidence: 99%
“…It is, therefore, 10 ms here. To determine the desired modified cycle time dt, this time base is multiplied by the ratio between the instantaneous drive speed 𝑦 đťś”(𝑡) and the maximum drive speed đťś” max [45,[50][51][52][53][54][55]. This is because the controller can be set for a constant driving speed of the robot, namely đťś” max .…”
Section: The Single-track Modelmentioning
confidence: 99%
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“…e) Intelligent state prediction model: One significant issue for bilateral teleoperation systems is synchronization or tracking performance error while considering variable time delay. Potential solutions need further evaluation and development, such as state synchronization schemes [244], wave filter models [245], and AIenabled state predictive models [246]. environments due to shortcomings in exiting online parameter estimation models.…”
Section: Summary and Insightsmentioning
confidence: 99%