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https://doi.org/10.1109/lra.20...
Article . 2024 . Peer-reviewed
License: CC BY
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https://dx.doi.org/10.48550/ar...
Article . 2024
License: CC BY
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A Minimum-Jerk Approach to Handle Singularities in Virtual Fixtures

Authors: Giovanni Braglia; Sylvain Calinon; Luigi Biagiotti;

A Minimum-Jerk Approach to Handle Singularities in Virtual Fixtures

Abstract

Implementing virtual fixtures in guiding tasks constrains the movement of the robot's end effector to specific curves within its workspace. However, incorporating guiding frameworks may encounter discontinuities when optimizing the reference target position to the nearest point relative to the current robot position. This article aims to give a geometric interpretation of such discontinuities, with specific reference to the commonly adopted Gauss-Newton algorithm. The effect of such discontinuities, defined as Euclidean Distance Singularities, is experimentally proved. We then propose a solution that is based on a Linear Quadratic Tracking problem with minimum jerk command, then compare and validate the performances of the proposed framework in two different human-robot interaction scenarios.

8 pages, 6 figures

Keywords

FOS: Computer and information sciences, Computer Science - Robotics, Human-Robot Collaboration; Motion and Path Planning; Optimization and Optimal Control; Physical Human-Robot Interaction;, Robotics (cs.RO)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
hybrid