Collision Preventing Phase-Progress Control for Velocity Adaptation in Human-Robot Collaboration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336417" target="_blank" >RIV/68407700:21230/19:00336417 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/Humanoids43949.2019.9035065" target="_blank" >https://doi.org/10.1109/Humanoids43949.2019.9035065</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/Humanoids43949.2019.9035065" target="_blank" >10.1109/Humanoids43949.2019.9035065</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Collision Preventing Phase-Progress Control for Velocity Adaptation in Human-Robot Collaboration

Popis výsledku v původním jazyce

As robots are leaving dedicated areas on the factory floor and start to share workspaces with humans, safety of such collaboration becomes a major challenge. In this work, we propose new approaches to robot velocity modulation: while the robot is on a path prescribed by the task, it predicts possible collisions with the human and gradually slows down, proportionally to the danger of collision. Two principal approaches are developed—Impulse Orb and Prognosis Window—that dynamically determine the possible robot-induced collisions and apply a novel velocity modulating approach, in which the phase progress of the robot trajectory is modulated while the desired robot path remains intact. The methods guarantee that the robot will halt before contacting the human, but they are less conservative and more flexible than solutions using reduced speed and complete stop only, thereby increasing the effectiveness of human-robot collaboration. This approach is especially useful in constrained setups where the robot path is prescribed. Speed modulation is smooth and does not lead to abrupt motions, making the behavior of the robot also better understandable for the human counterpart. The two principal methods under different parameter settings are experimentally validated in a human-robot interaction scenario with the Franka Emika Panda robot, an external RGB-D camera, and human keypoint detection using OpenPose.

Název v anglickém jazyce

Collision Preventing Phase-Progress Control for Velocity Adaptation in Human-Robot Collaboration

Popis výsledku anglicky

As robots are leaving dedicated areas on the factory floor and start to share workspaces with humans, safety of such collaboration becomes a major challenge. In this work, we propose new approaches to robot velocity modulation: while the robot is on a path prescribed by the task, it predicts possible collisions with the human and gradually slows down, proportionally to the danger of collision. Two principal approaches are developed—Impulse Orb and Prognosis Window—that dynamically determine the possible robot-induced collisions and apply a novel velocity modulating approach, in which the phase progress of the robot trajectory is modulated while the desired robot path remains intact. The methods guarantee that the robot will halt before contacting the human, but they are less conservative and more flexible than solutions using reduced speed and complete stop only, thereby increasing the effectiveness of human-robot collaboration. This approach is especially useful in constrained setups where the robot path is prescribed. Speed modulation is smooth and does not lead to abrupt motions, making the behavior of the robot also better understandable for the human counterpart. The two principal methods under different parameter settings are experimentally validated in a human-robot interaction scenario with the Franka Emika Panda robot, an external RGB-D camera, and human keypoint detection using OpenPose.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)

ISBN

978-1-5386-7629-5

ISSN

2164-0572

e-ISSN

2164-0580

Počet stran výsledku

8

Strana od-do

266-273

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Toronto

Datum konání akce

15. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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