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Recognition of Technical Gestures for Human-Robot Collaboration in Factories

Authors:
Eva Coupeté
Fabien Moutarde
Sotiris Manitsaris
Olivier Hugues

Keywords: Human-robot collaboration; Industrial application; Assembly line; Gestures recognition; Depth camera.

Abstract:
Enabling smooth Human-Robot collaboration requires enhancing perception and intelligence of robots, so that they can “understand” the actions performed by the humans with whom they are interacting. In this paper we are dealing with new industrial collaborative robots on assembly-line and supplychain in automotive manufacturing. We are conducting research on technical gestures recognition, to allow the robot to understand which task is being executed by human worker, and react accordingly. We use two kinds of sensors: depth-camera for monitoring of human movements, and inertial sensors placed on tools. In this study, we propose and use a method for head and hands tracking using a top-view depth-map, and use HMM (Hidden Markov Models) to recognize gestures with these data. Then, we refine the results from the HMM with data from inertial sensors equipping tools. Our research shows that: i) using 3D-vision only, we can obtain already good results of gestures recognition for several workers: 80% of the gestures are correctly recognized, ii) exploiting data from tools equipped with inertial sensors significantly improve the recognition accuracy to 94% in the same multi-user evaluation. A first test of our method with a simple Human-Robot collaboration scenario is also described.

Pages: 280 to 285

Copyright: Copyright (c) IARIA, 2016

Publication date: April 24, 2016

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-468-8

Location: Venice, Italy

Dates: from April 24, 2016 to April 28, 2016