On Inferring Intentions in Shared Tasks for Industrial Collaborative Robots

被引：14

作者：

Olivares-Alarcos, Alberto ^{[1
]}

Foix, Sergi ^{[1
]}

Alenya, Guillem ^{[1
]}

机构：

[1] CSIC UPC, Inst Robat & Informat Ind, Llorens I Artigas 4-6, Barcelona 08028, Spain

来源：

ELECTRONICS | 2019年 / 8卷 / 11期

关键词：

industrial collaborative robots; shared robotic tasks; physical human-robot interaction; human intention recognition; time series classification; INTELLIGENT; DESIGN; MODEL;

D O I：

10.3390/electronics8111306

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Inferring human operators' actions in shared collaborative tasks plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space, but also forces and the execution of a task. In this article, we present a robotic system that is able to identify different human's intentions and to adapt its behavior consequently, only employing force data. In order to accomplish this aim, three major contributions are presented: (a) a force based operator's intention recognition system based on data from only two users; (b) a force based dataset of physical human-robot interaction; and (c) validation of the whole system with 15 people in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human-robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.

引用

页数：22

共 39 条

[1]

Bagnall Anthony, 2016, The great time series classification bake off: An experimental evaluation of recently proposed algorithms

[2]

Bastianelli E, 2014, LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, P4519

[3]

Bauer W., 2016, Lightweight Robots in Manual AssemblyBest to Start Simply! Examining Companies Initial Experiences with Lightweight Robots

[4]

Berndt D.J., 1994, Advances in Knowledge Discovery and Data Mining, P359

[5] Multimodal Human-Human-Robot Interactions (MHHRI) Dataset for Studying Personality and Engagement [J].

Celiktutan, Oya ;

Skordos, Efstratios ;

Gunes, Hatice .

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2019, 10 (04) :484-497

[6] Collaborative manufacturing with physical human-robot interaction [J].

Cherubini, Andrea ;

Passama, Robin ;

Crosnier, Andre ;

Lasnier, Antoine ;

Fraisse, Philippe .

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2016, 40 :1-13

[7]

De Magistris G., 2018, ARXIV PREPRINT ARXIV

[8] iMRK: Demonstrator for Intelligent and Intuitive Human–Robot Collaboration in Industrial Manufacturing [J].

de Gea Fernández J. ;

Mronga D. ;

Günther M. ;

Wirkus M. ;

Schröer M. ;

Stiene S. ;

Kirchner E. ;

Bargsten V. ;

Bänziger T. ;

Teiwes J. ;

Krüger T. ;

Kirchner F. .

KI - Künstliche Intelligenz, 2017, 31 (2) :203-207

[9] A model-based residual approach for human-robot collaboration during manual polishing operations [J].

Gaz, Claudio ;

Magrini, Emanuele ;

De Luca, Alessandro .

MECHATRONICS, 2018, 55 :234-247

[10] A dataset of daily interactive manipulation [J].

Huang, Yongqiang ;

Sun, Yu .

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2019, 38 (08) :879-886

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