Real-time and Robust Collaborative Robot Motion Control with Microsoft Kinect® v2

被引：0

作者：

Teke, Burak ^{[1
]}

Lanz, Minna ^{[1
]}

Kamarainen, Joni-Kristian ^{[2
]}

Hietanen, Antti ^{[2
]}

机构：

[1] Tampere Univ Technol, Dept Prod Engn, Tampere, Finland

[2] Tampere Univ Technol, Dept Signal Proc, Tampere, Finland

来源：

2018 14TH IEEE/ASME INTERNATIONAL CONFERENCE ON MECHATRONIC AND EMBEDDED SYSTEMS AND APPLICATIONS (MESA) | 2018年

关键词：

Human-robot interaction; human-robot collaboration; collaborative robots; trajectory planning; Microsoft Kinect v2; ROS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recent development in depth sensing provide various opportunities for the development of new methods for Human Robot Interaction (HRI). Collaborative robots (co-bots) are redefining HRI across the manufacturing industry. However, little work has been done yet in the field of HRI with Kinect sensor in this industry. In this paper, we will present a HRI study using nearest-point approach with Microsoft Kinect v2 sensor's depth image (RGB-D). The approach is based on the Euclidean distance which has robust properties against different environments. The study aims to improve the motion performance of Universal Robot-5 (UR5) and interaction efficiency during the possible collaboration using the Robot Operating System (ROS) framework and its tools. After the depth data from the Kinect sensor has been processed, the nearest points differences are transmitted to the robot via ROS.

引用

页数：6

共 50 条

[21] Respiratory Motion Tracking Using Kinect V2
Silverstein, E.
Snyder, M.
MEDICAL PHYSICS, 2016, 43 (06) : 3651 - 3651
[22] Capturing Geometry in Real-Time Using a Tracked Microsoft Kinect
Tenedorio, Daniel
Fecho, Marlena
Schwartzhaupt, Jorge
Pardridge, Robert
Lue, James
Schulze, Juergen P.
ENGINEERING REALITY OF VIRTUAL REALITY 2012, 2012, 8289
[23] Investigating the impact of a motion capture system on Microsoft Kinect v2 recordings: A caution for using the technologies together
Naeemabadi, MReza
Dinesen, Birthe
Andersen, Ole Kaeseler
Hansen, John
PLOS ONE, 2018, 13 (09):
[24] Influence of a Marker-Based Motion Capture System on the Performance of Microsoft Kinect v2 Skeleton Algorithm
Naeemabadi, MReza
Dinesen, Birthe
Andersen, Ole Kaeseler
Hansen, John
IEEE SENSORS JOURNAL, 2019, 19 (01) : 171 - 179
[25] Bed angle detection in hospital room using Microsoft Kinect V2
Liu, Liang
Mehrotra, Sanjay
2016 IEEE 13TH INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN), 2016, : 277 - 280
[26] Comparison between the Kinect™ V1 and Kinect™ V2 for Respiratory Motion Tracking
Samir, Mohammed
Golkar, Ehsan
Rahni, Ashrani Aizzuddin Abd.
2015 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING APPLICATIONS (ICSIPA), 2015, : 150 - 155
[27] A prototype for the automatic measurement of the hand dimensions using the Microsoft Kinect V2
Tarabini, Marco
Marchisotti, Daniele
Sala, Remo
Marzaroli, Pietro
Giberti, Hermes
Sculati, Michele
2018 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA), 2018, : 140 - 145
[28] Patient walk detection in hospital room using Microsoft Kinect V2
Liu, Liang
Mehrotra, Sanjay
2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2016, : 4395 - 4398
[29] Automated classification of movement quality using the Microsoft Kinect V2 sensor
Dajime, Peter Fermin
Smith, Heather
Zhang, Yanxin
COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 125
[30] Implementation of facial recognition with Microsoft Kinect v2 sensor for patient verification
Silverstein, Evan
Snyder, Michael
MEDICAL PHYSICS, 2017, 44 (06) : 2391 - 2399

← 1 2 3 4 5 →