Study on a real-time visual measuring and tracking system for industry robot welding

2006 ◽  
Author(s):  
Chang-jie Liu ◽  
Ji-gui Zhu ◽  
Yan-bin Li ◽  
Sheng-hua Ye
Keyword(s):  
Real Time ◽  
Tracking System ◽  
Robot Welding

Development of a laser-based seam tracking system for real-time industrial robot welding applications

1996 ◽  
Author(s):  
George Agapiou ◽  
A. Economou ◽  
C. Kassiouras ◽  
S. Kontoyannis ◽  
Alexandros Papayannis ◽  
...  
Keyword(s):  
Real Time ◽  
Industrial Robot ◽  
Tracking System ◽  
Seam Tracking ◽  
Robot Welding

A Dynamic HMM-based Real-time Location Tracking System Utilizing UHF Passive RFID

2021 ◽  
pp. 1-1
Author(s):  
Bijoy Dripta Barua Chowdhury ◽  
Sara Masoud ◽  
Young-Jun Son ◽  
Chieri Kubota ◽  
Russell Tronstad
Keyword(s):  
Real Time ◽  
Tracking System ◽  
Location Tracking ◽  
Passive Rfid

A ROBUST AND FRIENDLY HUMAN–ROBOT INTERFACE SYSTEM BASED ON NATURAL HUMAN GESTURES

2010 ◽  
Vol 24 (06) ◽  
pp. 847-866 ◽  
Author(s):  
MING-SHAUNG CHANG ◽  
JUNG-HUA CHOU
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Fuzzy Logic Controller ◽  
Tracking System ◽  
Back Propagation ◽  
Principal Component ◽  
Working Environment ◽  
Expression Recognition ◽  
Linear Discriminant ◽  
Human Gestures

In this paper, we design a robust and friendly human–robot interface (HRI) system for our intelligent mobile robot based only on natural human gestures. It consists of a triple-face detection method and a fuzzy logic controller (FLC)-Kalman filter tracking system to check the users and predict their current position in a dynamic and cluttered working environment. In addition, through the combined classifier of the principal component analysis (PCA) and back-propagation artificial neural network (BPANN), single and successive commands defined by facial positions and hand gestures are identified for real-time command recognition after dynamic programming (DP). Therefore, the users can instruct this HRI system to make member recognition or expression recognition corresponding to their gesture commands, respectively based on the linear discriminant analysis (LDA) and BPANN. The experimental results prove that the proposed HRI system perform accurately in real-time face detection and tracking, and robustly react to the corresponding gesture commands at eight frames per second (fps).

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