Research on a detection and recognition method of tactile-slip sensation used to control the Elderly-assistant & Walking-assistant Robot

In this paper, according to the old people's physical characteristics and their technical requirements for comfort and mastery when operating the robot, a detection and recognition method of tactile and slip senses is proposed that used to control the Elderly-assistant & Walking-assistant Robot. First, on the basis of the proposed drive control system program of tactile and slip, detection system of tactile and slip senses is designed. And then, based on the tactile and slip feature representation and extraction, an improved classification and recognition method is proposed that K-nearest neighbor (KNN) algorithm are combined with K-means algorithm. In the end, through many online and offline experimental analysis, the results show that the tactile and slip senses detection and recognition method is effective, and it can realize the robot's driving control.

[1]  C. A. Murthy,et al.  On visualization and aggregation of nearest neighbor classifiers , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Wlodzislaw Duch,et al.  THE WEIGHTED k-NN WITH SELECTION OF FEATURES AND ITS NEURAL REALIZATION , 1999 .

[3]  Aiguo Song,et al.  Real time stiffness display interface device for perception of virtual soft object , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Gerard Lacey,et al.  A smart walker for the frail visually impaired , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[5]  E. S. Kolesar,et al.  Object imaging with a piezoelectric robotic tactile sensor , 1995 .

[6]  Eung-Hyuk Lee,et al.  Implementation of an intelligent walking assistant robot for the elderly in outdoor environment , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[7]  Qixin Cao,et al.  Based on force sensing-controlled human-machine interaction system for walking assistant robot , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[8]  Joelle Pineau,et al.  Towards robotic assistants in nursing homes: Challenges and results , 2003, Robotics Auton. Syst..

[9]  Songmin Jia,et al.  Rehabilitation Walker System with Standing Assistance Device , 2007, 2007 International Conference on Mechatronics and Automation.

[10]  E. Kolesar,et al.  Object imaging with a piezoelectric robotic tactile sensor , 1993, Proceedings of the IEEE 1993 National Aerospace and Electronics Conference-NAECON 1993.

[11]  Mark R. Cutkosky,et al.  Dynamic tactile sensing: perception of fine surface features with stress rate sensing , 1993, IEEE Trans. Robotics Autom..

[12]  Mark R. Cutkosky,et al.  Tactile sensor with 3-axis force and vibration sensing functions and its application to detect rotational slip , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[13]  Xiaodong Zhang,et al.  Detecting System Design of Tactile Sensor for the Elderly-Assistant & Walking-Assistant Robot , 2010 .

[14]  B. Graf,et al.  Reactive navigation of an intelligent robotic walking aid , 2001, Proceedings 10th IEEE International Workshop on Robot and Human Interactive Communication. ROMAN 2001 (Cat. No.01TH8591).

[15]  T. Michael Knasel,et al.  Robotics and autonomous systems , 1988, Robotics Auton. Syst..

[16]  S. Dubowsky,et al.  Robotic Personal Aids for Mobility and Monitoring for the Elderly , 2006, IEEE transactions on neural systems and rehabilitation engineering.

[17]  Javad Dargahi,et al.  A piezoelectric tactile sensor with three sensing elements for robotic, endoscopic and prosthetic applications , 2000 .

[18]  Kyoungchul Kong,et al.  Design and control of an exoskeleton for the elderly and patients , 2006, IEEE/ASME Transactions on Mechatronics.

[19]  Les A. Piegl,et al.  Algorithm for finding all k nearest neighbors , 2002, Comput. Aided Des..

[20]  George Goodsell.  On finding p-th nearest neighbours of scattered points in two dimensions for small p , 2000, Comput. Aided Geom. Des..

[21]  M. Narasimha Murty,et al.  Genetic K-means algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[22]  Dimitrios Gunopulos,et al.  Locally Adaptive Metric Nearest-Neighbor Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Zaher Al Aghbari,et al.  Fast k-NN Image Search with Self-Organizing Maps , 2002, CIVR.

[24]  Kazuhiro Kosuge,et al.  A Control Approach Based on Passive Behavior to Enhance User Interaction , 2007, IEEE Transactions on Robotics.