Research on novel control techniques for robot teleoperation in unstructured environments

A novel control method, called Interactive Partial Modeling-based Teleoperation Control (IPMTC) was developed to realize the intelligent control of the telerobot system in unstructured environment, combined with the intelligent digital hybrid controller that have been developed. This method can estimate task status and environment perception information, and send requests for intervention of human operation (HO) real time during autonomy. In order to guarantee real time transition of HO intervention and autonomy, the intelligent control module was developed based on the results of task estimation, which can realize the functions of task plan, subtask autonomous execution, and automated reasoning about the task. A new remote predictive control method and the single/double delay time compensation strategy are introduced to improve the real time performance of the system. The performances of the teleoperation system in unstructured environments are guaranteed with above control techniques.

[1]  Jonathan Kofman,et al.  Teleoperation of a robot manipulator using a vision-based human-robot interface , 2005, IEEE Transactions on Industrial Electronics.

[2]  Manuel Ferre,et al.  Stereoscopic video images for telerobotic applications , 2005 .

[3]  Zhao Chunying.  Research on Competitive Teleoperating Robot System , 2005 .

[4]  R. P. Paul,et al.  Model-based, delay-tolerant teleoperation in unstructured environments , 1991, [1991 Proceedings] 6th Mediterranean Electrotechnical Conference.

[5]  Liu Zhiyuan.  General Quasi-infinite Horizon Nonlinear Model Predictive Control , 2002 .

[6]  Ning Xi,et al.  A case study of 3D stereoscopic vs. 2D monoscopic tele-reality in real-time dexterous teleoperation , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Robin R. Murphy,et al.  Introduction to AI Robotics , 2000 .

[8]  Mohammad Haeri,et al.  Position control of an ultrasonic motor using generalized predictive control , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[9]  Wen-Hua Chen,et al.  Predictive control of general nonlinear systems using approximation , 2004 .

[10]  Debanik Roy.  Development and Model-Aided Performance Measure of a Sensor-Augmented Industrial Robotic System for Handling Steel Bearing Races in an Unstructured Environment , 2005, J. Intell. Robotic Syst..

[11]  Steven Dubowsky,et al.  Visually built task models for robot teams in unstructured environments , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[12]  Zhao Jie,et al.  Virtual environment based on Internet and Java3D for multi-robot teleoperation , 2005 .