Learning-based Action Planning for Real-time Robot Telecontrol with Binocular Vision in Enhanced Reality Environment
Action planning is one of the pivot issues in robot telecontrol, in which the action instructions are often given by the controller from remote site with the help of vision systems. In this paper, we present a learning-based strategy for action planning in robot telecontrol, in which the parameters of sophisticated actions of the remote robot equipped with a binocular vision system could be pre-scheduled with a virtual robot at the control terminal. The remote robot will then be taught with the scheduled action plan with a series of parameter sets obtained form try-outs with the virtual robot and object in the enhanced environment, thus implementing dedicated actions assigned correctly. The action planning process is implemented within a enhanced reality environment, in which both the virtual and the real robot will be displayed simultaneously for the purpose of being deeply immersed. Experiment results demonstrate that the proposed method is capable of promoting the action precision of the remote robot, and effective and valid to designated applications, where action precision plays a critical role.
Chensheng Wang Liang Chen Cong Zhang Wangpeng Zhang
School of Automation,Beijing University of Posts and Telecommunications,No.10 Xi Tu Cheng Lu,Haidian District,Beijing 100876,China
国际会议
2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)
桂林
英文
2041-2046
2009-12-19（万方平台首次上网日期，不代表论文的发表时间）